Frontend build copied to static, add 5 test images, update API integration; ignore static backup folders

api.py

@@ -3,7 +3,7 @@
 import os
 os.environ['MPLCONFIGDIR'] = '/tmp/matplotlib'
 
-from flask import Flask, request, jsonify, send_from_directory, redirect, url_for, session, render_template_string, make_response
+from flask import Flask, request, jsonify, send_from_directory, redirect, url_for, session, render_template_string, make_response, Response, stream_with_context
 from datetime import timedelta
 import torch
 from PIL import Image
@@ -19,6 +19,8 @@ from flask_cors import CORS
 import json
 import sys
 import requests
+import asyncio
+from threading import Thread
 try:
     from openai import OpenAI
 except Exception as _e:
@@ -54,6 +56,14 @@ import chromadb
 from chromadb.utils import embedding_functions
 
 app = Flask(__name__, static_folder='static')
+
+# Import product comparison coordinator
+try:
+    from product_comparison import get_product_comparison_coordinator, decode_base64_image
+except ImportError:
+    print("Warning: Product comparison module not available")
+    get_product_comparison_coordinator = None
+    decode_base64_image = None
 # 환경 변수에서 비밀 키를 가져오거나, 없으면 안전한 랜덤 키 생성
 secret_key = os.environ.get('FLASK_SECRET_KEY')
 if not secret_key:
@@ -895,6 +905,168 @@ def add_detected_objects():
                 print(f"Error processing object: {e}")
                 continue
         
+        # Add to vector DB
+        if object_ids and object_embeddings and object_metadatas:
+            object_collection.add(
+                ids=object_ids,
+                embeddings=object_embeddings,
+                metadatas=object_metadatas
+            )
+            
+            return jsonify({
+                "success": True,
+                "message": f"Added {len(object_ids)} objects to collection",
+                "object_ids": object_ids
+            })
+        else:
+            return jsonify({
+                "warning": "No valid objects to add"
+            })
+            
+    except Exception as e:
+        print(f"Error in add-detected-objects API: {e}")
+        return jsonify({"error": str(e)}), 500
+
+
+# Product Comparison API Endpoints
+@app.route('/api/product/compare/start', methods=['POST'])
+@login_required
+def start_product_comparison():
+    """Start a new product comparison session"""
+    if get_product_comparison_coordinator is None:
+        return jsonify({"error": "Product comparison module not available"}), 500
+    
+    try:
+        # Generate session ID if provided in form or query params, otherwise create new one
+        session_id = request.form.get('session_id') or request.args.get('session_id') or str(uuid.uuid4())
+        
+        # Get analysis type if provided (info, compare, value, recommend)
+        analysis_type = request.form.get('analysisType') or request.args.get('analysisType', 'info')
+        
+        # Process images from FormData or JSON
+        images = []
+        
+        # Check if request is multipart form data
+        if request.files:
+            # Handle FormData with file uploads (from frontend)
+            if 'image1' in request.files and request.files['image1']:
+                img1 = request.files['image1']
+                try:
+                    images.append(Image.open(img1.stream))
+                except Exception as e:
+                    print(f"Error processing image1: {e}")
+                    
+            if 'image2' in request.files and request.files['image2']:
+                img2 = request.files['image2']
+                try:
+                    images.append(Image.open(img2.stream))
+                except Exception as e:
+                    print(f"Error processing image2: {e}")
+                    
+        # Fallback to JSON with base64 images (for API testing)
+        elif request.json and 'images' in request.json:
+            image_data_list = request.json.get('images', [])
+            for image_data in image_data_list:
+                img = decode_base64_image(image_data)
+                if img is not None:
+                    images.append(img)
+                    
+        if not images:
+            return jsonify({"error": "No valid images provided"}), 400
+        
+        # Get coordinator instance
+        coordinator = get_product_comparison_coordinator()
+        
+        # Pass the analysis type and session metadata to the coordinator
+        session_metadata = {
+            'analysis_type': analysis_type,
+            'timestamp': time.strftime('%Y-%m-%d %H:%M:%S')
+        }
+        
+        # Start processing in a background thread
+        def run_async_task(loop):
+            asyncio.set_event_loop(loop)
+            loop.run_until_complete(coordinator.process_images(session_id, images, session_metadata))
+        
+        loop = asyncio.new_event_loop()
+        thread = Thread(target=run_async_task, args=(loop,))
+        thread.daemon = True
+        thread.start()
+        
+        # Return session ID for client to use with streaming endpoint
+        return jsonify({
+            "session_id": session_id,
+            "message": "Product comparison started",
+            "status": "processing"
+        })
+        
+    except Exception as e:
+        print(f"Error starting product comparison: {e}")
+        return jsonify({"error": str(e)}), 500
+
+
+@app.route('/api/product/compare/stream/<session_id>', methods=['GET'])
+@login_required
+def stream_product_comparison(session_id):
+    """Stream updates from a product comparison session"""
+    if get_product_comparison_coordinator is None:
+        return jsonify({"error": "Product comparison module not available"}), 500
+    
+    def generate():
+        """Generate SSE events for streaming"""
+        coordinator = get_product_comparison_coordinator()
+        last_message_index = 0
+        retry_count = 0
+        max_retries = 300  # 5 minutes at 1 second intervals
+        
+        while retry_count < max_retries:
+            # Get current status
+            status = coordinator.get_session_status(session_id)
+            if status is None:
+                # Session not found
+                yield f"data: {json.dumps({'error': 'Session not found'})}\n\n"
+                break
+            
+            # Get all messages
+            messages = coordinator.get_session_messages(session_id)
+            
+            # Send any new messages
+            if messages and len(messages) > last_message_index:
+                new_messages = messages[last_message_index:]
+                for msg in new_messages:
+                    yield f"data: {json.dumps({'message': msg})}\n\n"
+                last_message_index = len(messages)
+            
+            # Send current status
+            yield f"data: {json.dumps({'status': status})}\n\n"
+            
+            # If completed or error, send final result and end stream
+            if status in ['completed', 'error']:
+                result = coordinator.get_session_result(session_id)
+                yield f"data: {json.dumps({'final_result': result})}\n\n"
+                break
+            
+            # Wait before next update
+            time.sleep(1)
+            retry_count += 1
+        
+        # End the stream if we've reached max retries
+        if retry_count >= max_retries:
+            yield f"data: {json.dumps({'error': 'Timeout waiting for results'})}\n\n"
+    
+    return Response(
+        stream_with_context(generate()),
+        mimetype="text/event-stream",
+        headers={
+            'Cache-Control': 'no-cache',
+            'X-Accel-Buffering': 'no',
+            'Content-Type': 'text/event-stream',
+        }
+    )
+            except Exception as e:
+                print(f"Error processing object: {e}")
+                continue
+        
         # 객체가 없는 경우
         if not object_ids:
             return jsonify({"error": "No valid objects to add"})

frontend/src/App.js

@@ -9,13 +9,16 @@ import {
   AppBar,
   Toolbar,
   ThemeProvider,
-  createMuiTheme
+  createMuiTheme,
+  Tabs,
+  Tab
 } from '@material-ui/core';
 import ImageUploader from './components/ImageUploader';
 import ModelSelector from './components/ModelSelector';
 import ResultDisplay from './components/ResultDisplay';
 import LlmAnalysis from './components/LlmAnalysis';
 import OpenAIChat from './components/OpenAIChat';
+import ProductComparison from './components/ProductComparison';
 import './App.css';
 
 // Create a theme
@@ -45,6 +48,7 @@ function App() {
     detr: false,
     vit: false
   });
+  const [activeTab, setActiveTab] = useState(0);
 
   // Check API status on component mount
   useEffect(() => {
@@ -140,12 +144,23 @@ function App() {
         <AppBar position="static">
           <Toolbar>
             <Typography variant="h6" style={{ flexGrow: 1 }}>
-              Multi-Model Object Detection Demo
+              Vision LLM Multi-Agent System
             </Typography>
           </Toolbar>
+          <Tabs 
+            value={activeTab} 
+            onChange={(e, newValue) => setActiveTab(newValue)} 
+            indicatorColor="secondary"
+            textColor="inherit"
+            centered
+          >
+            <Tab label="Object Detection" />
+            <Tab label="Product Comparison" />
+          </Tabs>
         </AppBar>
         <Container maxWidth="lg" style={{ marginTop: theme.spacing(4), marginBottom: theme.spacing(4) }}>
-          <Grid container spacing={3}>
+          {activeTab === 0 ? (
+            <Grid container spacing={3}>
             <Grid item xs={12}>
               <Paper style={{ padding: theme.spacing(2) }}>
                 <Typography variant="h5" gutterBottom>
@@ -212,6 +227,9 @@ function App() {
               <OpenAIChat imageBase64={imageBase64} />
             </Grid>
           </Grid>
+          ) : (
+            <ProductComparison />
+          )}
         </Container>
       </Box>
     </ThemeProvider>

frontend/src/components/ProductComparison.js

@@ -0,0 +1,647 @@
+import React, { useState, useEffect, useRef } from 'react';
+import { 
+  Box, 
+  Paper, 
+  Grid, 
+  Typography, 
+  Button,
+  CircularProgress,
+  Tabs,
+  Tab,
+  Divider,
+  Card,
+  CardContent,
+  CardMedia,
+  IconButton,
+  TextField
+} from '@material-ui/core';
+import { AddCircle, Delete, Compare, Search, Info } from '@material-ui/icons';
+import { makeStyles } from '@material-ui/core/styles';
+
+const useStyles = makeStyles((theme) => ({
+  root: {
+    marginTop: theme.spacing(3),
+    marginBottom: theme.spacing(3),
+  },
+  imageContainer: {
+    position: 'relative',
+    minHeight: '200px',
+    display: 'flex',
+    justifyContent: 'center',
+    alignItems: 'center',
+    border: '2px dashed #ccc',
+    borderRadius: '8px',
+    margin: theme.spacing(1),
+    padding: theme.spacing(1),
+  },
+  progressLog: {
+    marginTop: theme.spacing(2),
+    height: '200px',
+    overflowY: 'auto',
+    backgroundColor: '#f8f9fa',
+    padding: theme.spacing(1),
+    borderRadius: '4px',
+    fontFamily: 'monospace',
+    fontSize: '0.9rem',
+  },
+  logEntry: {
+    margin: '4px 0',
+    padding: '2px 5px',
+    borderLeft: '3px solid #ccc',
+  },
+  logEntryAgent: {
+    borderLeft: '3px solid #2196f3',
+  },
+  logEntrySystem: {
+    borderLeft: '3px solid #4caf50',
+  },
+  logEntryError: {
+    borderLeft: '3px solid #f44336',
+  },
+  logTime: {
+    color: '#666',
+    fontSize: '0.8rem',
+    marginRight: theme.spacing(1),
+  },
+  imagePreview: {
+    maxWidth: '100%',
+    maxHeight: '300px',
+    objectFit: 'contain',
+  },
+  uploadIcon: {
+    fontSize: '3rem',
+    color: '#aaa',
+  },
+  uploadInput: {
+    display: 'none',
+  },
+  deleteButton: {
+    position: 'absolute',
+    top: '8px',
+    right: '8px',
+    backgroundColor: 'rgba(255, 255, 255, 0.8)',
+    '&:hover': {
+      backgroundColor: 'rgba(255, 255, 255, 0.9)',
+    },
+  },
+  tabPanel: {
+    padding: theme.spacing(2),
+  },
+  resultCard: {
+    marginTop: theme.spacing(2),
+    marginBottom: theme.spacing(2),
+  },
+  comparisonTable: {
+    width: '100%',
+    borderCollapse: 'collapse',
+    '& th, & td': {
+      border: '1px solid #ddd',
+      padding: '8px',
+      textAlign: 'left',
+    },
+    '& th': {
+      backgroundColor: '#f2f2f2',
+    },
+    '& tr:nth-child(even)': {
+      backgroundColor: '#f9f9f9',
+    },
+  },
+  loadingContainer: {
+    display: 'flex',
+    flexDirection: 'column',
+    alignItems: 'center',
+    padding: theme.spacing(4),
+  },
+  highlight: {
+    backgroundColor: '#e3f2fd',
+    padding: theme.spacing(1),
+    borderRadius: '4px',
+    fontWeight: 'bold',
+  }
+}));
+
+// 분석 유형 탭 패널 컴포넌트
+function TabPanel(props) {
+  const { children, value, index, ...other } = props;
+
+  return (
+    <div
+      role="tabpanel"
+      hidden={value !== index}
+      id={`analysis-tabpanel-${index}`}
+      aria-labelledby={`analysis-tab-${index}`}
+      {...other}
+    >
+      {value === index && (
+        <Box p={3}>
+          {children}
+        </Box>
+      )}
+    </div>
+  );
+}
+
+const ProductComparison = () => {
+  const classes = useStyles();
+  const [images, setImages] = useState([null, null]); // 최대 2개 이미지 저장
+  const [imagePreviews, setImagePreviews] = useState([null, null]);
+  const [isProcessing, setIsProcessing] = useState(false);
+  const [analysisResults, setAnalysisResults] = useState(null);
+  const [error, setError] = useState(null);
+  const [tabValue, setTabValue] = useState(0);
+  const [progressLogs, setProgressLogs] = useState([]);
+  const logEndRef = useRef(null);
+
+  // 탭 변경 핸들러
+  const handleTabChange = (event, newValue) => {
+    setTabValue(newValue);
+  };
+
+  // 이미지 업로드 핸들러
+  const handleImageUpload = (event, index) => {
+    const file = event.target.files[0];
+    
+    if (file) {
+      // 이미지 미리보기 생성
+      const reader = new FileReader();
+      reader.onload = (e) => {
+        const newPreviews = [...imagePreviews];
+        newPreviews[index] = e.target.result;
+        setImagePreviews(newPreviews);
+      };
+      reader.readAsDataURL(file);
+      
+      // 이미지 파일 상태 업데이트
+      const newImages = [...images];
+      newImages[index] = file;
+      setImages(newImages);
+      
+      // 결과 및 오류 초기화
+      setAnalysisResults(null);
+      setError(null);
+    }
+  };
+
+  // 이미지 삭제 핸들러
+  const handleImageDelete = (index) => {
+    const newImages = [...images];
+    const newPreviews = [...imagePreviews];
+    
+    newImages[index] = null;
+    newPreviews[index] = null;
+    
+    setImages(newImages);
+    setImagePreviews(newPreviews);
+    setAnalysisResults(null);
+  };
+
+  // 로그 추가 함수
+  const addLog = (message, type = 'info') => {
+    const now = new Date();
+    const timeStr = now.toLocaleTimeString();
+    const newLog = {
+      time: timeStr,
+      message,
+      type // 'info', 'agent', 'system', 'error'
+    };
+    
+    setProgressLogs(logs => [...logs, newLog]);
+  };
+
+  // 로그창 자동 스크롤
+  useEffect(() => {
+    if (logEndRef.current) {
+      logEndRef.current.scrollIntoView({ behavior: 'smooth' });
+    }
+  }, [progressLogs]);
+
+  // SSE 연결 함수
+  const connectToSSE = (sessionId) => {
+    const eventSource = new EventSource(`/api/product/compare/stream/${sessionId}`);
+    
+    eventSource.onmessage = (event) => {
+      try {
+        const data = JSON.parse(event.data);
+        if (data.message) {
+          addLog(data.message, data.agent || 'info');
+        }
+        // 최종 결과가 오면 분석 결과 업데이트
+        if (data.final_result) {
+          setAnalysisResults(data.final_result);
+          setIsProcessing(false);
+          eventSource.close();
+        }
+      } catch (err) {
+        addLog(`Event processing error: ${err.message}`, 'error');
+      }
+    };
+    
+    eventSource.onerror = (err) => {
+      addLog('Server connection lost. Please try again in a moment.', 'error');
+      eventSource.close();
+      setIsProcessing(false);
+    };
+    
+    return eventSource;
+  };
+
+  // 제품 분석 처리 핸들러
+  const handleAnalysis = async () => {
+    // 유효성 검사: 최소 1개 이상의 이미지가 있어야 함
+    if (!images[0] && !images[1]) {
+      setError('Please upload at least one product image for analysis');
+      return;
+    }
+
+    setIsProcessing(true);
+    setError(null);
+    setProgressLogs([]); // 로그 초기화
+    addLog('Starting product analysis...', 'system');
+    
+    try {
+      const formData = new FormData();
+      
+      // 업로드된 이미지만 FormData에 추가
+      if (images[0]) {
+        formData.append('image1', images[0]);
+        addLog('Product A image uploaded.', 'info');
+      }
+      if (images[1]) {
+        formData.append('image2', images[1]);
+        addLog('Product B image uploaded.', 'info');
+      }
+      
+      // 분석 타입 추가 (탭 인덱스로 구분)
+      const analysisTypes = ['info', 'compare', 'value', 'recommend'];
+      const analysisType = analysisTypes[tabValue];
+      formData.append('analysisType', analysisType);
+      addLog(`Analysis type: ${analysisType === 'info' ? 'Product Information' : analysisType === 'compare' ? 'Performance Comparison' : analysisType === 'value' ? 'Value Analysis' : 'Purchase Recommendation'}`, 'system');
+      
+      // 백엔드 API 호출 (세션 시작)
+      addLog('Initializing analysis session...', 'system');
+      const response = await fetch('/api/product/compare/start', {
+        method: 'POST',
+        body: formData,
+      });
+      
+      if (!response.ok) {
+        throw new Error(`HTTP error! Status: ${response.status}`);
+      }
+      
+      const data = await response.json();
+      const sessionId = data.session_id;
+      
+      if (!sessionId) {
+        throw new Error('Failed to receive session ID');
+      }
+      
+      addLog(`Analysis session started. (Session ID: ${sessionId.substring(0, 8)}...)`, 'system');
+      addLog('Agents are collaborating to analyze products. Please wait a moment...', 'system');
+      
+      // SSE 스트림 연결
+      const eventSource = connectToSSE(sessionId);
+      
+      // 컴포넌트 언마운트시 연결 종료
+      return () => {
+        eventSource.close();
+      };
+      
+    } catch (err) {
+      console.error('제품 분석 오류:', err);
+      addLog(`Error occurred: ${err.message}`, 'error');
+      setError(`Error during analysis: ${err.message}`);
+      setIsProcessing(false);
+    }
+  };
+
+  // 분석 결과 렌더링 함수
+  const renderAnalysisResults = () => {
+    if (!analysisResults) return null;
+    
+    // 분석 유형에 따라 다른 결과 표시
+    switch (tabValue) {
+      case 0: // 제품 정보 탐색
+        return (
+          <Card className={classes.resultCard}>
+            <CardContent>
+              <Typography variant="h6" gutterBottom>Product Information</Typography>
+              <Divider style={{ margin: '8px 0 16px' }} />
+              
+              {analysisResults.productInfo && (
+                <div>
+                  <Typography variant="subtitle1">
+                    <strong>Product Name:</strong> {analysisResults.productInfo.name}
+                  </Typography>
+                  <Typography variant="body1">
+                    <strong>Brand:</strong> {analysisResults.productInfo.brand}
+                  </Typography>
+                  <Typography variant="body1">
+                    <strong>Category:</strong> {analysisResults.productInfo.category}
+                  </Typography>
+                  
+                  <Typography variant="subtitle1" style={{ marginTop: '16px' }}>Key Specifications</Typography>
+                  <ul>
+                    {analysisResults.productInfo.specs.map((spec, index) => (
+                      <li key={index}>
+                        <Typography variant="body2">
+                          <strong>{spec.name}:</strong> {spec.value}
+                        </Typography>
+                      </li>
+                    ))}
+                  </ul>
+                  
+                  <Typography variant="subtitle1" style={{ marginTop: '16px' }}>Pros</Typography>
+                  <ul>
+                    {analysisResults.productInfo.pros.map((pro, index) => (
+                      <li key={index}>
+                        <Typography variant="body2">{pro}</Typography>
+                      </li>
+                    ))}
+                  </ul>
+                  
+                  <Typography variant="subtitle1" style={{ marginTop: '16px' }}>Cons</Typography>
+                  <ul>
+                    {analysisResults.productInfo.cons.map((con, index) => (
+                      <li key={index}>
+                        <Typography variant="body2">{con}</Typography>
+                      </li>
+                    ))}
+                  </ul>
+                </div>
+              )}
+            </CardContent>
+          </Card>
+        );
+        
+      case 1: // 제품 성능 비교
+        return (
+          <Card className={classes.resultCard}>
+            <CardContent>
+              <Typography variant="h6" gutterBottom>Product Comparison Analysis</Typography>
+              <Divider style={{ margin: '8px 0 16px' }} />
+              
+              {analysisResults.comparison && (
+                <div>
+                  <Typography variant="subtitle1" gutterBottom>Product Specification Comparison</Typography>
+                  
+                  <table className={classes.comparisonTable}>
+                    <thead>
+                      <tr>
+                        <th>Feature</th>
+                        <th>Product A</th>
+                        <th>Product B</th>
+                        <th>Comparison</th>
+                      </tr>
+                    </thead>
+                    <tbody>
+                      {analysisResults.comparison.specs.map((spec, index) => (
+                        <tr key={index}>
+                          <td><strong>{spec.name}</strong></td>
+                          <td>{spec.valueA}</td>
+                          <td>{spec.valueB}</td>
+                          <td>
+                            <span className={spec.winner === 'A' ? classes.highlight : ''}>
+                              {spec.comparison}
+                            </span>
+                          </td>
+                        </tr>
+                      ))}
+                    </tbody>
+                  </table>
+                  
+                  <Typography variant="subtitle1" style={{ marginTop: '16px' }}>Overall Comparison</Typography>
+                  <Typography variant="body1">{analysisResults.comparison.summary}</Typography>
+                </div>
+              )}
+            </CardContent>
+          </Card>
+        );
+        
+      case 2: // 가격 대비 가치 분석
+        return (
+          <Card className={classes.resultCard}>
+            <CardContent>
+              <Typography variant="h6" gutterBottom>Price-to-Value Analysis</Typography>
+              <Divider style={{ margin: '8px 0 16px' }} />
+              
+              {analysisResults.valueAnalysis && (
+                <div>
+                  <Typography variant="subtitle1">Price Information</Typography>
+                  <Typography variant="body1">
+                    <strong>Product A:</strong> {analysisResults.valueAnalysis.priceA}
+                  </Typography>
+                  {analysisResults.valueAnalysis.priceB && (
+                    <Typography variant="body1">
+                      <strong>Product B:</strong> {analysisResults.valueAnalysis.priceB}
+                    </Typography>
+                  )}
+                  
+                  <Typography variant="subtitle1" style={{ marginTop: '16px' }}>Value Analysis</Typography>
+                  <Typography variant="body1">{analysisResults.valueAnalysis.analysis}</Typography>
+                  
+                  <Typography variant="subtitle1" style={{ marginTop: '16px' }}>Price-to-Performance Score</Typography>
+                  <Typography variant="body1">
+                    <strong>Product A:</strong> {analysisResults.valueAnalysis.valueScoreA}/10
+                  </Typography>
+                  {analysisResults.valueAnalysis.valueScoreB && (
+                    <Typography variant="body1">
+                      <strong>Product B:</strong> {analysisResults.valueAnalysis.valueScoreB}/10
+                    </Typography>
+                  )}
+                </div>
+              )}
+            </CardContent>
+          </Card>
+        );
+        
+      case 3: // 최적 구매 추천
+        return (
+          <Card className={classes.resultCard}>
+            <CardContent>
+              <Typography variant="h6" gutterBottom>Purchase Recommendations</Typography>
+              <Divider style={{ margin: '8px 0 16px' }} />
+              
+              {analysisResults.recommendation && (
+                <div>
+                  <Typography variant="subtitle1" className={classes.highlight}>
+                    Recommended Product: {analysisResults.recommendation.recommendedProduct}
+                  </Typography>
+                  
+                  <Typography variant="body1" style={{ marginTop: '16px' }}>
+                    {analysisResults.recommendation.reason}
+                  </Typography>
+                  
+                  <Typography variant="subtitle1" style={{ marginTop: '16px' }}>Alternative Products</Typography>
+                  <ul>
+                    {analysisResults.recommendation.alternatives.map((alt, index) => (
+                      <li key={index}>
+                        <Typography variant="body2">
+                          <strong>{alt.name}</strong>: {alt.reason}
+                        </Typography>
+                      </li>
+                    ))}
+                  </ul>
+                  
+                  {analysisResults.recommendation.buyingTips && (
+                    <>
+                      <Typography variant="subtitle1" style={{ marginTop: '16px' }}>Buying Tips</Typography>
+                      <ul>
+                        {analysisResults.recommendation.buyingTips.map((tip, index) => (
+                          <li key={index}>
+                            <Typography variant="body2">{tip}</Typography>
+                          </li>
+                        ))}
+                      </ul>
+                    </>
+                  )}
+                </div>
+              )}
+            </CardContent>
+          </Card>
+        );
+        
+      default:
+        return null;
+    }
+  };
+
+  return (
+    <Paper className={classes.root}>
+      <Box p={3}>
+        <Typography variant="h5" gutterBottom>
+          Product Comparison Analysis
+        </Typography>
+        <Typography variant="body1" paragraph>
+          Upload product images to receive detailed information and comparison analysis.
+          You can analyze various products including cars, smartphones, laptops, and more.
+        </Typography>
+        
+        <Grid container spacing={3}>
+          {/* 이미지 업로드 영역 */}
+          {[0, 1].map((index) => (
+            <Grid item xs={12} md={6} key={index}>
+              <Box className={classes.imageContainer}>
+                {imagePreviews[index] ? (
+                  <>
+                    <img 
+                      src={imagePreviews[index]} 
+                      alt={`Product ${index + 1}`}
+                      className={classes.imagePreview}
+                    />
+                    <IconButton
+                      className={classes.deleteButton}
+                      onClick={() => handleImageDelete(index)}
+                    >
+                      <Delete />
+                    </IconButton>
+                  </>
+                ) : (
+                  <>
+                    <input
+                      accept="image/*"
+                      className={classes.uploadInput}
+                      id={`upload-image-${index}`}
+                      type="file"
+                      onChange={(e) => handleImageUpload(e, index)}
+                    />
+                    <label htmlFor={`upload-image-${index}`}>
+                      <Box display="flex" flexDirection="column" alignItems="center">
+                        <AddCircle className={classes.uploadIcon} />
+                        <Typography variant="body2" style={{ marginTop: '8px' }}>
+                          Upload {index === 0 ? 'Product A' : 'Product B'} Image
+                        </Typography>
+                      </Box>
+                    </label>
+                  </>
+                )}
+              </Box>
+            </Grid>
+          ))}
+          
+          {/* 분석 유형 탭 */}
+          <Grid item xs={12}>
+            <Paper>
+              <Tabs
+                value={tabValue}
+                onChange={handleTabChange}
+                indicatorColor="primary"
+                textColor="primary"
+                centered
+              >
+                <Tab icon={<Info />} label="Product Info" />
+                <Tab icon={<Compare />} label="Performance" disabled={!images[0] || !images[1]} />
+                <Tab icon={<Search />} label="Value Analysis" />
+                <Tab label="Recommendations" />
+              </Tabs>
+              
+              <Box p={2} textAlign="center">
+                <Button
+                  variant="contained"
+                  color="primary"
+                  onClick={handleAnalysis}
+                  disabled={isProcessing || (!images[0] && !images[1])}
+                  startIcon={isProcessing ? <CircularProgress size={24} /> : null}
+                >
+                  {isProcessing ? 'Analyzing...' : 'Start Analysis'}
+                </Button>
+              </Box>
+              
+              {/* 오류 메시지 표시 */}
+              {error && (
+                <Box p={2} bgcolor="#ffebee" borderRadius="4px" mb={2}>
+                  <Typography color="error">{error}</Typography>
+                </Box>
+              )}
+            </Paper>
+          </Grid>
+          
+          {/* 진행 과정 로그 표시 */}
+          <Grid item xs={12}>
+            <Paper>
+              <Box p={2}>
+                <Typography variant="h6" gutterBottom>
+                  Analysis Progress
+                </Typography>
+                <Box className={classes.progressLog}>
+                  {progressLogs.length === 0 ? (
+                    <Typography variant="body2" color="textSecondary" style={{padding: '10px'}}>
+                      Progress details will appear here when analysis starts.
+                    </Typography>
+                  ) : (
+                    progressLogs.map((log, index) => (
+                      <Box 
+                        key={index} 
+                        className={`${classes.logEntry} ${log.type === 'agent' ? classes.logEntryAgent : log.type === 'system' ? classes.logEntrySystem : log.type === 'error' ? classes.logEntryError : ''}`}
+                      >
+                        <span className={classes.logTime}>[{log.time}]</span>
+                        {log.message}
+                      </Box>
+                    ))
+                  )}
+                  <div ref={logEndRef} />
+                </Box>
+              </Box>
+            </Paper>
+          </Grid>
+
+          {/* 결과 표시 영역 */}
+          <Grid item xs={12}>
+            {isProcessing ? (
+              <Box className={classes.loadingContainer}>
+                <CircularProgress />
+                <Typography variant="h6" style={{ marginTop: '16px' }}>
+                  Analyzing Products...
+                </Typography>
+                <Typography variant="body2" color="textSecondary">
+                  Product recognition, information retrieval, and comparison analysis in progress.
+                </Typography>
+              </Box>
+            ) : renderAnalysisResults()}
+          </Grid>
+        </Grid>
+      </Box>
+    </Paper>
+  );
+};
+
+export default ProductComparison;

product_comparison.py

@@ -0,0 +1,1108 @@
+"""
+Product Comparison Multi-Agent System
+
+This module implements a multi-agent system for product comparison based on images.
+The system uses various specialized agents to process images, extract features,
+compare products, and provide recommendations.
+
+Main components:
+- Coordinator: Orchestrates the multi-agent workflow
+- ImageProcessingAgent: Handles image recognition and object detection
+- FeatureExtractionAgent: Extracts product specifications and features
+- ComparisonAgent: Compares products based on extracted features
+- RecommendationAgent: Provides purchase recommendations
+
+Each agent utilizes vision models and LLMs to perform its specialized tasks.
+"""
+
+import os
+import uuid
+import json
+import time
+from typing import Dict, List, Any, Optional, Tuple
+import base64
+from io import BytesIO
+from datetime import datetime
+from threading import Thread
+
+import torch
+from PIL import Image
+import numpy as np
+
+# Import LangChain components for agent implementation
+try:
+    from langchain_core.prompts import ChatPromptTemplate
+    from langchain_core.output_parsers import StrOutputParser, JsonOutputParser
+    from langchain.agents import create_openai_functions_agent
+    from langchain.agents import AgentExecutor
+    from langchain.memory import ConversationBufferMemory
+    from langchain.tools.render import format_tool_to_openai_function
+    from langchain_community.chat_models import ChatOpenAI
+    from langchain_community.tools.python.tool import PythonAstREPLTool
+except ImportError:
+    print("Warning: LangChain components not available. Installing required packages...")
+    import subprocess
+    subprocess.run(["pip", "install", "langchain", "langchain-openai", "langchain-community"])
+    from langchain_core.prompts import ChatPromptTemplate
+    from langchain_core.output_parsers import StrOutputParser, JsonOutputParser
+    from langchain.agents import create_openai_functions_agent
+    from langchain.agents import AgentExecutor
+    from langchain.memory import ConversationBufferMemory
+    from langchain.tools.render import format_tool_to_openai_function
+    from langchain_community.chat_models import ChatOpenAI
+    from langchain_community.tools.python.tool import PythonAstREPLTool
+
+# Import vision models if available
+try:
+    from ultralytics import YOLO
+except ImportError:
+    YOLO = None
+
+try:
+    from transformers import CLIPProcessor, CLIPModel
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+    from transformers import ViTImageProcessor, ViTForImageClassification
+except ImportError:
+    CLIPProcessor = None
+    CLIPModel = None
+    AutoModelForCausalLM = None
+    AutoTokenizer = None
+    ViTImageProcessor = None
+    ViTForImageClassification = None
+
+# Session storage for SSE communication
+class SessionManager:
+    """Manages active product comparison sessions and their event streams"""
+    
+    def __init__(self):
+        self.active_sessions = {}
+        self.results = {}
+        
+    def create_session(self, session_id=None):
+        """Create a new session with unique ID"""
+        if session_id is None:
+            session_id = str(uuid.uuid4())
+        
+        self.active_sessions[session_id] = {
+            "created_at": datetime.now(),
+            "messages": [],
+            "status": "created"
+        }
+        return session_id
+    
+    def add_message(self, session_id, message, agent_type="system"):
+        """Add a message to the session event stream"""
+        if session_id not in self.active_sessions:
+            return False
+        
+        timestamp = datetime.now().strftime("%H:%M:%S")
+        self.active_sessions[session_id]["messages"].append({
+            "message": message,
+            "agent": agent_type,
+            "timestamp": timestamp
+        })
+        return True
+    
+    def get_messages(self, session_id):
+        """Get all messages for a session"""
+        if session_id not in self.active_sessions:
+            return []
+        return self.active_sessions[session_id]["messages"]
+    
+    def set_final_result(self, session_id, result):
+        """Store the final analysis result for a session"""
+        self.results[session_id] = result
+        self.active_sessions[session_id]["status"] = "completed"
+    
+    def get_final_result(self, session_id):
+        """Get the final result for a session"""
+        return self.results.get(session_id)
+    
+    def set_status(self, session_id, status):
+        """Update session status"""
+        if session_id in self.active_sessions:
+            self.active_sessions[session_id]["status"] = status
+    
+    def get_status(self, session_id):
+        """Get session status"""
+        if session_id in self.active_sessions:
+            return self.active_sessions[session_id]["status"]
+        return None
+    
+    def clean_old_sessions(self, max_age_hours=24):
+        """Clean up old sessions"""
+        now = datetime.now()
+        sessions_to_remove = []
+        
+        for session_id, session_data in self.active_sessions.items():
+            age = now - session_data["created_at"]
+            if age.total_seconds() > max_age_hours * 3600:
+                sessions_to_remove.append(session_id)
+        
+        for session_id in sessions_to_remove:
+            del self.active_sessions[session_id]
+            if session_id in self.results:
+                del self.results[session_id]
+
+# Initialize the session manager
+session_manager = SessionManager()
+
+# Base Agent Class
+class BaseAgent:
+    """Base class for all specialized agents"""
+    
+    def __init__(self, name, llm=None):
+        self.name = name
+        
+        # Use TinyLlama as the default LLM if none is provided
+        if llm is None:
+            try:
+                # Initialize TinyLlama model
+                model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+                tokenizer = AutoTokenizer.from_pretrained(model_name)
+                model = AutoModelForCausalLM.from_pretrained(
+                    model_name,
+                    torch_dtype=torch.float16,
+                ).to("cuda" if torch.cuda.is_available() else "cpu")
+                
+                # Wrap the model for LangChain
+                self.llm = self._create_llm_wrapper(model, tokenizer)
+            except Exception as e:
+                print(f"Error loading TinyLlama: {e}")
+                # Fallback to a simple string output
+                self.llm = None
+        else:
+            self.llm = llm
+    
+    def _create_llm_wrapper(self, model, tokenizer):
+        """Create a simple wrapper for the LLM model"""
+        # This is a simplified wrapper - in production, you'd use a proper LangChain integration
+        def generate_text(prompt, max_tokens=512):
+            inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+            with torch.no_grad():
+                output = model.generate(
+                    **inputs,
+                    max_new_tokens=max_tokens,
+                    temperature=0.7,
+                    do_sample=True
+                )
+            response = tokenizer.decode(output[0], skip_special_tokens=True)
+            # Remove the prompt from the response
+            if response.startswith(prompt):
+                response = response[len(prompt):].strip()
+            return response
+        
+        return generate_text
+    
+    def log(self, session_id, message):
+        """Log a message to the session"""
+        return session_manager.add_message(session_id, message, agent_type=self.name)
+    
+    async def process(self, session_id, data):
+        """Process data with this agent - to be implemented by subclasses"""
+        raise NotImplementedError("Subclasses must implement this method")
+
+
+class ImageProcessingAgent(BaseAgent):
+    """Agent responsible for image processing and object recognition
+    
+    This agent uses computer vision models to detect product type, brand, model,
+    and other visual characteristics from product images.
+    """
+    
+    def __init__(self, name="ImageProcessingAgent"):
+        super().__init__(name)
+        # Initialize vision models
+        self.models = self._load_vision_models()
+    
+    def _load_vision_models(self):
+        """Load vision models for product recognition"""
+        models = {}
+        
+        # Try to load YOLO for object detection
+        try:
+            if YOLO is not None:
+                models["yolo"] = YOLO("yolov8n.pt")
+        except Exception as e:
+            print(f"Error loading YOLO: {e}")
+        
+        # Try to load ViT for image classification
+        try:
+            if ViTImageProcessor is not None and ViTForImageClassification is not None:
+                models["vit_processor"] = ViTImageProcessor.from_pretrained("google/vit-base-patch16-224")
+                models["vit_model"] = ViTForImageClassification.from_pretrained("google/vit-base-patch16-224")
+        except Exception as e:
+            print(f"Error loading ViT: {e}")
+        
+        # Try to load CLIP for visual embedding
+        try:
+            if CLIPProcessor is not None and CLIPModel is not None:
+                models["clip_processor"] = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+                models["clip_model"] = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+        except Exception as e:
+            print(f"Error loading CLIP: {e}")
+            
+        return models
+    
+    def _process_with_yolo(self, image):
+        """Process image with YOLO for object detection"""
+        if "yolo" not in self.models:
+            return {"error": "YOLO model not available"}
+            
+        # Convert image to numpy array if it's a PIL image
+        if isinstance(image, Image.Image):
+            image_np = np.array(image)
+        else:
+            image_np = image
+            
+        # Run inference
+        results = self.models["yolo"](image_np)
+        
+        # Extract detection results
+        detections = []
+        for result in results:
+            boxes = result.boxes
+            for box in boxes:
+                class_id = int(box.cls[0].item())
+                class_name = result.names[class_id]
+                confidence = round(box.conf[0].item(), 2)
+                bbox = box.xyxy[0].tolist()
+                bbox = [round(x) for x in bbox]
+                
+                detections.append({
+                    "class": class_name,
+                    "confidence": confidence,
+                    "bbox": bbox
+                })
+        
+        return {"detections": detections}
+    
+    def _process_with_vit(self, image):
+        """Process image with ViT for classification"""
+        if "vit_model" not in self.models or "vit_processor" not in self.models:
+            return {"error": "ViT model not available"}
+            
+        # Prepare image for the model
+        inputs = self.models["vit_processor"](images=image, return_tensors="pt")
+        
+        # Run inference
+        with torch.no_grad():
+            outputs = self.models["vit_model"](**inputs)
+            logits = outputs.logits
+        
+        # Get top 5 predictions
+        probs = torch.nn.functional.softmax(logits, dim=-1)[0]
+        top5_prob, top5_indices = torch.topk(probs, 5)
+        
+        results = []
+        for i, (prob, idx) in enumerate(zip(top5_prob, top5_indices)):
+            class_name = self.models["vit_model"].config.id2label[idx.item()]
+            results.append({
+                "rank": i+1,
+                "class": class_name,
+                "probability": round(prob.item(), 3)
+            })
+            
+        return {"classifications": results}
+    
+    def _extract_product_info_from_vision(self, image, results):
+        """Extract product information using LLM and vision results"""
+        if self.llm is None:
+            # Provide a basic extraction based on detection results only
+            if "detections" in results and results["detections"]:
+                detections = results["detections"]
+                # Get the most confident detection
+                top_detection = max(detections, key=lambda x: x["confidence"])
+                return {
+                    "product_type": top_detection["class"],
+                    "confidence": top_detection["confidence"]
+                }
+            return {"product_type": "unknown"}
+        
+        # If we have an LLM, we can do more sophisticated extraction
+        prompt = f"""Analyze this product image detection results and extract detailed product information.
+        Detection results: {json.dumps(results)}
+        
+        Extract the following information in JSON format:
+        - product_type: The category of the product (car, smartphone, laptop, etc.)
+        - brand: The most likely brand of the product
+        - model: Any model information that can be determined
+        - color: The main color of the product
+        - key_features: List of notable visual features
+        """
+        
+        try:
+            response = self.llm(prompt)
+            # Try to parse as JSON
+            try:
+                extracted = json.loads(response)
+                return extracted
+            except json.JSONDecodeError:
+                # If LLM output is not valid JSON, extract key information using simple parsing
+                lines = response.split('\n')
+                extracted = {}
+                for line in lines:
+                    if ':' in line:
+                        key, value = line.split(':', 1)
+                        key = key.strip().lower().replace(' ', '_')
+                        value = value.strip().strip('"').strip("'")
+                        if key and value:
+                            extracted[key] = value
+                return extracted
+        except Exception as e:
+            print(f"Error extracting product info: {e}")
+            return {"product_type": "unknown", "error": str(e)}
+    
+    async def process(self, session_id, data):
+        """Process product images to identify products and extract features"""
+        self.log(session_id, "Starting image analysis to identify products...")
+        
+        results = {}
+        product_info = {}
+        
+        # Process each image if available
+        images = data.get("images", [])
+        for i, img in enumerate(images):
+            if img is None:
+                continue
+                
+            image_key = f"image{i+1}"
+            self.log(session_id, f"Processing {image_key}...")
+            
+            # Run object detection
+            yolo_results = self._process_with_yolo(img)
+            
+            # Run classification
+            vit_results = self._process_with_vit(img)
+            
+            # Combine results
+            vision_results = {
+                **yolo_results,
+                **vit_results
+            }
+            
+            # Extract product information from vision results
+            info = self._extract_product_info_from_vision(img, vision_results)
+            
+            self.log(session_id, f"Identified product in {image_key}: {info.get('product_type', 'unknown')}")
+            if "brand" in info:
+                self.log(session_id, f"Detected brand: {info['brand']}")
+                
+            results[image_key] = {
+                "vision_results": vision_results,
+                "product_info": info
+            }
+            product_info[image_key] = info
+            
+        self.log(session_id, "Image processing completed")
+        return {
+            "vision_results": results,
+            "product_info": product_info
+        }
+
+
+class FeatureExtractionAgent(BaseAgent):
+    """Agent responsible for extracting detailed product features and specifications
+    
+    This agent analyzes image processing results and uses LLMs to extract detailed
+    product specifications, features, and technical details.
+    """
+    
+    def __init__(self, name="FeatureExtractionAgent"):
+        super().__init__(name)
+    
+    def _extract_specifications(self, product_info):
+        """Extract detailed specifications from product information"""
+        if self.llm is None:
+            # If no LLM is available, return basic specs from product info
+            return product_info
+            
+        # Prepare prompt for specification extraction
+        product_type = product_info.get("product_type", "unknown")
+        prompt = f"""Based on this product information, generate a detailed list of specifications.
+        Product information: {json.dumps(product_info)}
+        
+        For a {product_type}, extract or infer the following specifications in JSON format:
+        """
+        
+        # Add product-specific specification types based on product type
+        if "car" in product_type.lower() or "vehicle" in product_type.lower():
+            prompt += """
+            - make: The manufacturer of the car
+            - model: The model name
+            - year: Estimated year of manufacture
+            - body_type: The body style (sedan, SUV, etc.)
+            - fuel_type: The fuel type if identifiable
+            - engine: Engine specifications if identifiable
+            - color: Exterior color
+            - features: List of visible features
+            """
+        elif "phone" in product_type.lower() or "smartphone" in product_type.lower():
+            prompt += """
+            - brand: The manufacturer
+            - model: The phone model
+            - screen_size: Estimated screen size
+            - camera: Visible camera specifications
+            - color: Device color
+            - generation: Device generation if identifiable
+            - features: List of visible features
+            """
+        elif "laptop" in product_type.lower() or "computer" in product_type.lower():
+            prompt += """
+            - brand: The manufacturer
+            - model: The computer model
+            - screen_size: Estimated screen size
+            - form_factor: Laptop, desktop, all-in-one, etc.
+            - color: Device color
+            - features: List of visible features
+            """
+        else:
+            # Generic product specifications
+            prompt += """
+            - brand: The manufacturer if identifiable
+            - model: The product model if identifiable
+            - color: Product color
+            - dimensions: Estimated dimensions
+            - features: List of visible features
+            - materials: Visible materials used
+            """
+        
+        try:
+            response = self.llm(prompt)
+            # Try to parse as JSON
+            try:
+                specs = json.loads(response)
+                return specs
+            except json.JSONDecodeError:
+                # If LLM output is not valid JSON, extract key information using simple parsing
+                lines = response.split('\n')
+                specs = {}
+                for line in lines:
+                    if ':' in line:
+                        key, value = line.split(':', 1)
+                        key = key.strip().lower().replace(' ', '_').strip('-').strip('_')
+                        value = value.strip().strip('"').strip("'")
+                        if key and value:
+                            specs[key] = value
+                return specs
+        except Exception as e:
+            print(f"Error extracting specifications: {e}")
+            return {"error": str(e)}
+    
+    def _get_feature_highlights(self, specs):
+        """Extract key feature highlights from specifications"""
+        if not specs or not isinstance(specs, dict):
+            return []
+            
+        highlights = []
+        
+        # Extract key features based on product type
+        product_type = specs.get("product_type", "").lower()
+        
+        if "car" in product_type or "vehicle" in product_type:
+            # Highlight car features
+            if "make" in specs and "model" in specs:
+                highlights.append(f"{specs['make']} {specs['model']}")
+            if "year" in specs:
+                highlights.append(f"{specs['year']} model")
+            if "engine" in specs:
+                highlights.append(f"Engine: {specs['engine']}")
+            if "body_type" in specs:
+                highlights.append(f"{specs['body_type']} body style")
+        
+        elif "phone" in product_type or "smartphone" in product_type:
+            # Highlight phone features
+            if "brand" in specs and "model" in specs:
+                highlights.append(f"{specs['brand']} {specs['model']}")
+            if "screen_size" in specs:
+                highlights.append(f"{specs['screen_size']} display")
+            if "camera" in specs:
+                highlights.append(f"Camera: {specs['camera']}")
+        
+        elif "laptop" in product_type or "computer" in product_type:
+            # Highlight laptop features
+            if "brand" in specs and "model" in specs:
+                highlights.append(f"{specs['brand']} {specs['model']}")
+            if "screen_size" in specs:
+                highlights.append(f"{specs['screen_size']} display")
+        
+        # Generic highlights for any product
+        if "features" in specs:
+            if isinstance(specs["features"], list):
+                highlights.extend(specs["features"][:3])  # Top 3 features
+            elif isinstance(specs["features"], str):
+                features = specs["features"].split(",")
+                highlights.extend([f.strip() for f in features[:3]])  # Top 3 features
+        
+        # Add color as a feature if available
+        if "color" in specs:
+            highlights.append(f"{specs['color']} color")
+            
+        return highlights
+    
+    async def process(self, session_id, data):
+        """Process product information to extract detailed specifications"""
+        self.log(session_id, "Extracting detailed product specifications...")
+        
+        results = {}
+        product_info = data.get("product_info", {})
+        
+        if not product_info:
+            self.log(session_id, "No product information available for specification extraction")
+            return {"specifications": {}}
+            
+        # Process each product
+        for key, info in product_info.items():
+            self.log(session_id, f"Extracting specifications for {key}...")
+            
+            # Extract detailed specifications
+            specs = self._extract_specifications(info)
+            
+            # Get feature highlights
+            highlights = self._get_feature_highlights(specs)
+            
+            # Log results
+            if highlights:
+                self.log(session_id, f"Key features for {key}: {', '.join(highlights[:3])}")
+            
+            results[key] = {
+                "specifications": specs,
+                "highlights": highlights
+            }
+            
+        self.log(session_id, "Feature extraction completed")
+        return {"specifications": results}
+
+
+class ComparisonAgent(BaseAgent):
+    """Agent responsible for comparing products based on their specifications
+    
+    This agent analyzes the specifications of multiple products and identifies
+    the key differences, advantages, and disadvantages between them.
+    """
+    
+    def __init__(self, name="ComparisonAgent"):
+        super().__init__(name)
+    
+    def _compare_specifications(self, specs1, specs2):
+        """Compare two sets of product specifications"""
+        if not specs1 or not specs2:
+            return {"error": "Insufficient specification data for comparison"}
+            
+        # If we have an LLM, use it for more sophisticated comparison
+        if self.llm is not None:
+            prompt = f"""Compare these two products based on their specifications.
+            Product 1: {json.dumps(specs1)}
+            Product 2: {json.dumps(specs2)}
+            
+            Provide a detailed comparison in JSON format with the following structure:
+            {{"differences": [...], "product1_advantages": [...], "product2_advantages": [...], "summary": "..."}}
+            
+            - differences: List key differences between the products
+            - product1_advantages: List advantages of Product 1 over Product 2
+            - product2_advantages: List advantages of Product 2 over Product 1
+            - summary: A concise summary of the comparison
+            """
+            
+            try:
+                response = self.llm(prompt)
+                # Try to parse as JSON
+                try:
+                    comparison = json.loads(response)
+                    return comparison
+                except json.JSONDecodeError:
+                    # If LLM output is not valid JSON, extract key sections using simple parsing
+                    sections = {}
+                    current_section = None
+                    section_content = []
+                    
+                    lines = response.split('\n')
+                    for line in lines:
+                        line = line.strip()
+                        if not line:
+                            continue
+                            
+                        if line.endswith(":") and not line.startswith("-"):
+                            # This is a section header
+                            if current_section and section_content:
+                                sections[current_section] = section_content
+                                section_content = []
+                            
+                            current_section = line.strip(":").lower().replace(" ", "_")
+                        elif line.startswith("-") and current_section:
+                            # This is a list item
+                            section_content.append(line.strip("- "))
+                        elif current_section:
+                            # This is content for the current section
+                            section_content.append(line)
+                    
+                    # Add the last section
+                    if current_section and section_content:
+                        sections[current_section] = section_content
+                        
+                    return sections
+            except Exception as e:
+                print(f"Error in LLM comparison: {e}")
+                # Fall back to simple comparison
+                pass
+        
+        # Simple comparison logic as fallback
+        differences = []
+        product1_advantages = []
+        product2_advantages = []
+        
+        # Identify common keys to compare
+        all_keys = set(list(specs1.keys()) + list(specs2.keys()))
+        
+        # Exclude utility keys like 'error'
+        exclude_keys = {'error', 'product_type', 'confidence'}
+        compare_keys = all_keys - exclude_keys
+        
+        for key in compare_keys:
+            val1 = specs1.get(key)
+            val2 = specs2.get(key)
+            
+            if val1 is None and val2 is not None:
+                differences.append(f"Product 2 has {key}: {val2}, but Product 1 doesn't")
+                product2_advantages.append(f"Has {key}: {val2}")
+            elif val1 is not None and val2 is None:
+                differences.append(f"Product 1 has {key}: {val1}, but Product 2 doesn't")
+                product1_advantages.append(f"Has {key}: {val1}")
+            elif val1 != val2:
+                differences.append(f"Different {key}: Product 1 has {val1}, Product 2 has {val2}")
+                
+                # Try to determine advantages based on common metrics
+                if key in ['price', 'weight']:
+                    # Lower is generally better
+                    try:
+                        num1 = float(str(val1).split()[0])
+                        num2 = float(str(val2).split()[0])
+                        if num1 < num2:
+                            product1_advantages.append(f"Lower {key}: {val1}")
+                        else:
+                            product2_advantages.append(f"Lower {key}: {val2}")
+                    except (ValueError, IndexError):
+                        pass
+                elif key in ['screen_size', 'storage', 'memory', 'ram', 'battery', 'capacity']:
+                    # Higher is generally better
+                    try:
+                        num1 = float(str(val1).split()[0])
+                        num2 = float(str(val2).split()[0])
+                        if num1 > num2:
+                            product1_advantages.append(f"Higher {key}: {val1}")
+                        else:
+                            product2_advantages.append(f"Higher {key}: {val2}")
+                    except (ValueError, IndexError):
+                        pass
+        
+        # Create a simple summary
+        product1_type = specs1.get('product_type', 'Product 1')
+        product2_type = specs2.get('product_type', 'Product 2')
+        
+        summary = f"Comparison between {product1_type} and {product2_type} reveals {len(differences)} key differences."
+        
+        if len(product1_advantages) > len(product2_advantages):
+            summary += f" {product1_type} appears to have more advantages."
+        elif len(product2_advantages) > len(product1_advantages):
+            summary += f" {product2_type} appears to have more advantages."
+        else:
+            summary += " Both products have similar number of advantages."
+            
+        return {
+            "differences": differences,
+            "product1_advantages": product1_advantages,
+            "product2_advantages": product2_advantages,
+            "summary": summary
+        }
+    
+    async def process(self, session_id, data):
+        """Compare products based on their specifications"""
+        self.log(session_id, "Starting product comparison analysis...")
+        
+        specifications = data.get("specifications", {})
+        if len(specifications) < 2:
+            self.log(session_id, "Not enough products to compare")
+            return {"comparison": {"error": "Need at least two products to compare"}}
+            
+        # Get the product keys (image1, image2, etc.)
+        product_keys = list(specifications.keys())
+        
+        if len(product_keys) > 2:
+            self.log(session_id, f"Found {len(product_keys)} products, comparing the first two only")
+            product_keys = product_keys[:2]
+            
+        # Get the specifications for each product
+        product1_specs = specifications.get(product_keys[0], {}).get("specifications", {})
+        product2_specs = specifications.get(product_keys[1], {}).get("specifications", {})
+        
+        # Perform comparison
+        comparison = self._compare_specifications(product1_specs, product2_specs)
+        
+        # Log comparison results
+        if "differences" in comparison:
+            num_diff = len(comparison["differences"])
+            self.log(session_id, f"Found {num_diff} key differences between the products")
+            
+            # Log a few example differences
+            if num_diff > 0:
+                for i, diff in enumerate(comparison["differences"][:3]):
+                    self.log(session_id, f"Difference {i+1}: {diff}")
+                    
+        # Log summary if available
+        if "summary" in comparison:
+            self.log(session_id, f"Comparison summary: {comparison['summary']}")
+            
+        self.log(session_id, "Comparison analysis completed")
+        return {
+            "comparison": comparison,
+            "product_keys": product_keys
+        }
+
+
+class RecommendationAgent(BaseAgent):
+    """Agent responsible for providing purchase recommendations
+    
+    This agent analyzes product comparisons and provides personalized recommendations
+    based on the user's needs and preferences.
+    """
+    
+    def __init__(self, name="RecommendationAgent"):
+        super().__init__(name)
+    
+    def _generate_recommendation(self, comparison, product_keys, specifications):
+        """Generate a purchase recommendation based on comparison results"""
+        if not comparison or "error" in comparison:
+            return {
+                "recommendation": "Insufficient data to make a recommendation",
+                "reason": "Could not compare the products",
+                "confidence": 0.0
+            }
+            
+        # Get product information
+        product1_key = product_keys[0]
+        product2_key = product_keys[1]
+        product1_specs = specifications.get(product1_key, {}).get("specifications", {})
+        product2_specs = specifications.get(product2_key, {}).get("specifications", {})
+        
+        product1_type = product1_specs.get("product_type", product1_key)
+        product2_type = product2_specs.get("product_type", product2_key)
+        
+        # If we have an LLM, use it for more sophisticated recommendation
+        if self.llm is not None:
+            prompt = f"""Based on this product comparison, provide a purchase recommendation.
+            Product 1: {json.dumps(product1_specs)}
+            Product 2: {json.dumps(product2_specs)}
+            Comparison: {json.dumps(comparison)}
+            
+            Provide a recommendation in JSON format with the following structure:
+            {{"recommended_product": "1 or 2", "recommendation": "...", "reason": "...", "confidence": 0.0-1.0, "use_cases": [...]}}
+            
+            - recommended_product: Either "1" or "2" indicating which product is recommended
+            - recommendation: A concise recommendation statement
+            - reason: The main reason for the recommendation
+            - confidence: A confidence score between 0 and 1
+            - use_cases: List of ideal use cases for the recommended product
+            """
+            
+            try:
+                response = self.llm(prompt)
+                # Try to parse as JSON
+                try:
+                    recommendation = json.loads(response)
+                    return recommendation
+                except json.JSONDecodeError:
+                    # If LLM output is not valid JSON, extract key information using simple parsing
+                    lines = response.split('\n')
+                    recommendation = {}
+                    
+                    # Look for recommendation indicator
+                    for line in lines:
+                        if "recommended product" in line.lower() and ("1" in line or "2" in line):
+                            recommendation["recommended_product"] = "1" if "1" in line else "2"
+                        elif "recommendation" in line.lower() and ":" in line:
+                            recommendation["recommendation"] = line.split(":", 1)[1].strip().strip('"')
+                        elif "reason" in line.lower() and ":" in line:
+                            recommendation["reason"] = line.split(":", 1)[1].strip().strip('"')
+                        elif "confidence" in line.lower() and ":" in line:
+                            try:
+                                conf_str = line.split(":", 1)[1].strip()
+                                conf = float(conf_str)
+                                recommendation["confidence"] = conf
+                            except ValueError:
+                                recommendation["confidence"] = 0.5
+                        elif "use cases" in line.lower() and ":" in line:
+                            use_cases = line.split(":", 1)[1].strip().strip('"')
+                            recommendation["use_cases"] = [uc.strip() for uc in use_cases.split(",")]
+                    
+                    return recommendation
+            except Exception as e:
+                print(f"Error in LLM recommendation: {e}")
+                # Fall back to simple recommendation
+                pass
+        
+        # Simple recommendation logic as fallback
+        product1_advantages = comparison.get("product1_advantages", [])
+        product2_advantages = comparison.get("product2_advantages", [])
+        
+        # Count advantages
+        p1_count = len(product1_advantages)
+        p2_count = len(product2_advantages)
+        
+        # Simple confidence calculation
+        total_advantages = p1_count + p2_count
+        if total_advantages == 0:
+            confidence = 0.5  # Can't determine
+        else:
+            # Maximum confidence is 0.95, minimum is 0.55
+            max_confidence = 0.95
+            min_confidence = 0.55
+            base_confidence = max(p1_count, p2_count) / total_advantages if total_advantages > 0 else 0.5
+            confidence = min_confidence + base_confidence * (max_confidence - min_confidence)
+            
+        # Make recommendation
+        if p1_count > p2_count:
+            recommended = "1"
+            recommendation = f"{product1_type} is recommended over {product2_type}"
+            reason = f"It has {p1_count} advantages compared to {p2_count} for the alternative"
+        elif p2_count > p1_count:
+            recommended = "2"
+            recommendation = f"{product2_type} is recommended over {product1_type}"
+            reason = f"It has {p2_count} advantages compared to {p1_count} for the alternative"
+        else:
+            # Equal advantages, slight preference for first product
+            recommended = "1"
+            recommendation = f"Both {product1_type} and {product2_type} appear equally matched"
+            reason = "Consider your specific needs as both have similar advantages"
+            confidence = 0.5  # Equal confidence
+            
+        # Determine use cases based on advantages
+        use_cases = []
+        
+        if recommended == "1" and product1_advantages:
+            # Extract use cases from product 1's advantages
+            use_cases = [f"When {adv.lower()}" for adv in product1_advantages[:3]]
+        elif recommended == "2" and product2_advantages:
+            # Extract use cases from product 2's advantages
+            use_cases = [f"When {adv.lower()}" for adv in product2_advantages[:3]]
+        
+        # Add generic use case if none found
+        if not use_cases:
+            use_cases = [f"General {product1_type if recommended == '1' else product2_type} usage"]
+            
+        return {
+            "recommended_product": recommended,
+            "recommendation": recommendation,
+            "reason": reason,
+            "confidence": round(confidence, 2),
+            "use_cases": use_cases
+        }
+    
+    async def process(self, session_id, data):
+        """Generate purchase recommendations based on product comparison"""
+        self.log(session_id, "Generating product recommendations...")
+        
+        # Get comparison data
+        comparison = data.get("comparison", {})
+        product_keys = data.get("product_keys", [])
+        specifications = data.get("specifications", {})
+        
+        if not comparison or not product_keys or len(product_keys) < 2:
+            self.log(session_id, "Insufficient data for recommendation")
+            return {"recommendation": {"error": "Not enough data to generate recommendation"}}
+            
+        # Generate recommendation
+        recommendation = self._generate_recommendation(comparison, product_keys, specifications)
+        
+        # Log recommendation
+        self.log(session_id, f"Recommendation: {recommendation.get('recommendation', 'No recommendation')}")
+        self.log(session_id, f"Reason: {recommendation.get('reason', 'No reason provided')}")
+        
+        # Log confidence
+        confidence = recommendation.get('confidence', 0)
+        confidence_percent = f"{int(confidence * 100)}%"
+        self.log(session_id, f"Confidence in recommendation: {confidence_percent}")
+        
+        self.log(session_id, "Recommendation generation completed")
+        return {"recommendation": recommendation}
+
+
+class ProductComparisonCoordinator:
+    """Main coordinator for the product comparison multi-agent system
+    
+    This class orchestrates the entire product comparison workflow by managing
+    all the specialized agents and their interactions.
+    """
+    
+    def __init__(self):
+        # Initialize all agents
+        self.image_processor = ImageProcessingAgent()
+        self.feature_extractor = FeatureExtractionAgent()
+        self.comparison_agent = ComparisonAgent()
+        self.recommendation_agent = RecommendationAgent()
+        
+    async def process_images(self, session_id, images, session_metadata=None):
+        """Process images through the entire multi-agent workflow
+        
+        Args:
+            session_id: Unique session identifier
+            images: List of image data (PIL Images or numpy arrays)
+            session_metadata: Optional dictionary with additional session information
+            
+        Returns:
+            Dictionary containing the final analysis results
+        """
+        # Initialize session if it doesn't exist
+        if session_manager.get_status(session_id) is None:
+            session_manager.create_session(session_id)
+        
+        # Set default metadata if not provided
+        if session_metadata is None:
+            session_metadata = {}
+        
+        # Get analysis type from metadata
+        analysis_type = session_metadata.get('analysis_type', 'info')
+        
+        session_manager.set_status(session_id, "processing")
+        session_manager.add_message(session_id, f"Starting product {analysis_type} analysis")
+        
+        try:
+            # Step 1: Process images with Image Processing Agent
+            session_manager.add_message(session_id, "Step 1: Analyzing product images...")
+            image_results = await self.image_processor.process(session_id, {"images": images})
+            
+            # Check if we have enough products to compare
+            product_info = image_results.get("product_info", {})
+            if len(product_info) < 1:
+                session_manager.add_message(session_id, "Error: No products detected in images")
+                session_manager.set_status(session_id, "error")
+                return {"error": "No products detected in images"}
+                
+            # Step 2: Extract features with Feature Extraction Agent
+            session_manager.add_message(session_id, "Step 2: Extracting product specifications...")
+            feature_results = await self.feature_extractor.process(session_id, image_results)
+            
+            # Step 3: Compare products with Comparison Agent if we have multiple products
+            comparison_results = {}
+            if len(product_info) >= 2:
+                session_manager.add_message(session_id, "Step 3: Comparing products...")
+                comparison_results = await self.comparison_agent.process(
+                    session_id, 
+                    {**feature_results, "specifications": feature_results.get("specifications", {})}
+                )
+                
+                # Step 4: Generate recommendation with Recommendation Agent
+                session_manager.add_message(session_id, "Step 4: Generating purchase recommendation...")
+                recommendation_results = await self.recommendation_agent.process(
+                    session_id,
+                    {**comparison_results, "specifications": feature_results.get("specifications", {})}
+                )
+            else:
+                session_manager.add_message(session_id, "Skipping comparison: Only one product detected")
+                comparison_results = {"comparison": {"error": "Need at least two products to compare"}}
+                recommendation_results = {"recommendation": {"error": "Need at least two products to compare"}}
+                
+            # Tailor results based on analysis type
+            final_results = {
+                "status": "completed"
+            }
+            
+            # Include results based on analysis type
+            if analysis_type == 'info':
+                final_results["productInfo"] = image_results.get("product_info", {})
+                final_results["specifications"] = feature_results.get("specifications", {})
+                
+            elif analysis_type == 'compare':
+                final_results["comparison"] = comparison_results.get("comparison", {})
+                final_results["productInfo"] = image_results.get("product_info", {})
+                
+            elif analysis_type == 'value':
+                # Value analysis combines specs and comparison data
+                final_results["valueAnalysis"] = {
+                    "priceA": "$" + str(1000 + int(hash(str(session_id)) % 500)),  # Mock price for demo
+                    "valueScoreA": 7 + (int(hash(str(session_id)) % 3)),  # Mock score between 7-9
+                    "analysis": "Based on the specifications and market positioning, this product offers good value for money."
+                }
+                if len(product_info) >= 2:
+                    final_results["valueAnalysis"]["priceB"] = "$" + str(1200 + int(hash(str(session_id + "B")) % 500))
+                    final_results["valueAnalysis"]["valueScoreB"] = 6 + (int(hash(str(session_id + "B")) % 4))
+                
+            elif analysis_type == 'recommend':
+                if recommendation_results.get("recommendation", {}).get("error"):
+                    # Create a mock recommendation if only one product
+                    if len(product_info) == 1:
+                        product_name = next(iter(product_info.values())).get("name", "Product")
+                        final_results["recommendation"] = {
+                            "recommendedProduct": product_name,
+                            "reason": "This is the only product analyzed and appears to meet standard quality benchmarks.",
+                            "confidence": 0.85,
+                            "alternatives": [
+                                {"name": "Similar model with higher storage", "reason": "If you need more storage capacity"},
+                                {"name": "Budget alternative", "reason": "If price is your primary concern"}
+                            ],
+                            "buyingTips": [
+                                "Wait for seasonal sales for the best price",
+                                "Check for warranty terms before purchasing"
+                            ]
+                        }
+                    else:
+                        final_results["recommendation"] = recommendation_results.get("recommendation", {})
+                else:
+                    final_results["recommendation"] = recommendation_results.get("recommendation", {})
+            
+            # Always include basic product info and vision results for context
+            final_results["vision_results"] = image_results.get("vision_results", {})
+            if "productInfo" not in final_results:  # Don't duplicate if already added
+                final_results["productInfo"] = image_results.get("product_info", {})
+            
+            # Set final results in session manager
+            session_manager.set_final_result(session_id, final_results)
+            session_manager.set_status(session_id, "completed")
+            session_manager.add_message(session_id, "Product comparison analysis completed successfully")
+            
+            return final_results
+            
+        except Exception as e:
+            error_msg = f"Error during product comparison: {str(e)}"
+            session_manager.add_message(session_id, error_msg)
+            session_manager.set_status(session_id, "error")
+            return {"error": error_msg}
+    
+    def get_session_messages(self, session_id):
+        """Get all messages for a session"""
+        return session_manager.get_messages(session_id)
+    
+    def get_session_result(self, session_id):
+        """Get the final result for a session"""
+        return session_manager.get_final_result(session_id)
+    
+    def get_session_status(self, session_id):
+        """Get the status of a session"""
+        return session_manager.get_status(session_id)
+
+
+# Helper function to create a coordinator instance
+def get_product_comparison_coordinator():
+    """Get a singleton instance of the ProductComparisonCoordinator"""
+    if not hasattr(get_product_comparison_coordinator, "_instance"):
+        get_product_comparison_coordinator._instance = ProductComparisonCoordinator()
+    return get_product_comparison_coordinator._instance
+
+
+# Helper function to convert base64 image data to PIL Image
+def decode_base64_image(base64_data):
+    """Convert base64 image data to PIL Image"""
+    try:
+        # Check if the base64 data includes a data URL prefix
+        if base64_data.startswith('data:image'):
+            # Extract the actual base64 data after the comma
+            base64_data = base64_data.split(',', 1)[1]
+            
+        # Decode base64 data
+        image_data = base64.b64decode(base64_data)
+        
+        # Convert to PIL Image
+        image = Image.open(BytesIO(image_data))
+        return image
+    except Exception as e:
+        print(f"Error decoding base64 image: {e}")
+        return None

static/asset-manifest.json

@@ -1,24 +1,24 @@
 {
   "files": {
-    "main.css": "/static/css/main.3b4eede1.chunk.css",
-    "main.js": "/static/js/main.fb4939ae.chunk.js",
-    "main.js.map": "/static/js/main.fb4939ae.chunk.js.map",
+    "main.css": "/static/css/main.59c2a54e.chunk.css",
+    "main.js": "/static/js/main.db88b5b1.chunk.js",
+    "main.js.map": "/static/js/main.db88b5b1.chunk.js.map",
     "runtime-main.js": "/static/js/runtime-main.25710301.js",
     "runtime-main.js.map": "/static/js/runtime-main.25710301.js.map",
-    "static/js/2.9b5b80e8.chunk.js": "/static/js/2.9b5b80e8.chunk.js",
-    "static/js/2.9b5b80e8.chunk.js.map": "/static/js/2.9b5b80e8.chunk.js.map",
+    "static/js/2.abf91741.chunk.js": "/static/js/2.abf91741.chunk.js",
+    "static/js/2.abf91741.chunk.js.map": "/static/js/2.abf91741.chunk.js.map",
     "static/js/3.9013e23f.chunk.js": "/static/js/3.9013e23f.chunk.js",
     "static/js/3.9013e23f.chunk.js.map": "/static/js/3.9013e23f.chunk.js.map",
     "index.html": "/index.html",
-    "precache-manifest.2fad43b74e0c6c48ddb79498a374f3f4.js": "/precache-manifest.2fad43b74e0c6c48ddb79498a374f3f4.js",
+    "precache-manifest.653a7aee73b10eaaa2fae0610652f6fa.js": "/precache-manifest.653a7aee73b10eaaa2fae0610652f6fa.js",
     "service-worker.js": "/service-worker.js",
-    "static/css/main.3b4eede1.chunk.css.map": "/static/css/main.3b4eede1.chunk.css.map",
-    "static/js/2.9b5b80e8.chunk.js.LICENSE.txt": "/static/js/2.9b5b80e8.chunk.js.LICENSE.txt"
+    "static/css/main.59c2a54e.chunk.css.map": "/static/css/main.59c2a54e.chunk.css.map",
+    "static/js/2.abf91741.chunk.js.LICENSE.txt": "/static/js/2.abf91741.chunk.js.LICENSE.txt"
   },
   "entrypoints": [
     "static/js/runtime-main.25710301.js",
-    "static/js/2.9b5b80e8.chunk.js",
-    "static/css/main.3b4eede1.chunk.css",
-    "static/js/main.fb4939ae.chunk.js"
+    "static/js/2.abf91741.chunk.js",
+    "static/css/main.59c2a54e.chunk.css",
+    "static/js/main.db88b5b1.chunk.js"
   ]
 }

static/index.html

@@ -1 +1 @@
-<!doctype html><html lang="en"><head><meta charset="utf-8"/><link rel="icon" href="/favicon.ico"/><meta name="viewport" content="width=device-width,initial-scale=1"/><meta name="theme-color" content="#000000"/><meta name="description" content="Multi-Model Object Detection Demo"/><link rel="apple-touch-icon" href="/logo192.png"/><link rel="manifest" href="/manifest.json"/><title>Vision Web App</title><link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Roboto:300,400,500,700&display=swap"/><link href="/static/css/main.3b4eede1.chunk.css" rel="stylesheet"></head><body><noscript>You need to enable JavaScript to run this app.</noscript><div id="root"></div><script>!function(e){function r(r){for(var n,i,a=r[0],c=r[1],l=r[2],p=0,s=[];p<a.length;p++)i=a[p],Object.prototype.hasOwnProperty.call(o,i)&&o[i]&&s.push(o[i][0]),o[i]=0;for(n in c)Object.prototype.hasOwnProperty.call(c,n)&&(e[n]=c[n]);for(f&&f(r);s.length;)s.shift()();return u.push.apply(u,l||[]),t()}function t(){for(var e,r=0;r<u.length;r++){for(var t=u[r],n=!0,a=1;a<t.length;a++){var c=t[a];0!==o[c]&&(n=!1)}n&&(u.splice(r--,1),e=i(i.s=t[0]))}return e}var n={},o={1:0},u=[];function i(r){if(n[r])return n[r].exports;var t=n[r]={i:r,l:!1,exports:{}};return e[r].call(t.exports,t,t.exports,i),t.l=!0,t.exports}i.e=function(e){var r=[],t=o[e];if(0!==t)if(t)r.push(t[2]);else{var n=new Promise((function(r,n){t=o[e]=[r,n]}));r.push(t[2]=n);var u,a=document.createElement("script");a.charset="utf-8",a.timeout=120,i.nc&&a.setAttribute("nonce",i.nc),a.src=function(e){return i.p+"static/js/"+({}[e]||e)+"."+{3:"9013e23f"}[e]+".chunk.js"}(e);var c=new Error;u=function(r){a.onerror=a.onload=null,clearTimeout(l);var t=o[e];if(0!==t){if(t){var n=r&&("load"===r.type?"missing":r.type),u=r&&r.target&&r.target.src;c.message="Loading chunk "+e+" failed.\n("+n+": "+u+")",c.name="ChunkLoadError",c.type=n,c.request=u,t[1](c)}o[e]=void 0}};var l=setTimeout((function(){u({type:"timeout",target:a})}),12e4);a.onerror=a.onload=u,document.head.appendChild(a)}return Promise.all(r)},i.m=e,i.c=n,i.d=function(e,r,t){i.o(e,r)||Object.defineProperty(e,r,{enumerable:!0,get:t})},i.r=function(e){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},i.t=function(e,r){if(1&r&&(e=i(e)),8&r)return e;if(4&r&&"object"==typeof e&&e&&e.__esModule)return e;var t=Object.create(null);if(i.r(t),Object.defineProperty(t,"default",{enumerable:!0,value:e}),2&r&&"string"!=typeof e)for(var n in e)i.d(t,n,function(r){return e[r]}.bind(null,n));return t},i.n=function(e){var r=e&&e.__esModule?function(){return e.default}:function(){return e};return i.d(r,"a",r),r},i.o=function(e,r){return Object.prototype.hasOwnProperty.call(e,r)},i.p="/",i.oe=function(e){throw console.error(e),e};var a=this["webpackJsonpvision-web-app"]=this["webpackJsonpvision-web-app"]||[],c=a.push.bind(a);a.push=r,a=a.slice();for(var l=0;l<a.length;l++)r(a[l]);var f=c;t()}([])</script><script src="/static/js/2.9b5b80e8.chunk.js"></script><script src="/static/js/main.fb4939ae.chunk.js"></script></body></html>
+<!doctype html><html lang="en"><head><meta charset="utf-8"/><link rel="icon" href="/favicon.ico"/><meta name="viewport" content="width=device-width,initial-scale=1"/><meta name="theme-color" content="#000000"/><meta name="description" content="Multi-Model Object Detection Demo"/><link rel="apple-touch-icon" href="/logo192.png"/><link rel="manifest" href="/manifest.json"/><title>Vision Web App</title><link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Roboto:300,400,500,700&display=swap"/><link href="/static/css/main.59c2a54e.chunk.css" rel="stylesheet"></head><body><noscript>You need to enable JavaScript to run this app.</noscript><div id="root"></div><script>!function(e){function r(r){for(var n,i,a=r[0],c=r[1],l=r[2],p=0,s=[];p<a.length;p++)i=a[p],Object.prototype.hasOwnProperty.call(o,i)&&o[i]&&s.push(o[i][0]),o[i]=0;for(n in c)Object.prototype.hasOwnProperty.call(c,n)&&(e[n]=c[n]);for(f&&f(r);s.length;)s.shift()();return u.push.apply(u,l||[]),t()}function t(){for(var e,r=0;r<u.length;r++){for(var t=u[r],n=!0,a=1;a<t.length;a++){var c=t[a];0!==o[c]&&(n=!1)}n&&(u.splice(r--,1),e=i(i.s=t[0]))}return e}var n={},o={1:0},u=[];function i(r){if(n[r])return n[r].exports;var t=n[r]={i:r,l:!1,exports:{}};return e[r].call(t.exports,t,t.exports,i),t.l=!0,t.exports}i.e=function(e){var r=[],t=o[e];if(0!==t)if(t)r.push(t[2]);else{var n=new Promise((function(r,n){t=o[e]=[r,n]}));r.push(t[2]=n);var u,a=document.createElement("script");a.charset="utf-8",a.timeout=120,i.nc&&a.setAttribute("nonce",i.nc),a.src=function(e){return i.p+"static/js/"+({}[e]||e)+"."+{3:"9013e23f"}[e]+".chunk.js"}(e);var c=new Error;u=function(r){a.onerror=a.onload=null,clearTimeout(l);var t=o[e];if(0!==t){if(t){var n=r&&("load"===r.type?"missing":r.type),u=r&&r.target&&r.target.src;c.message="Loading chunk "+e+" failed.\n("+n+": "+u+")",c.name="ChunkLoadError",c.type=n,c.request=u,t[1](c)}o[e]=void 0}};var l=setTimeout((function(){u({type:"timeout",target:a})}),12e4);a.onerror=a.onload=u,document.head.appendChild(a)}return Promise.all(r)},i.m=e,i.c=n,i.d=function(e,r,t){i.o(e,r)||Object.defineProperty(e,r,{enumerable:!0,get:t})},i.r=function(e){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},i.t=function(e,r){if(1&r&&(e=i(e)),8&r)return e;if(4&r&&"object"==typeof e&&e&&e.__esModule)return e;var t=Object.create(null);if(i.r(t),Object.defineProperty(t,"default",{enumerable:!0,value:e}),2&r&&"string"!=typeof e)for(var n in e)i.d(t,n,function(r){return e[r]}.bind(null,n));return t},i.n=function(e){var r=e&&e.__esModule?function(){return e.default}:function(){return e};return i.d(r,"a",r),r},i.o=function(e,r){return Object.prototype.hasOwnProperty.call(e,r)},i.p="/",i.oe=function(e){throw console.error(e),e};var a=this["webpackJsonpvision-web-app"]=this["webpackJsonpvision-web-app"]||[],c=a.push.bind(a);a.push=r,a=a.slice();for(var l=0;l<a.length;l++)r(a[l]);var f=c;t()}([])</script><script src="/static/js/2.abf91741.chunk.js"></script><script src="/static/js/main.db88b5b1.chunk.js"></script></body></html>

static/precache-manifest.4683f6cd857bd9aa85304ac13a82d061.js

@@ -1,30 +0,0 @@
-self.__precacheManifest = (self.__precacheManifest || []).concat([
-  {
-    "revision": "d54f7d4646b42d5859a3c60dc17c2cbc",
-    "url": "/index.html"
-  },
-  {
-    "revision": "bb5a62e98165b2cca977",
-    "url": "/static/css/main.3b4eede1.chunk.css"
-  },
-  {
-    "revision": "ca6d8d363680165da358",
-    "url": "/static/js/2.9b5b80e8.chunk.js"
-  },
-  {
-    "revision": "89a1b2dcd30c03705b2bceeb141b76b6",
-    "url": "/static/js/2.9b5b80e8.chunk.js.LICENSE.txt"
-  },
-  {
-    "revision": "70d68b45d511e1e11f23",
-    "url": "/static/js/3.9013e23f.chunk.js"
-  },
-  {
-    "revision": "bb5a62e98165b2cca977",
-    "url": "/static/js/main.fc0e2dc7.chunk.js"
-  },
-  {
-    "revision": "d8c310b0ac7ffa6d8151",
-    "url": "/static/js/runtime-main.25710301.js"
-  }
-]);

static/{precache-manifest.2fad43b74e0c6c48ddb79498a374f3f4.js → precache-manifest.653a7aee73b10eaaa2fae0610652f6fa.js}

@@ -1,27 +1,27 @@
 self.__precacheManifest = (self.__precacheManifest || []).concat([
   {
-    "revision": "65fe3b2853370de7fac7c1ee9bb1521e",
+    "revision": "63e79b8c5a7091459c975e9ced1d4412",
     "url": "/index.html"
   },
   {
-    "revision": "62814d1f5d8f989e26a8",
-    "url": "/static/css/main.3b4eede1.chunk.css"
+    "revision": "d435db4fd542a4ef6aa8",
+    "url": "/static/css/main.59c2a54e.chunk.css"
   },
   {
-    "revision": "ca6d8d363680165da358",
-    "url": "/static/js/2.9b5b80e8.chunk.js"
+    "revision": "a3aa56e81892b1b7fdee",
+    "url": "/static/js/2.abf91741.chunk.js"
   },
   {
     "revision": "89a1b2dcd30c03705b2bceeb141b76b6",
-    "url": "/static/js/2.9b5b80e8.chunk.js.LICENSE.txt"
+    "url": "/static/js/2.abf91741.chunk.js.LICENSE.txt"
   },
   {
     "revision": "70d68b45d511e1e11f23",
     "url": "/static/js/3.9013e23f.chunk.js"
   },
   {
-    "revision": "62814d1f5d8f989e26a8",
-    "url": "/static/js/main.fb4939ae.chunk.js"
+    "revision": "d435db4fd542a4ef6aa8",
+    "url": "/static/js/main.db88b5b1.chunk.js"
   },
   {
     "revision": "d8c310b0ac7ffa6d8151",

static/precache-manifest.90eb425b86312bf2b8dbe43e7d0377d8.js

@@ -1,30 +0,0 @@
-self.__precacheManifest = (self.__precacheManifest || []).concat([
-  {
-    "revision": "686c81215854d346608ddcf0fcd01a24",
-    "url": "/index.html"
-  },
-  {
-    "revision": "7eefab545d6bfce9770a",
-    "url": "/static/css/main.3b4eede1.chunk.css"
-  },
-  {
-    "revision": "ca6d8d363680165da358",
-    "url": "/static/js/2.9b5b80e8.chunk.js"
-  },
-  {
-    "revision": "89a1b2dcd30c03705b2bceeb141b76b6",
-    "url": "/static/js/2.9b5b80e8.chunk.js.LICENSE.txt"
-  },
-  {
-    "revision": "70d68b45d511e1e11f23",
-    "url": "/static/js/3.9013e23f.chunk.js"
-  },
-  {
-    "revision": "7eefab545d6bfce9770a",
-    "url": "/static/js/main.975ea04a.chunk.js"
-  },
-  {
-    "revision": "d8c310b0ac7ffa6d8151",
-    "url": "/static/js/runtime-main.25710301.js"
-  }
-]);

static/precache-manifest.9a82b4d1673393a138e3168d2b6c841d.js

@@ -1,30 +0,0 @@
-self.__precacheManifest = (self.__precacheManifest || []).concat([
-  {
-    "revision": "b511568a479eef23388cdb305d8d56a3",
-    "url": "/index.html"
-  },
-  {
-    "revision": "eaece3a38555d76d4a73",
-    "url": "/static/css/main.3b4eede1.chunk.css"
-  },
-  {
-    "revision": "ca6d8d363680165da358",
-    "url": "/static/js/2.9b5b80e8.chunk.js"
-  },
-  {
-    "revision": "89a1b2dcd30c03705b2bceeb141b76b6",
-    "url": "/static/js/2.9b5b80e8.chunk.js.LICENSE.txt"
-  },
-  {
-    "revision": "70d68b45d511e1e11f23",
-    "url": "/static/js/3.9013e23f.chunk.js"
-  },
-  {
-    "revision": "eaece3a38555d76d4a73",
-    "url": "/static/js/main.fe247ab2.chunk.js"
-  },
-  {
-    "revision": "d8c310b0ac7ffa6d8151",
-    "url": "/static/js/runtime-main.25710301.js"
-  }
-]);

static/service-worker.js

@@ -14,7 +14,7 @@
 importScripts("https://storage.googleapis.com/workbox-cdn/releases/4.3.1/workbox-sw.js");
 
 importScripts(
-  "/precache-manifest.2fad43b74e0c6c48ddb79498a374f3f4.js"
+  "/precache-manifest.653a7aee73b10eaaa2fae0610652f6fa.js"
 );
 
 self.addEventListener('message', (event) => {
@@ -35,5 +35,5 @@ workbox.precaching.precacheAndRoute(self.__precacheManifest, {});
 
 workbox.routing.registerNavigationRoute(workbox.precaching.getCacheKeyForURL("/index.html"), {
   
-  blacklist: [/^\/_/,/\/[^\/?]+\.[^\/]+$/],
+  blacklist: [/^\/_/,/\/[^/?]+\.[^/]+$/],
 });

static/static/css/main.3b4eede1.chunk.css.map

@@ -1 +0,0 @@
-{"version":3,"sources":["index.css","App.css"],"names":[],"mappings":"AAAA,KACE,QAAS,CACT,mJAEY,CACZ,kCAAmC,CACnC,iCAAkC,CAClC,wBACF,CAEA,KACE,yEAEF,CCbA,KACE,iBACF,CAEA,eACE,UAAW,CACX,WAAY,CACZ,eAAgB,CAChB,kBAAmB,CACnB,aAAc,CACd,YACF,CAEA,cACE,cAAe,CACf,qBAAsB,CACtB,iBAAkB,CAClB,WACF,CAEA,gBACE,eAAgB,CAChB,eACF,CAEA,YACE,cAAe,CACf,kBACF,CAEA,kBACE,0BAA2B,CAC3B,qCACF,CAEA,qBACE,wBAAyB,CACzB,wBACF,CAEA,qBACE,UAAY,CACZ,kBACF,CAEA,kBACE,eAAgB,CAChB,eAAiB,CACjB,UACF","file":"main.3b4eede1.chunk.css","sourcesContent":["body {\n  margin: 0;\n  font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen',\n    'Ubuntu', 'Cantarell', 'Fira Sans', 'Droid Sans', 'Helvetica Neue',\n    sans-serif;\n  -webkit-font-smoothing: antialiased;\n  -moz-osx-font-smoothing: grayscale;\n  background-color: #f5f5f5;\n}\n\ncode {\n  font-family: source-code-pro, Menlo, Monaco, Consolas, 'Courier New',\n    monospace;\n}\n",".App {\n  text-align: center;\n}\n\n.preview-image {\n  width: 100%;\n  height: auto;\n  max-height: 100%;\n  object-fit: contain;\n  display: block;\n  margin-top: 0;\n}\n\n.result-image {\n  max-width: 100%;\n  border: 1px solid #ddd;\n  border-radius: 4px;\n  padding: 4px;\n}\n\n.detection-list {\n  margin-top: 16px;\n  text-align: left;\n}\n\n.model-card {\n  cursor: pointer;\n  transition: all 0.3s;\n}\n\n.model-card:hover {\n  transform: translateY(-5px);\n  box-shadow: 0 10px 20px rgba(0,0,0,0.1);\n}\n\n.model-card.selected {\n  border: 2px solid #3f51b5;\n  background-color: #e8eaf6;\n}\n\n.model-card.disabled {\n  opacity: 0.6;\n  cursor: not-allowed;\n}\n\n.performance-info {\n  margin-top: 16px;\n  font-size: 0.9rem;\n  color: #666;\n}\n"]}

static/static/css/{main.3b4eede1.chunk.css → main.59c2a54e.chunk.css}

@@ -1,2 +1,2 @@
-body{margin:0;font-family:-apple-system,BlinkMacSystemFont,"Segoe UI","Roboto","Oxygen","Ubuntu","Cantarell","Fira Sans","Droid Sans","Helvetica Neue",sans-serif;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale;background-color:#f5f5f5}code{font-family:source-code-pro,Menlo,Monaco,Consolas,"Courier New",monospace}.App{text-align:center}.preview-image{width:100%;height:auto;max-height:100%;object-fit:contain;display:block;margin-top:0}.result-image{max-width:100%;border:1px solid #ddd;border-radius:4px;padding:4px}.detection-list{margin-top:16px;text-align:left}.model-card{cursor:pointer;transition:all .3s}.model-card:hover{transform:translateY(-5px);box-shadow:0 10px 20px rgba(0,0,0,.1)}.model-card.selected{border:2px solid #3f51b5;background-color:#e8eaf6}.model-card.disabled{opacity:.6;cursor:not-allowed}.performance-info{margin-top:16px;font-size:.9rem;color:#666}
-/*# sourceMappingURL=main.3b4eede1.chunk.css.map */
+body{margin:0;font-family:-apple-system,BlinkMacSystemFont,"Segoe UI","Roboto","Oxygen","Ubuntu","Cantarell","Fira Sans","Droid Sans","Helvetica Neue",sans-serif;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale;background-color:#f5f5f5}code{font-family:source-code-pro,Menlo,Monaco,Consolas,"Courier New",monospace}.App{text-align:center}.preview-image{max-width:100%;max-height:300px;margin-top:16px}.result-image{max-width:100%;border:1px solid #ddd;border-radius:4px;padding:4px}.detection-list{margin-top:16px;text-align:left}.model-card{cursor:pointer;transition:all .3s}.model-card:hover{transform:translateY(-5px);box-shadow:0 10px 20px rgba(0,0,0,.1)}.model-card.selected{border:2px solid #3f51b5;background-color:#e8eaf6}.model-card.disabled{opacity:.6;cursor:not-allowed}.performance-info{margin-top:16px;font-size:.9rem;color:#666}
+/*# sourceMappingURL=main.59c2a54e.chunk.css.map */

static/static/css/main.59c2a54e.chunk.css.map

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React, { useState, useRef } from 'react';\nimport { \n  Paper, \n  Typography, \n  Box, \n  Button, \n  IconButton \n} from '@material-ui/core';\nimport CloudUploadIcon from '@material-ui/icons/CloudUpload';\nimport DeleteIcon from '@material-ui/icons/Delete';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  paper: {\n    padding: theme.spacing(2),\n    display: 'flex',\n    flexDirection: 'column',\n    alignItems: 'center',\n    height: '100%',\n    minHeight: 300,\n    transition: 'all 0.3s ease'\n  },\n  dragActive: {\n    border: '2px dashed #3f51b5',\n    backgroundColor: 'rgba(63, 81, 181, 0.05)'\n  },\n  dragInactive: {\n    border: '2px dashed #ccc',\n    backgroundColor: 'white'\n  },\n  uploadBox: {\n    display: 'flex',\n    flexDirection: 'column',\n    alignItems: 'center',\n    justifyContent: 'center',\n    height: '100%',\n    width: '100%',\n    cursor: 'pointer'\n  },\n  uploadIcon: {\n    fontSize: 60,\n    color: '#3f51b5',\n    marginBottom: theme.spacing(2)\n  },\n  supportText: {\n    marginTop: theme.spacing(2)\n  },\n  previewBox: {\n    display: 'flex',\n    flexDirection: 'column',\n    alignItems: 'center',\n    width: '100%',\n    height: '100%',\n    position: 'relative'\n  },\n  imageContainer: {\n    position: 'relative',\n    width: '100%',\n    // Use viewport-based height so any aspect ratio fits inside\n    height: '60vh',\n    [theme.breakpoints.down('sm')]: {\n      height: '45vh',\n    },\n    display: 'flex',\n    justifyContent: 'center',\n    alignItems: 'center',\n    overflow: 'hidden',\n    marginTop: theme.spacing(2),\n  },\n  deleteButton: {\n    position: 'absolute',\n    top: 0,\n    right: 0,\n    backgroundColor: 'rgba(255, 255, 255, 0.7)',\n    '&:hover': {\n      backgroundColor: 'rgba(255, 255, 255, 0.9)',\n    }\n  }\n}));\n\nconst ImageUploader = ({ onImageUpload }) => {\n  const classes = useStyles();\n  const [previewUrl, setPreviewUrl] = useState(null);\n  const [dragActive, setDragActive] = useState(false);\n  const fileInputRef = useRef(null);\n\n  const handleDrag = (e) => {\n    e.preventDefault();\n    e.stopPropagation();\n    if (e.type === 'dragenter' || e.type === 'dragover') {\n      setDragActive(true);\n    } else if (e.type === 'dragleave') {\n      setDragActive(false);\n    }\n  };\n\n  const handleDrop = (e) => {\n    e.preventDefault();\n    e.stopPropagation();\n    setDragActive(false);\n    if (e.dataTransfer.files && e.dataTransfer.files[0]) {\n      handleFiles(e.dataTransfer.files[0]);\n    }\n  };\n\n  const handleChange = (e) => {\n    e.preventDefault();\n    if (e.target.files && e.target.files[0]) {\n      handleFiles(e.target.files[0]);\n    }\n  };\n\n  const handleFiles = (file) => {\n    if (file.type.startsWith('image/')) {\n      setPreviewUrl(URL.createObjectURL(file));\n      onImageUpload(file);\n    } else {\n      alert('Please upload an image file');\n    }\n  };\n\n  const onButtonClick = () => {\n    fileInputRef.current.click();\n  };\n\n  const handleRemoveImage = () => {\n    setPreviewUrl(null);\n    onImageUpload(null);\n    fileInputRef.current.value = \"\";\n  };\n\n  return (\n    <Paper \n      className={`${classes.paper} ${dragActive ? classes.dragActive : classes.dragInactive}`}\n      onDragEnter={handleDrag}\n      onDragLeave={handleDrag}\n      onDragOver={handleDrag}\n      onDrop={handleDrop}\n    >\n      <input\n        ref={fileInputRef}\n        type=\"file\"\n        accept=\"image/*\"\n        onChange={handleChange}\n        style={{ display: 'none' }}\n      />\n\n      {!previewUrl ? (\n        <Box \n          className={classes.uploadBox}\n          onClick={onButtonClick}\n        >\n          <CloudUploadIcon className={classes.uploadIcon} />\n          <Typography variant=\"h6\" gutterBottom>\n            Drag & Drop an image here\n          </Typography>\n          <Typography variant=\"body2\" color=\"textSecondary\" gutterBottom>\n            or\n          </Typography>\n          <Button\n            variant=\"contained\"\n            color=\"primary\"\n            component=\"span\"\n            startIcon={<CloudUploadIcon />}\n          >\n            Browse Files\n          </Button>\n          <Typography variant=\"body2\" color=\"textSecondary\" className={classes.supportText}>\n            Supported formats: JPG, PNG, GIF\n          </Typography>\n        </Box>\n      ) : (\n        <Box className={classes.previewBox}>\n          <Typography variant=\"h6\" gutterBottom>\n            Preview\n          </Typography>\n          <Box className={classes.imageContainer}>\n            <img\n              src={previewUrl}\n              alt=\"Preview\"\n              className=\"preview-image\"\n              style={{\n                width: '100%',\n                height: 'auto',\n                maxHeight: '100%',\n                objectFit: 'contain',\n                display: 'block'\n              }}\n            />\n            <IconButton\n              aria-label=\"delete\"\n              className={classes.deleteButton}\n              onClick={handleRemoveImage}\n            >\n              <DeleteIcon />\n            </IconButton>\n          </Box>\n        </Box>\n      )}\n    </Paper>\n  );\n};\n\nexport default ImageUploader;\n","import React from 'react';\nimport { \n  Grid, \n  Card, \n  CardContent, \n  CardActions, \n  Typography, \n  Button, \n  Chip,\n  Box\n} from '@material-ui/core';\nimport VisibilityIcon from '@material-ui/icons/Visibility';\nimport CategoryIcon from '@material-ui/icons/Category';\nimport PlayArrowIcon from '@material-ui/icons/PlayArrow';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  card: {\n    height: '100%',\n    display: 'flex',\n    flexDirection: 'column',\n  },\n  selectedCard: {\n    border: '2px solid #3f51b5',\n  },\n  unavailableCard: {\n    opacity: 0.6,\n  },\n  cardContent: {\n    flexGrow: 1,\n  },\n  chipContainer: {\n    marginBottom: theme.spacing(1.5),\n  },\n  successChip: {\n    backgroundColor: '#34C759',\n    color: '#fff',\n  },\n  errorChip: {\n    backgroundColor: '#FF3B3F',\n    color: '#fff',\n  },\n  modelType: {\n    marginTop: theme.spacing(1),\n  },\n  processButton: {\n    marginTop: theme.spacing(3),\n    textAlign: 'center',\n  }\n}));\n\nconst ModelSelector = ({ \n  onModelSelect, \n  onProcess, \n  isProcessing, \n  modelsStatus, \n  selectedModel,\n  imageSelected \n}) => {\n  const classes = useStyles();\n  \n  const models = [\n    {\n      id: 'yolo',\n      name: 'YOLOv8',\n      description: 'Fast and accurate object detection',\n      icon: <VisibilityIcon />,\n      available: modelsStatus.yolo\n    },\n    {\n      id: 'detr',\n      name: 'DETR',\n      description: 'DEtection TRansformer for object detection',\n      icon: <VisibilityIcon />,\n      available: modelsStatus.detr\n    },\n    {\n      id: 'vit',\n      name: 'ViT',\n      description: 'Vision Transformer for image classification',\n      icon: <CategoryIcon />,\n      available: modelsStatus.vit\n    }\n  ];\n\n  const handleModelClick = (modelId) => {\n    if (models.find(m => m.id === modelId).available) {\n      onModelSelect(modelId);\n    }\n  };\n\n  return (\n    <Box sx={{ p: 2, height: '100%' }}>\n      <Typography variant=\"h6\" gutterBottom>\n        Select Model\n      </Typography>\n      \n      <Grid container spacing={2}>\n        {models.map((model) => (\n          <Grid item xs={12} sm={4} key={model.id}>\n            <Card \n              className={`\n                ${classes.card} \n                ${selectedModel === model.id ? classes.selectedCard : ''} \n                ${!model.available ? classes.unavailableCard : ''}\n              `}\n              onClick={() => handleModelClick(model.id)}\n            >\n              <CardContent className={classes.cardContent}>\n                <Box sx={{ mb: 2, color: 'primary' }}>\n                  {model.icon}\n                </Box>\n                <Typography variant=\"h5\" component=\"div\" gutterBottom>\n                  {model.name}\n                </Typography>\n                <div className={classes.chipContainer}>\n                  {model.available ? (\n                    <Chip \n                      label=\"Available\" \n                      className={classes.successChip}\n                      size=\"small\" \n                    />\n                  ) : (\n                    <Chip \n                      label=\"Not Available\" \n                      className={classes.errorChip}\n                      size=\"small\" \n                    />\n                  )}\n                </div>\n                <Typography variant=\"body2\" color=\"textSecondary\">\n                  {model.description}\n                </Typography>\n              </CardContent>\n              <CardActions>\n                <Button \n                  size=\"small\" \n                  onClick={() => handleModelClick(model.id)}\n                  disabled={!model.available}\n                  color={selectedModel === model.id ? \"primary\" : \"default\"}\n                  variant={selectedModel === model.id ? \"contained\" : \"outlined\"}\n                  fullWidth\n                >\n                  {selectedModel === model.id ? 'Selected' : 'Select'}\n                </Button>\n              </CardActions>\n            </Card>\n          </Grid>\n        ))}\n      </Grid>\n\n      <div className={classes.processButton}>\n        <Button\n          variant=\"contained\"\n          color=\"primary\"\n          size=\"large\"\n          startIcon={<PlayArrowIcon />}\n          onClick={onProcess}\n          disabled={!selectedModel || !imageSelected || isProcessing}\n        >\n          {isProcessing ? 'Processing...' : 'Process Image'}\n        </Button>\n      </div>\n    </Box>\n  );\n};\n\nexport default ModelSelector;\n","import React, { useState } from 'react';\nimport { \n  Button, \n  Box, \n  Typography, \n  CircularProgress, \n  Snackbar,\n  Dialog,\n  DialogTitle,\n  DialogContent,\n  DialogActions,\n  TextField,\n  FormControl,\n  InputLabel,\n  Select,\n  MenuItem,\n  Grid,\n  Card,\n  CardMedia,\n  CardContent,\n  Chip\n} from '@material-ui/core';\nimport { Alert } from '@material-ui/lab';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  root: {\n    marginTop: theme.spacing(2),\n    marginBottom: theme.spacing(2),\n    padding: theme.spacing(2),\n    backgroundColor: '#f5f5f5',\n    borderRadius: theme.shape.borderRadius,\n  },\n  button: {\n    marginRight: theme.spacing(2),\n  },\n  searchDialog: {\n    minWidth: '500px',\n  },\n  formControl: {\n    marginBottom: theme.spacing(2),\n    minWidth: '100%',\n  },\n  searchResults: {\n    marginTop: theme.spacing(2),\n  },\n  resultCard: {\n    marginBottom: theme.spacing(2),\n  },\n  resultImage: {\n    height: 140,\n    objectFit: 'contain',\n  },\n  chip: {\n    margin: theme.spacing(0.5),\n  },\n  similarityChip: {\n    backgroundColor: theme.palette.primary.main,\n    color: 'white',\n  }\n}));\n\nconst VectorDBActions = ({ results }) => {\n  const classes = useStyles();\n  const [isSaving, setIsSaving] = useState(false);\n  const [saveSuccess, setSaveSuccess] = useState(false);\n  const [saveError, setSaveError] = useState(null);\n  const [openSearchDialog, setOpenSearchDialog] = useState(false);\n  const [searchType, setSearchType] = useState('image');\n  const [searchClass, setSearchClass] = useState('');\n  const [searchResults, setSearchResults] = useState([]);\n  const [isSearching, setIsSearching] = useState(false);\n  const [searchError, setSearchError] = useState(null);\n  \n  // Extract model and data from results\n  const { model, data } = results;\n  \n  // Handle saving to vector DB\n  const handleSaveToVectorDB = async () => {\n    setIsSaving(true);\n    setSaveError(null);\n    \n    try {\n      let response;\n      \n      if (model === 'vit') {\n        // For ViT, save the whole image with classifications\n        response = await fetch('/api/add-to-collection', {\n          method: 'POST',\n          headers: {\n            'Content-Type': 'application/json',\n          },\n          body: JSON.stringify({\n            image: data.image,\n            metadata: {\n              model: 'vit',\n              classifications: data.classifications\n            }\n          })\n        });\n      } else {\n        // For YOLO and DETR, save detected objects\n        response = await fetch('/api/add-detected-objects', {\n          method: 'POST',\n          headers: {\n            'Content-Type': 'application/json',\n          },\n          body: JSON.stringify({\n            image: data.image,\n            objects: data.detections,\n            imageId: generateUUID()\n          })\n        });\n      }\n      \n      if (!response.ok) {\n        throw new Error(`HTTP error! Status: ${response.status}`);\n      }\n      \n      const result = await response.json();\n      \n      if (result.error) {\n        throw new Error(result.error);\n      }\n      \n      setSaveSuccess(true);\n      setTimeout(() => setSaveSuccess(false), 5000);\n    } catch (err) {\n      console.error('Error saving to vector DB:', err);\n      setSaveError(`Error saving to vector DB: ${err.message}`);\n    } finally {\n      setIsSaving(false);\n    }\n  };\n  \n  // Handle opening search dialog\n  const handleOpenSearchDialog = () => {\n    setOpenSearchDialog(true);\n    setSearchResults([]);\n    setSearchError(null);\n  };\n  \n  // Handle closing search dialog\n  const handleCloseSearchDialog = () => {\n    setOpenSearchDialog(false);\n  };\n  \n  // Handle search type change\n  const handleSearchTypeChange = (event) => {\n    setSearchType(event.target.value);\n    setSearchResults([]);\n    setSearchError(null);\n  };\n  \n  // Handle search class change\n  const handleSearchClassChange = (event) => {\n    setSearchClass(event.target.value);\n  };\n  \n  // Handle search\n  const handleSearch = async () => {\n    setIsSearching(true);\n    setSearchError(null);\n    \n    try {\n      let requestBody = {};\n      \n      if (searchType === 'image') {\n        // Search by current image\n        requestBody = {\n          searchType: 'image',\n          image: data.image,\n          n_results: 5\n        };\n      } else {\n        // Search by class name\n        if (!searchClass.trim()) {\n          throw new Error('Please enter a class name');\n        }\n        \n        requestBody = {\n          searchType: 'class',\n          class_name: searchClass.trim(),\n          n_results: 5\n        };\n      }\n      \n      const response = await fetch('/api/search-similar-objects', {\n        method: 'POST',\n        headers: {\n          'Content-Type': 'application/json',\n        },\n        body: JSON.stringify(requestBody)\n      });\n      \n      if (!response.ok) {\n        throw new Error(`HTTP error! Status: ${response.status}`);\n      }\n      \n      const result = await response.json();\n      \n      if (result.error) {\n        throw new Error(result.error);\n      }\n      \n      console.log('Search API response:', result);\n      \n      // The backend responds with {success, searchType, results} structure, so extract only the results array\n      if (result.success && Array.isArray(result.results)) {\n        console.log('Setting search results array:', result.results);\n        console.log('Results array length:', result.results.length);\n        console.log('First result item:', result.results[0]);\n        setSearchResults(result.results);\n      } else {\n        console.error('Unexpected API response format:', result);\n        throw new Error('Unexpected API response format');\n      }\n    } catch (err) {\n      console.error('Error searching vector DB:', err);\n      setSearchError(`Error searching vector DB: ${err.message}`);\n    } finally {\n      setIsSearching(false);\n    }\n  };\n  \n  // Generate UUID for image ID\n  const generateUUID = () => {\n    return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, function(c) {\n      const r = Math.random() * 16 | 0;\n      const v = c === 'x' ? r : (r & 0x3 | 0x8);\n      return v.toString(16);\n    });\n  };\n  \n  // Render search results\n  const renderSearchResults = () => {\n    console.log('Rendering search results:', searchResults);\n    console.log('Search results length:', searchResults.length);\n    \n    if (searchResults.length === 0) {\n      console.log('No results to render');\n      return (\n        <Typography variant=\"body1\">No results found.</Typography>\n      );\n    }\n    \n    return (\n      <Grid container spacing={2}>\n        {searchResults.map((result, index) => {\n          const similarity = (1 - result.distance) * 100;\n          \n          return (\n            <Grid item xs={12} sm={6} key={index}>\n              <Card className={classes.resultCard}>\n                {result.metadata && result.metadata.image_data ? (\n                  <CardMedia\n                    className={classes.resultImage}\n                    component=\"img\"\n                    height=\"200\"\n                    image={`data:image/jpeg;base64,${result.metadata.image_data}`}\n                    alt={result.metadata && result.metadata.class ? result.metadata.class : 'Object'}\n                  />\n                ) : (\n                  <Box \n                    className={classes.resultImage}\n                    style={{ \n                      backgroundColor: '#f0f0f0', \n                      display: 'flex', \n                      alignItems: 'center', \n                      justifyContent: 'center',\n                      height: 200\n                    }}\n                  >\n                    <Typography variant=\"body2\" color=\"textSecondary\">\n                      {result.metadata && result.metadata.class ? result.metadata.class : 'Object'} Image\n                    </Typography>\n                  </Box>\n                )}\n                <CardContent>\n                  <Box display=\"flex\" justifyContent=\"space-between\" alignItems=\"center\" mb={1}>\n                    <Typography variant=\"subtitle1\">Result #{index + 1}</Typography>\n                    <Chip \n                      label={`Similarity: ${similarity.toFixed(2)}%`}\n                      className={classes.similarityChip}\n                      size=\"small\"\n                    />\n                  </Box>\n                  <Typography variant=\"body2\" color=\"textSecondary\">\n                    <strong>Class:</strong> {result.metadata.class || 'N/A'}\n                  </Typography>\n                  {result.metadata.confidence && (\n                    <Typography variant=\"body2\" color=\"textSecondary\">\n                      <strong>Confidence:</strong> {(result.metadata.confidence * 100).toFixed(2)}%\n                    </Typography>\n                  )}\n                  <Typography variant=\"body2\" color=\"textSecondary\">\n                    <strong>Object ID:</strong> {result.id}\n                  </Typography>\n                </CardContent>\n              </Card>\n            </Grid>\n          );\n        })}\n      </Grid>\n    );\n  };\n  \n  return (\n    <Box className={classes.root}>\n      <Typography variant=\"h6\" gutterBottom>\n        Vector Database Actions\n      </Typography>\n      \n      <Box display=\"flex\" alignItems=\"center\" mb={2}>\n        <Button\n          variant=\"contained\"\n          color=\"primary\"\n          onClick={handleSaveToVectorDB}\n          disabled={isSaving}\n          className={classes.button}\n        >\n          {isSaving ? (\n            <>\n              <CircularProgress size={20} color=\"inherit\" style={{ marginRight: 8 }} />\n              Saving...\n            </>\n          ) : (\n            'Save to Vector DB'\n          )}\n        </Button>\n        \n        <Button\n          variant=\"outlined\"\n          color=\"primary\"\n          onClick={handleOpenSearchDialog}\n          className={classes.button}\n        >\n          Search Similar\n        </Button>\n      </Box>\n      \n      {saveError && (\n        <Alert severity=\"error\" style={{ marginTop: 8 }}>\n          {saveError}\n        </Alert>\n      )}\n      \n      <Snackbar open={saveSuccess} autoHideDuration={5000} onClose={() => setSaveSuccess(false)}>\n        <Alert severity=\"success\">\n          {model === 'vit' ? (\n            'Image and classifications successfully saved to vector DB!'\n          ) : (\n            'Detected objects successfully saved to vector DB!'\n          )}\n        </Alert>\n      </Snackbar>\n      \n      {/* Search Dialog */}\n      <Dialog\n        open={openSearchDialog}\n        onClose={handleCloseSearchDialog}\n        maxWidth=\"md\"\n        fullWidth\n      >\n        <DialogTitle>Search Vector Database</DialogTitle>\n        <DialogContent>\n          <FormControl className={classes.formControl}>\n            <InputLabel id=\"search-type-label\">Search Type</InputLabel>\n            <Select\n              labelId=\"search-type-label\"\n              id=\"search-type\"\n              value={searchType}\n              onChange={handleSearchTypeChange}\n            >\n              <MenuItem value=\"image\">Search by Current Image</MenuItem>\n              <MenuItem value=\"class\">Search by Class Name</MenuItem>\n            </Select>\n          </FormControl>\n          \n          {searchType === 'class' && (\n            <FormControl className={classes.formControl}>\n              <TextField\n                label=\"Class Name\"\n                value={searchClass}\n                onChange={handleSearchClassChange}\n                placeholder=\"e.g. person, car, dog...\"\n                fullWidth\n              />\n            </FormControl>\n          )}\n          \n          {searchError && (\n            <Alert severity=\"error\" style={{ marginBottom: 16 }}>\n              {searchError}\n            </Alert>\n          )}\n          \n          <Box className={classes.searchResults}>\n            {isSearching ? (\n              <Box display=\"flex\" justifyContent=\"center\" alignItems=\"center\" p={4}>\n                <CircularProgress />\n                <Typography variant=\"body1\" style={{ marginLeft: 16 }}>\n                  Searching...\n                </Typography>\n              </Box>\n            ) : (\n              <>\n                {console.log('Search dialog render - searchResults:', searchResults)}\n                {searchResults.length > 0 ? renderSearchResults() : \n                  <Typography variant=\"body1\">No results found. Please try another search.</Typography>\n                }\n              </>\n            )}\n          </Box>\n        </DialogContent>\n        <DialogActions>\n          <Button onClick={handleCloseSearchDialog} color=\"default\">\n            Close\n          </Button>\n          <Button \n            onClick={handleSearch} \n            color=\"primary\" \n            variant=\"contained\"\n            disabled={isSearching || (searchType === 'class' && !searchClass.trim())}\n          >\n            Search\n          </Button>\n        </DialogActions>\n      </Dialog>\n    </Box>\n  );\n};\n\nexport default VectorDBActions;\n","import React from 'react';\nimport { \n  Paper, \n  Typography, \n  Box, \n  List, \n  ListItem, \n  ListItemText, \n  Divider,\n  Grid,\n  Chip\n} from '@material-ui/core';\nimport VectorDBActions from './VectorDBActions';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  paper: {\n    padding: theme.spacing(2)\n  },\n  marginBottom: {\n    marginBottom: theme.spacing(2)\n  },\n  resultImage: {\n    maxWidth: '100%',\n    maxHeight: '400px',\n    objectFit: 'contain'\n  },\n  dividerMargin: {\n    margin: `${theme.spacing(2)}px 0`\n  },\n  chipContainer: {\n    display: 'flex',\n    gap: theme.spacing(1),\n    flexWrap: 'wrap'\n  }\n}));\n\nconst ResultDisplay = ({ results }) => {\n  const classes = useStyles();\n  if (!results) return null;\n  \n  const { model, data } = results;\n  \n  // Helper to format times nicely\n  const formatTime = (ms) => {\n    if (ms === undefined || ms === null || isNaN(ms)) return '-';\n    const num = Number(ms);\n    if (num < 1000) return `${num.toFixed(2)} ms`;\n    return `${(num / 1000).toFixed(2)} s`;\n  };\n  \n  // Check if there's an error\n  if (data.error) {\n    return (\n      <Paper sx={{ p: 2, bgcolor: '#ffebee' }}>\n        <Typography color=\"error\">{data.error}</Typography>\n      </Paper>\n    );\n  }\n\n  // Display performance info\n  const renderPerformanceInfo = () => {\n    if (!data.performance) return null;\n    \n    return (\n      <Box className=\"performance-info\">\n        <Divider className={classes.dividerMargin} />\n        <Typography variant=\"body2\">\n          Inference time: {formatTime(data.performance.inference_time)} on {data.performance.device}\n        </Typography>\n      </Box>\n    );\n  };\n\n  // Render for YOLO and DETR (object detection)\n  if (model === 'yolo' || model === 'detr') {\n    return (\n      <Paper className={classes.paper}>\n        <Typography variant=\"h6\" gutterBottom>\n          {model === 'yolo' ? 'YOLOv8' : 'DETR'} Detection Results\n        </Typography>\n        \n        <Grid container spacing={3}>\n          <Grid item xs={12} md={6}>\n            {data.image && (\n              <Box className={classes.marginBottom}>\n                <Typography variant=\"subtitle1\" gutterBottom>\n                  Detection Result\n                </Typography>\n                <img \n                  src={`data:image/png;base64,${data.image}`} \n                  alt=\"Detection Result\" \n                  className={classes.resultImage}\n                />\n              </Box>\n            )}\n          </Grid>\n          \n          <Grid item xs={12} md={6}>\n            <Box className={classes.marginBottom}>\n              <Typography variant=\"subtitle1\" gutterBottom>\n                Detected Objects:\n              </Typography>\n              \n              {data.detections && data.detections.length > 0 ? (\n                <List>\n                  {data.detections.map((detection, index) => (\n                    <React.Fragment key={index}>\n                      <ListItem>\n                        <ListItemText \n                          primary={\n                            <Box style={{ display: 'flex', alignItems: 'center' }}>\n                              <Typography variant=\"body1\" component=\"span\">\n                                {detection.class}\n                              </Typography>\n                              <Chip \n                                label={`${(detection.confidence * 100).toFixed(0)}%`}\n                                size=\"small\"\n                                color=\"primary\"\n                                style={{ marginLeft: 8 }}\n                              />\n                            </Box>\n                          } \n                          secondary={`Bounding Box: [${detection.bbox.join(', ')}]`} \n                        />\n                      </ListItem>\n                      {index < data.detections.length - 1 && <Divider />}\n                    </React.Fragment>\n                  ))}\n                </List>\n              ) : (\n                <Typography variant=\"body1\">No objects detected</Typography>\n              )}\n            </Box>\n          </Grid>\n        </Grid>\n        \n        {renderPerformanceInfo()}\n        \n        {/* Vector DB Actions for Object Detection */}\n        <VectorDBActions results={results} />\n      </Paper>\n    );\n  }\n  \n  // Render for ViT (classification)\n  if (model === 'vit') {\n    return (\n      <Paper className={classes.paper}>\n        <Typography variant=\"h6\" gutterBottom>\n          ViT Classification Results\n        </Typography>\n        \n        <Typography variant=\"subtitle1\" gutterBottom>\n          Top Predictions:\n        </Typography>\n        \n        {data.top_predictions && data.top_predictions.length > 0 ? (\n          <List>\n            {data.top_predictions.map((prediction, index) => (\n              <React.Fragment key={index}>\n                <ListItem>\n                  <ListItemText \n                    primary={\n                      <Box style={{ display: 'flex', alignItems: 'center' }}>\n                        <Typography variant=\"body1\" component=\"span\">\n                          {prediction.rank}. {prediction.class}\n                        </Typography>\n                        <Chip \n                          label={`${(prediction.probability * 100).toFixed(1)}%`}\n                          size=\"small\"\n                          color={index === 0 ? \"primary\" : \"default\"}\n                          style={{ marginLeft: 8 }}\n                        />\n                      </Box>\n                    } \n                  />\n                </ListItem>\n                {index < data.top_predictions.length - 1 && <Divider />}\n              </React.Fragment>\n            ))}\n          </List>\n        ) : (\n          <Typography variant=\"body1\">No classifications available</Typography>\n        )}\n        \n        {renderPerformanceInfo()}\n        \n        {/* Vector DB Actions for ViT Classification */}\n        <VectorDBActions results={results} />\n      </Paper>\n    );\n  }\n  \n  return null;\n};\n\nexport default ResultDisplay;\n","import React, { useState } from 'react';\nimport { \n  Paper, \n  Typography, \n  Box, \n  TextField, \n  Button, \n  CircularProgress,\n  Divider\n} from '@material-ui/core';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  paper: {\n    padding: theme.spacing(2),\n    marginTop: theme.spacing(2)\n  },\n  marginBottom: {\n    marginBottom: theme.spacing(2)\n  },\n  dividerMargin: {\n    margin: `${theme.spacing(2)}px 0`\n  },\n  responseBox: {\n    padding: theme.spacing(2),\n    backgroundColor: '#f5f5f5',\n    borderRadius: theme.shape.borderRadius,\n    marginTop: theme.spacing(2),\n    whiteSpace: 'pre-wrap'\n  },\n  buttonProgress: {\n    marginLeft: theme.spacing(1)\n  }\n}));\n\nconst LlmAnalysis = ({ visionResults, model }) => {\n  const classes = useStyles();\n  const [userQuery, setUserQuery] = useState('');\n  const [isAnalyzing, setIsAnalyzing] = useState(false);\n  const [analysisResult, setAnalysisResult] = useState(null);\n  const [error, setError] = useState(null);\n\n  // Format time for display\n  const formatTime = (ms) => {\n    if (ms === undefined || ms === null || isNaN(ms)) return '-';\n    const num = Number(ms);\n    if (num < 1000) return `${num.toFixed(2)} ms`;\n    return `${(num / 1000).toFixed(2)} s`;\n  };\n\n  const handleAnalyze = async () => {\n    if (!userQuery.trim()) return;\n    \n    setIsAnalyzing(true);\n    setError(null);\n    \n    try {\n      const response = await fetch('/api/analyze', {\n        method: 'POST',\n        headers: {\n          'Content-Type': 'application/json',\n        },\n        body: JSON.stringify({\n          visionResults: visionResults,\n          userQuery: userQuery\n        }),\n      });\n\n      if (!response.ok) {\n        throw new Error(`HTTP error! Status: ${response.status}`);\n      }\n\n      const data = await response.json();\n      \n      if (data.error) {\n        setError(data.error);\n      } else {\n        setAnalysisResult(data);\n      }\n    } catch (err) {\n      console.error('Error analyzing with LLM:', err);\n      setError(`Error analyzing with LLM: ${err.message}`);\n    } finally {\n      setIsAnalyzing(false);\n    }\n  };\n\n  if (!visionResults) return null;\n\n  return (\n    <Paper className={classes.paper}>\n      <Typography variant=\"h6\" gutterBottom>\n        Ask AI about the {model === 'vit' ? 'Classification' : 'Detection'} Results\n      </Typography>\n      \n      <Typography variant=\"body2\" className={classes.marginBottom}>\n        Ask a question about the detected objects or classifications to get an AI-powered analysis.\n      </Typography>\n      \n      <TextField\n        fullWidth\n        label=\"Your question about the image\"\n        variant=\"outlined\"\n        value={userQuery}\n        onChange={(e) => setUserQuery(e.target.value)}\n        disabled={isAnalyzing}\n        className={classes.marginBottom}\n        placeholder={model === 'vit' \n          ? \"E.g., What category does this image belong to?\" \n          : \"E.g., How many people are in this image?\"}\n      />\n      \n      <Button \n        variant=\"contained\" \n        color=\"primary\"\n        onClick={handleAnalyze}\n        disabled={isAnalyzing || !userQuery.trim()}\n      >\n        Analyze with AI\n        {isAnalyzing && <CircularProgress size={24} className={classes.buttonProgress} />}\n      </Button>\n      \n      {error && (\n        <Box mt={2}>\n          <Typography color=\"error\">{error}</Typography>\n        </Box>\n      )}\n      \n      {analysisResult && (\n        <>\n          <Divider className={classes.dividerMargin} />\n          \n          <Typography variant=\"subtitle1\" gutterBottom>\n            AI Analysis:\n          </Typography>\n          \n          <Box className={classes.responseBox}>\n            <Typography variant=\"body1\">\n              {analysisResult.response}\n            </Typography>\n          </Box>\n          \n          {analysisResult.performance && (\n            <Box mt={1}>\n              <Typography variant=\"body2\" color=\"textSecondary\">\n                Analysis time: {formatTime(analysisResult.performance.inference_time)} on {analysisResult.performance.device}\n              </Typography>\n            </Box>\n          )}\n        </>\n      )}\n    </Paper>\n  );\n};\n\nexport default LlmAnalysis;\n","import React, { useState } from 'react';\nimport {\n  Paper,\n  Typography,\n  Grid,\n  TextField,\n  Button,\n  Divider,\n  MenuItem,\n  Select,\n  FormControl,\n  InputLabel\n} from '@material-ui/core';\n\n// Props:\n// - imageBase64: data URL string of the currently uploaded image (preferred for searchType=image)\nfunction OpenAIChat({ imageBase64 }) {\n  const [apiKey, setApiKey] = useState('');\n  const [prompt, setPrompt] = useState('');\n  const [response, setResponse] = useState('');\n  const [loading, setLoading] = useState(false);\n  const [error, setError] = useState('');\n\n  // Vision RAG parameters\n  const [searchType, setSearchType] = useState('image');\n  const [objectId, setObjectId] = useState('');\n  const [className, setClassName] = useState('');\n  const [nResults, setNResults] = useState(5);\n\n  const onSend = async () => {\n    setError('');\n    setResponse('');\n    const q = (prompt || '').trim();\n    if (!q) { setError('질문을 입력하세요.'); return; }\n\n    // Build request body for /api/vision-rag/query\n    const body = {\n      userQuery: q,\n      searchType: searchType,\n      n_results: Number(nResults) || 5,\n    };\n    if (apiKey) body.api_key = apiKey;\n    if (searchType === 'image') {\n      if (!imageBase64) { setError('이미지가 필요합니다. 먼저 이미지를 업로드하세요.'); return; }\n      body.image = imageBase64;\n    } else if (searchType === 'object') {\n      if (!objectId.trim()) { setError('objectId를 입력하세요.'); return; }\n      body.objectId = objectId.trim();\n    } else if (searchType === 'class') {\n      if (!className.trim()) { setError('class_name을 입력하세요.'); return; }\n      body.class_name = className.trim();\n    }\n\n    setLoading(true);\n    try {\n      const res = await fetch('/api/vision-rag/query', {\n        method: 'POST',\n        headers: { 'Content-Type': 'application/json' },\n        credentials: 'include',\n        body: JSON.stringify(body)\n      });\n\n      if (!res.ok) {\n        let txt = await res.text();\n        try { txt = JSON.stringify(JSON.parse(txt), null, 2); } catch {}\n        throw new Error(txt);\n      }\n      const data = await res.json();\n      const meta = `Model: ${data.model || '-'} | Latency: ${data.latency_sec || '-'}s`;\n      setResponse((data.answer || '(빈 응답)') + '\\n\\n---\\n' + meta);\n    } catch (e) {\n      setError('Error: ' + e.message);\n    } finally {\n      setLoading(false);\n    }\n  };\n\n  const onClear = () => {\n    setPrompt('');\n    setResponse('');\n    setError('');\n  };\n\n  return (\n    <Paper style={{ padding: 16 }}>\n      <Typography variant=\"h5\" gutterBottom>\n        Vision RAG (LangChain)\n      </Typography>\n      <Typography variant=\"body2\" color=\"textSecondary\" gutterBottom>\n        서버에 OPENAI_API_KEY가 설정되어 있다면 API Key는 생략 가능합니다. 검색 유형을 선택하고 질문을 보내면, 벡터 DB에서 검색된 컨텍스트로 답변합니다.\n      </Typography>\n      <Grid container spacing={2}>\n        <Grid item xs={12} md={6}>\n          <FormControl fullWidth variant=\"outlined\" size=\"small\">\n            <InputLabel id=\"search-type-label\">Search Type</InputLabel>\n            <Select\n              labelId=\"search-type-label\"\n              value={searchType}\n              onChange={(e) => setSearchType(e.target.value)}\n              label=\"Search Type\"\n            >\n              <MenuItem value=\"image\">image (current upload)</MenuItem>\n              <MenuItem value=\"object\">object (objectId)</MenuItem>\n              <MenuItem value=\"class\">class (class_name)</MenuItem>\n            </Select>\n          </FormControl>\n        </Grid>\n        <Grid item xs={12} md={6}>\n          <TextField\n            label=\"OpenAI API Key (optional)\"\n            value={apiKey}\n            onChange={(e) => setApiKey(e.target.value)}\n            fullWidth\n            variant=\"outlined\"\n            size=\"small\"\n            type=\"password\"\n            placeholder=\"sk-...\"\n          />\n        </Grid>\n\n        {searchType === 'object' && (\n          <Grid item xs={12} md={6}>\n            <TextField\n              label=\"objectId\"\n              value={objectId}\n              onChange={(e) => setObjectId(e.target.value)}\n              fullWidth\n              variant=\"outlined\"\n              size=\"small\"\n            />\n          </Grid>\n        )}\n        {searchType === 'class' && (\n          <Grid item xs={12} md={6}>\n            <TextField\n              label=\"class_name\"\n              value={className}\n              onChange={(e) => setClassName(e.target.value)}\n              fullWidth\n              variant=\"outlined\"\n              size=\"small\"\n              placeholder=\"e.g. person, car, dog\"\n            />\n          </Grid>\n        )}\n        <Grid item xs={12} md={6}>\n          <TextField\n            label=\"n_results\"\n            value={nResults}\n            onChange={(e) => setNResults(e.target.value)}\n            fullWidth\n            variant=\"outlined\"\n            size=\"small\"\n            type=\"number\"\n            inputProps={{ min: 1, max: 50 }}\n          />\n        </Grid>\n\n        <Grid item xs={12}>\n          <TextField\n            label=\"User Question\"\n            value={prompt}\n            onChange={(e) => setPrompt(e.target.value)}\n            fullWidth\n            multiline\n            rows={4}\n            variant=\"outlined\"\n            placeholder={searchType === 'image' ? '업로드한 이미지 기반으로 답변해줘' : '검색된 객체 컨텍스트를 사용해 답변해줘'}\n          />\n        </Grid>\n        {error && (\n          <Grid item xs={12}>\n            <Typography color=\"error\">{error}</Typography>\n          </Grid>\n        )}\n        <Grid item xs={12}>\n          <div style={{ display: 'flex', gap: 8 }}>\n            <Button color=\"primary\" variant=\"contained\" onClick={onSend} disabled={loading}>\n              {loading ? 'Sending...' : 'Send Question'}\n            </Button>\n            <Button variant=\"outlined\" onClick={onClear}>Clear</Button>\n          </div>\n        </Grid>\n        <Grid item xs={12}>\n          <Divider style={{ margin: '12px 0' }} />\n          <Typography variant=\"subtitle2\" color=\"textSecondary\">Response</Typography>\n          <pre style={{ whiteSpace: 'pre-wrap', fontFamily: 'ui-monospace, monospace' }}>{response}</pre>\n        </Grid>\n      </Grid>\n    </Paper>\n  );\n}\n\nexport default OpenAIChat;\n","import React, { useState, useEffect } from 'react';\nimport { \n  Container, \n  Typography, \n  Box, \n  Paper, \n  Grid, \n  CircularProgress,\n  AppBar,\n  Toolbar,\n  ThemeProvider,\n  createMuiTheme\n} from '@material-ui/core';\nimport ImageUploader from './components/ImageUploader';\nimport ModelSelector from './components/ModelSelector';\nimport ResultDisplay from './components/ResultDisplay';\nimport LlmAnalysis from './components/LlmAnalysis';\nimport OpenAIChat from './components/OpenAIChat';\nimport './App.css';\n\n// Create a theme\nconst theme = createMuiTheme({\n  palette: {\n    primary: {\n      main: '#3f51b5',\n    },\n    secondary: {\n      main: '#f50057',\n    },\n  },\n  typography: {\n    fontFamily: 'Roboto, Arial, sans-serif',\n  },\n});\n\nfunction App() {\n  const [selectedImage, setSelectedImage] = useState(null);\n  const [imageBase64, setImageBase64] = useState('');\n  const [selectedModel, setSelectedModel] = useState('');\n  const [isProcessing, setIsProcessing] = useState(false);\n  const [results, setResults] = useState(null);\n  const [error, setError] = useState(null);\n  const [modelsStatus, setModelsStatus] = useState({\n    yolo: false,\n    detr: false,\n    vit: false\n  });\n\n  // Check API status on component mount\n  useEffect(() => {\n    fetch('/api/status')\n      .then(response => response.json())\n      .then(data => {\n        setModelsStatus(data.models);\n      })\n      .catch(err => {\n        console.error('Error checking API status:', err);\n        setError('Error connecting to the backend API. Please make sure the server is running.');\n      });\n  }, []);\n\n  const handleImageUpload = (image) => {\n    setSelectedImage(image);\n    setResults(null);\n    setError(null);\n\n    // Convert uploaded File -> base64 data URL for Vision RAG image search\n    if (image instanceof File) {\n      const reader = new FileReader();\n      reader.onload = () => {\n        const dataUrl = reader.result; // e.g., data:image/png;base64,....\n        setImageBase64(typeof dataUrl === 'string' ? dataUrl : '');\n      };\n      reader.onerror = () => setImageBase64('');\n      reader.readAsDataURL(image);\n    } else {\n      setImageBase64('');\n    }\n  };\n\n  const handleModelSelect = (model) => {\n    setSelectedModel(model);\n    setResults(null);\n    setError(null);\n  };\n\n  const processImage = async () => {\n    if (!selectedImage || !selectedModel) {\n      setError('Please select both an image and a model');\n      return;\n    }\n\n    setIsProcessing(true);\n    setError(null);\n\n    // Create form data for the image\n    const formData = new FormData();\n    formData.append('image', selectedImage);\n\n    let endpoint = '';\n    switch (selectedModel) {\n      case 'yolo':\n        endpoint = '/api/detect/yolo';\n        break;\n      case 'detr':\n        endpoint = '/api/detect/detr';\n        break;\n      case 'vit':\n        endpoint = '/api/classify/vit';\n        break;\n      default:\n        setError('Invalid model selection');\n        setIsProcessing(false);\n        return;\n    }\n\n    try {\n      const response = await fetch(endpoint, {\n        method: 'POST',\n        body: formData,\n      });\n\n      if (!response.ok) {\n        throw new Error(`HTTP error! Status: ${response.status}`);\n      }\n\n      const data = await response.json();\n      setResults({ model: selectedModel, data });\n    } catch (err) {\n      console.error('Error processing image:', err);\n      setError(`Error processing image: ${err.message}`);\n    } finally {\n      setIsProcessing(false);\n    }\n  };\n\n  return (\n    <ThemeProvider theme={theme}>\n      <Box style={{ flexGrow: 1 }}>\n        <AppBar position=\"static\">\n          <Toolbar>\n            <Typography variant=\"h6\" style={{ flexGrow: 1 }}>\n              Multi-Model Object Detection Demo\n            </Typography>\n          </Toolbar>\n        </AppBar>\n        <Container maxWidth=\"lg\" style={{ marginTop: theme.spacing(4), marginBottom: theme.spacing(4) }}>\n          <Grid container spacing={3}>\n            <Grid item xs={12}>\n              <Paper style={{ padding: theme.spacing(2) }}>\n                <Typography variant=\"h5\" gutterBottom>\n                  Upload an image to see how each model performs!\n                </Typography>\n                <Typography variant=\"body1\" paragraph>\n                  This demo showcases three different object detection and image classification models:\n                </Typography>\n                <Typography variant=\"body1\" component=\"div\">\n                  <ul>\n                    <li><strong>YOLOv8</strong>: Fast and accurate object detection</li>\n                    <li><strong>DETR</strong>: DEtection TRansformer for object detection</li>\n                    <li><strong>ViT</strong>: Vision Transformer for image classification</li>\n                  </ul>\n                </Typography>\n              </Paper>\n            </Grid>\n            \n            <Grid item xs={12} md={6}>\n              <ImageUploader onImageUpload={handleImageUpload} />\n            </Grid>\n            \n            <Grid item xs={12} md={6}>\n              <ModelSelector \n                onModelSelect={handleModelSelect} \n                onProcess={processImage}\n                isProcessing={isProcessing}\n                modelsStatus={modelsStatus}\n                selectedModel={selectedModel}\n                imageSelected={!!selectedImage}\n              />\n            </Grid>\n            \n            {error && (\n              <Grid item xs={12}>\n                <Paper style={{ padding: theme.spacing(2), backgroundColor: '#ffebee' }}>\n                  <Typography color=\"error\">{error}</Typography>\n                </Paper>\n              </Grid>\n            )}\n            \n            {isProcessing && (\n              <Grid item xs={12} style={{ textAlign: 'center', margin: `${theme.spacing(4)}px 0` }}>\n                <CircularProgress />\n                <Typography variant=\"h6\" style={{ marginTop: theme.spacing(2) }}>\n                  Processing image...\n                </Typography>\n              </Grid>\n            )}\n            \n            {results && (\n              <>\n                <Grid item xs={12}>\n                  <ResultDisplay results={results} />\n                </Grid>\n                <Grid item xs={12}>\n                  <LlmAnalysis visionResults={results.data} model={results.model} />\n                </Grid>\n              </>\n            )}\n\n            {/* Vision RAG (LangChain) section at the end */}\n            <Grid item xs={12}>\n              <OpenAIChat imageBase64={imageBase64} />\n            </Grid>\n          </Grid>\n        </Container>\n      </Box>\n    </ThemeProvider>\n  );\n}\n\nexport default App;\n","const reportWebVitals = (onPerfEntry) => {\n  if (onPerfEntry && onPerfEntry instanceof Function) {\n    import('web-vitals').then(({ getCLS, getFID, getFCP, getLCP, getTTFB }) => {\n      getCLS(onPerfEntry);\n      getFID(onPerfEntry);\n      getFCP(onPerfEntry);\n      getLCP(onPerfEntry);\n      getTTFB(onPerfEntry);\n    });\n  }\n};\n\nexport default reportWebVitals;\n","import React from 'react';\nimport ReactDOM from 'react-dom';\nimport './index.css';\nimport App from './App';\nimport reportWebVitals from './reportWebVitals';\n\nReactDOM.render(\n  <React.StrictMode>\n    <App />\n  </React.StrictMode>,\n  document.getElementById('root')\n);\n\n// If you want to start measuring performance in your app, pass a function\n// to log results (for example: reportWebVitals(console.log))\n// or send to an analytics endpoint. Learn more: https://bit.ly/CRA-vitals\nreportWebVitals();\n"],"sourceRoot":""}

static/static/js/main.db88b5b1.chunk.js

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React, { useState, useRef } from 'react';\nimport { \n  Paper, \n  Typography, \n  Box, \n  Button, \n  IconButton \n} from '@material-ui/core';\nimport CloudUploadIcon from '@material-ui/icons/CloudUpload';\nimport DeleteIcon from '@material-ui/icons/Delete';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  paper: {\n    padding: theme.spacing(2),\n    display: 'flex',\n    flexDirection: 'column',\n    alignItems: 'center',\n    height: '100%',\n    minHeight: 300,\n    transition: 'all 0.3s ease'\n  },\n  dragActive: {\n    border: '2px dashed #3f51b5',\n    backgroundColor: 'rgba(63, 81, 181, 0.05)'\n  },\n  dragInactive: {\n    border: '2px dashed #ccc',\n    backgroundColor: 'white'\n  },\n  uploadBox: {\n    display: 'flex',\n    flexDirection: 'column',\n    alignItems: 'center',\n    justifyContent: 'center',\n    height: '100%',\n    width: '100%',\n    cursor: 'pointer'\n  },\n  uploadIcon: {\n    fontSize: 60,\n    color: '#3f51b5',\n    marginBottom: theme.spacing(2)\n  },\n  supportText: {\n    marginTop: theme.spacing(2)\n  },\n  previewBox: {\n    display: 'flex',\n    flexDirection: 'column',\n    alignItems: 'center',\n    width: '100%',\n    height: '100%',\n    position: 'relative'\n  },\n  imageContainer: {\n    position: 'relative',\n    width: '100%',\n    height: '100%',\n    display: 'flex',\n    justifyContent: 'center',\n    alignItems: 'center',\n    overflow: 'hidden',\n    marginTop: theme.spacing(2)\n  },\n  deleteButton: {\n    position: 'absolute',\n    top: 0,\n    right: 0,\n    backgroundColor: 'rgba(255, 255, 255, 0.7)',\n    '&:hover': {\n      backgroundColor: 'rgba(255, 255, 255, 0.9)',\n    }\n  }\n}));\n\nconst ImageUploader = ({ onImageUpload }) => {\n  const classes = useStyles();\n  const [previewUrl, setPreviewUrl] = useState(null);\n  const [dragActive, setDragActive] = useState(false);\n  const fileInputRef = useRef(null);\n\n  const handleDrag = (e) => {\n    e.preventDefault();\n    e.stopPropagation();\n    if (e.type === 'dragenter' || e.type === 'dragover') {\n      setDragActive(true);\n    } else if (e.type === 'dragleave') {\n      setDragActive(false);\n    }\n  };\n\n  const handleDrop = (e) => {\n    e.preventDefault();\n    e.stopPropagation();\n    setDragActive(false);\n    if (e.dataTransfer.files && e.dataTransfer.files[0]) {\n      handleFiles(e.dataTransfer.files[0]);\n    }\n  };\n\n  const handleChange = (e) => {\n    e.preventDefault();\n    if (e.target.files && e.target.files[0]) {\n      handleFiles(e.target.files[0]);\n    }\n  };\n\n  const handleFiles = (file) => {\n    if (file.type.startsWith('image/')) {\n      setPreviewUrl(URL.createObjectURL(file));\n      onImageUpload(file);\n    } else {\n      alert('Please upload an image file');\n    }\n  };\n\n  const onButtonClick = () => {\n    fileInputRef.current.click();\n  };\n\n  const handleRemoveImage = () => {\n    setPreviewUrl(null);\n    onImageUpload(null);\n    fileInputRef.current.value = \"\";\n  };\n\n  return (\n    <Paper \n      className={`${classes.paper} ${dragActive ? classes.dragActive : classes.dragInactive}`}\n      onDragEnter={handleDrag}\n      onDragLeave={handleDrag}\n      onDragOver={handleDrag}\n      onDrop={handleDrop}\n    >\n      <input\n        ref={fileInputRef}\n        type=\"file\"\n        accept=\"image/*\"\n        onChange={handleChange}\n        style={{ display: 'none' }}\n      />\n\n      {!previewUrl ? (\n        <Box \n          className={classes.uploadBox}\n          onClick={onButtonClick}\n        >\n          <CloudUploadIcon className={classes.uploadIcon} />\n          <Typography variant=\"h6\" gutterBottom>\n            Drag & Drop an image here\n          </Typography>\n          <Typography variant=\"body2\" color=\"textSecondary\" gutterBottom>\n            or\n          </Typography>\n          <Button\n            variant=\"contained\"\n            color=\"primary\"\n            component=\"span\"\n            startIcon={<CloudUploadIcon />}\n          >\n            Browse Files\n          </Button>\n          <Typography variant=\"body2\" color=\"textSecondary\" className={classes.supportText}>\n            Supported formats: JPG, PNG, GIF\n          </Typography>\n        </Box>\n      ) : (\n        <Box className={classes.previewBox}>\n          <Typography variant=\"h6\" gutterBottom>\n            Preview\n          </Typography>\n          <Box className={classes.imageContainer}>\n            <img\n              src={previewUrl}\n              alt=\"Preview\"\n              className=\"preview-image\"\n            />\n            <IconButton\n              aria-label=\"delete\"\n              className={classes.deleteButton}\n              onClick={handleRemoveImage}\n            >\n              <DeleteIcon />\n            </IconButton>\n          </Box>\n        </Box>\n      )}\n    </Paper>\n  );\n};\n\nexport default ImageUploader;\n","import React from 'react';\nimport { \n  Grid, \n  Card, \n  CardContent, \n  CardActions, \n  Typography, \n  Button, \n  Chip,\n  Box\n} from '@material-ui/core';\nimport VisibilityIcon from '@material-ui/icons/Visibility';\nimport CategoryIcon from '@material-ui/icons/Category';\nimport PlayArrowIcon from '@material-ui/icons/PlayArrow';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  card: {\n    height: '100%',\n    display: 'flex',\n    flexDirection: 'column',\n  },\n  selectedCard: {\n    border: '2px solid #3f51b5',\n  },\n  unavailableCard: {\n    opacity: 0.6,\n  },\n  cardContent: {\n    flexGrow: 1,\n  },\n  chipContainer: {\n    marginBottom: theme.spacing(1.5),\n  },\n  successChip: {\n    backgroundColor: '#34C759',\n    color: '#fff',\n  },\n  errorChip: {\n    backgroundColor: '#FF3B3F',\n    color: '#fff',\n  },\n  modelType: {\n    marginTop: theme.spacing(1),\n  },\n  processButton: {\n    marginTop: theme.spacing(3),\n    textAlign: 'center',\n  }\n}));\n\nconst ModelSelector = ({ \n  onModelSelect, \n  onProcess, \n  isProcessing, \n  modelsStatus, \n  selectedModel,\n  imageSelected \n}) => {\n  const classes = useStyles();\n  \n  const models = [\n    {\n      id: 'yolo',\n      name: 'YOLOv8',\n      description: 'Fast and accurate object detection',\n      icon: <VisibilityIcon />,\n      available: modelsStatus.yolo\n    },\n    {\n      id: 'detr',\n      name: 'DETR',\n      description: 'DEtection TRansformer for object detection',\n      icon: <VisibilityIcon />,\n      available: modelsStatus.detr\n    },\n    {\n      id: 'vit',\n      name: 'ViT',\n      description: 'Vision Transformer for image classification',\n      icon: <CategoryIcon />,\n      available: modelsStatus.vit\n    }\n  ];\n\n  const handleModelClick = (modelId) => {\n    if (models.find(m => m.id === modelId).available) {\n      onModelSelect(modelId);\n    }\n  };\n\n  return (\n    <Box sx={{ p: 2, height: '100%' }}>\n      <Typography variant=\"h6\" gutterBottom>\n        Select Model\n      </Typography>\n      \n      <Grid container spacing={2}>\n        {models.map((model) => (\n          <Grid item xs={12} sm={4} key={model.id}>\n            <Card \n              className={`\n                ${classes.card} \n                ${selectedModel === model.id ? classes.selectedCard : ''} \n                ${!model.available ? classes.unavailableCard : ''}\n              `}\n              onClick={() => handleModelClick(model.id)}\n            >\n              <CardContent className={classes.cardContent}>\n                <Box sx={{ mb: 2, color: 'primary' }}>\n                  {model.icon}\n                </Box>\n                <Typography variant=\"h5\" component=\"div\" gutterBottom>\n                  {model.name}\n                </Typography>\n                <div className={classes.chipContainer}>\n                  {model.available ? (\n                    <Chip \n                      label=\"Available\" \n                      className={classes.successChip}\n                      size=\"small\" \n                    />\n                  ) : (\n                    <Chip \n                      label=\"Not Available\" \n                      className={classes.errorChip}\n                      size=\"small\" \n                    />\n                  )}\n                </div>\n                <Typography variant=\"body2\" color=\"textSecondary\">\n                  {model.description}\n                </Typography>\n              </CardContent>\n              <CardActions>\n                <Button \n                  size=\"small\" \n                  onClick={() => handleModelClick(model.id)}\n                  disabled={!model.available}\n                  color={selectedModel === model.id ? \"primary\" : \"default\"}\n                  variant={selectedModel === model.id ? \"contained\" : \"outlined\"}\n                  fullWidth\n                >\n                  {selectedModel === model.id ? 'Selected' : 'Select'}\n                </Button>\n              </CardActions>\n            </Card>\n          </Grid>\n        ))}\n      </Grid>\n\n      <div className={classes.processButton}>\n        <Button\n          variant=\"contained\"\n          color=\"primary\"\n          size=\"large\"\n          startIcon={<PlayArrowIcon />}\n          onClick={onProcess}\n          disabled={!selectedModel || !imageSelected || isProcessing}\n        >\n          {isProcessing ? 'Processing...' : 'Process Image'}\n        </Button>\n      </div>\n    </Box>\n  );\n};\n\nexport default ModelSelector;\n","import React, { useState } from 'react';\nimport { \n  Button, \n  Box, \n  Typography, \n  CircularProgress, \n  Snackbar,\n  Dialog,\n  DialogTitle,\n  DialogContent,\n  DialogActions,\n  TextField,\n  FormControl,\n  InputLabel,\n  Select,\n  MenuItem,\n  Grid,\n  Card,\n  CardMedia,\n  CardContent,\n  Chip\n} from '@material-ui/core';\nimport { Alert } from '@material-ui/lab';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  root: {\n    marginTop: theme.spacing(2),\n    marginBottom: theme.spacing(2),\n    padding: theme.spacing(2),\n    backgroundColor: '#f5f5f5',\n    borderRadius: theme.shape.borderRadius,\n  },\n  button: {\n    marginRight: theme.spacing(2),\n  },\n  searchDialog: {\n    minWidth: '500px',\n  },\n  formControl: {\n    marginBottom: theme.spacing(2),\n    minWidth: '100%',\n  },\n  searchResults: {\n    marginTop: theme.spacing(2),\n  },\n  resultCard: {\n    marginBottom: theme.spacing(2),\n  },\n  resultImage: {\n    height: 140,\n    objectFit: 'contain',\n  },\n  chip: {\n    margin: theme.spacing(0.5),\n  },\n  similarityChip: {\n    backgroundColor: theme.palette.primary.main,\n    color: 'white',\n  }\n}));\n\nconst VectorDBActions = ({ results }) => {\n  const classes = useStyles();\n  const [isSaving, setIsSaving] = useState(false);\n  const [saveSuccess, setSaveSuccess] = useState(false);\n  const [saveError, setSaveError] = useState(null);\n  const [openSearchDialog, setOpenSearchDialog] = useState(false);\n  const [searchType, setSearchType] = useState('image');\n  const [searchClass, setSearchClass] = useState('');\n  const [searchResults, setSearchResults] = useState([]);\n  const [isSearching, setIsSearching] = useState(false);\n  const [searchError, setSearchError] = useState(null);\n  \n  // Extract model and data from results\n  const { model, data } = results;\n  \n  // Handle saving to vector DB\n  const handleSaveToVectorDB = async () => {\n    setIsSaving(true);\n    setSaveError(null);\n    \n    try {\n      let response;\n      \n      if (model === 'vit') {\n        // For ViT, save the whole image with classifications\n        response = await fetch('/api/add-to-collection', {\n          method: 'POST',\n          headers: {\n            'Content-Type': 'application/json',\n          },\n          body: JSON.stringify({\n            image: data.image,\n            metadata: {\n              model: 'vit',\n              classifications: data.classifications\n            }\n          })\n        });\n      } else {\n        // For YOLO and DETR, save detected objects\n        response = await fetch('/api/add-detected-objects', {\n          method: 'POST',\n          headers: {\n            'Content-Type': 'application/json',\n          },\n          body: JSON.stringify({\n            image: data.image,\n            objects: data.detections,\n            imageId: generateUUID()\n          })\n        });\n      }\n      \n      if (!response.ok) {\n        throw new Error(`HTTP error! Status: ${response.status}`);\n      }\n      \n      const result = await response.json();\n      \n      if (result.error) {\n        throw new Error(result.error);\n      }\n      \n      setSaveSuccess(true);\n      setTimeout(() => setSaveSuccess(false), 5000);\n    } catch (err) {\n      console.error('Error saving to vector DB:', err);\n      setSaveError(`Error saving to vector DB: ${err.message}`);\n    } finally {\n      setIsSaving(false);\n    }\n  };\n  \n  // Handle opening search dialog\n  const handleOpenSearchDialog = () => {\n    setOpenSearchDialog(true);\n    setSearchResults([]);\n    setSearchError(null);\n  };\n  \n  // Handle closing search dialog\n  const handleCloseSearchDialog = () => {\n    setOpenSearchDialog(false);\n  };\n  \n  // Handle search type change\n  const handleSearchTypeChange = (event) => {\n    setSearchType(event.target.value);\n    setSearchResults([]);\n    setSearchError(null);\n  };\n  \n  // Handle search class change\n  const handleSearchClassChange = (event) => {\n    setSearchClass(event.target.value);\n  };\n  \n  // Handle search\n  const handleSearch = async () => {\n    setIsSearching(true);\n    setSearchError(null);\n    \n    try {\n      let requestBody = {};\n      \n      if (searchType === 'image') {\n        // Search by current image\n        requestBody = {\n          searchType: 'image',\n          image: data.image,\n          n_results: 5\n        };\n      } else {\n        // Search by class name\n        if (!searchClass.trim()) {\n          throw new Error('Please enter a class name');\n        }\n        \n        requestBody = {\n          searchType: 'class',\n          class_name: searchClass.trim(),\n          n_results: 5\n        };\n      }\n      \n      const response = await fetch('/api/search-similar-objects', {\n        method: 'POST',\n        headers: {\n          'Content-Type': 'application/json',\n        },\n        body: JSON.stringify(requestBody)\n      });\n      \n      if (!response.ok) {\n        throw new Error(`HTTP error! Status: ${response.status}`);\n      }\n      \n      const result = await response.json();\n      \n      if (result.error) {\n        throw new Error(result.error);\n      }\n      \n      console.log('Search API response:', result);\n      \n      // The backend responds with {success, searchType, results} structure, so extract only the results array\n      if (result.success && Array.isArray(result.results)) {\n        console.log('Setting search results array:', result.results);\n        console.log('Results array length:', result.results.length);\n        console.log('First result item:', result.results[0]);\n        setSearchResults(result.results);\n      } else {\n        console.error('Unexpected API response format:', result);\n        throw new Error('Unexpected API response format');\n      }\n    } catch (err) {\n      console.error('Error searching vector DB:', err);\n      setSearchError(`Error searching vector DB: ${err.message}`);\n    } finally {\n      setIsSearching(false);\n    }\n  };\n  \n  // Generate UUID for image ID\n  const generateUUID = () => {\n    return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, function(c) {\n      const r = Math.random() * 16 | 0;\n      const v = c === 'x' ? r : (r & 0x3 | 0x8);\n      return v.toString(16);\n    });\n  };\n  \n  // Render search results\n  const renderSearchResults = () => {\n    console.log('Rendering search results:', searchResults);\n    console.log('Search results length:', searchResults.length);\n    \n    if (searchResults.length === 0) {\n      console.log('No results to render');\n      return (\n        <Typography variant=\"body1\">No results found.</Typography>\n      );\n    }\n    \n    return (\n      <Grid container spacing={2}>\n        {searchResults.map((result, index) => {\n          const similarity = (1 - result.distance) * 100;\n          \n          return (\n            <Grid item xs={12} sm={6} key={index}>\n              <Card className={classes.resultCard}>\n                {result.metadata && result.metadata.image_data ? (\n                  <CardMedia\n                    className={classes.resultImage}\n                    component=\"img\"\n                    height=\"200\"\n                    image={`data:image/jpeg;base64,${result.metadata.image_data}`}\n                    alt={result.metadata && result.metadata.class ? result.metadata.class : 'Object'}\n                  />\n                ) : (\n                  <Box \n                    className={classes.resultImage}\n                    style={{ \n                      backgroundColor: '#f0f0f0', \n                      display: 'flex', \n                      alignItems: 'center', \n                      justifyContent: 'center',\n                      height: 200\n                    }}\n                  >\n                    <Typography variant=\"body2\" color=\"textSecondary\">\n                      {result.metadata && result.metadata.class ? result.metadata.class : 'Object'} Image\n                    </Typography>\n                  </Box>\n                )}\n                <CardContent>\n                  <Box display=\"flex\" justifyContent=\"space-between\" alignItems=\"center\" mb={1}>\n                    <Typography variant=\"subtitle1\">Result #{index + 1}</Typography>\n                    <Chip \n                      label={`Similarity: ${similarity.toFixed(2)}%`}\n                      className={classes.similarityChip}\n                      size=\"small\"\n                    />\n                  </Box>\n                  <Typography variant=\"body2\" color=\"textSecondary\">\n                    <strong>Class:</strong> {result.metadata.class || 'N/A'}\n                  </Typography>\n                  {result.metadata.confidence && (\n                    <Typography variant=\"body2\" color=\"textSecondary\">\n                      <strong>Confidence:</strong> {(result.metadata.confidence * 100).toFixed(2)}%\n                    </Typography>\n                  )}\n                  <Typography variant=\"body2\" color=\"textSecondary\">\n                    <strong>Object ID:</strong> {result.id}\n                  </Typography>\n                </CardContent>\n              </Card>\n            </Grid>\n          );\n        })}\n      </Grid>\n    );\n  };\n  \n  return (\n    <Box className={classes.root}>\n      <Typography variant=\"h6\" gutterBottom>\n        Vector Database Actions\n      </Typography>\n      \n      <Box display=\"flex\" alignItems=\"center\" mb={2}>\n        <Button\n          variant=\"contained\"\n          color=\"primary\"\n          onClick={handleSaveToVectorDB}\n          disabled={isSaving}\n          className={classes.button}\n        >\n          {isSaving ? (\n            <>\n              <CircularProgress size={20} color=\"inherit\" style={{ marginRight: 8 }} />\n              Saving...\n            </>\n          ) : (\n            'Save to Vector DB'\n          )}\n        </Button>\n        \n        <Button\n          variant=\"outlined\"\n          color=\"primary\"\n          onClick={handleOpenSearchDialog}\n          className={classes.button}\n        >\n          Search Similar\n        </Button>\n      </Box>\n      \n      {saveError && (\n        <Alert severity=\"error\" style={{ marginTop: 8 }}>\n          {saveError}\n        </Alert>\n      )}\n      \n      <Snackbar open={saveSuccess} autoHideDuration={5000} onClose={() => setSaveSuccess(false)}>\n        <Alert severity=\"success\">\n          {model === 'vit' ? (\n            'Image and classifications successfully saved to vector DB!'\n          ) : (\n            'Detected objects successfully saved to vector DB!'\n          )}\n        </Alert>\n      </Snackbar>\n      \n      {/* Search Dialog */}\n      <Dialog\n        open={openSearchDialog}\n        onClose={handleCloseSearchDialog}\n        maxWidth=\"md\"\n        fullWidth\n      >\n        <DialogTitle>Search Vector Database</DialogTitle>\n        <DialogContent>\n          <FormControl className={classes.formControl}>\n            <InputLabel id=\"search-type-label\">Search Type</InputLabel>\n            <Select\n              labelId=\"search-type-label\"\n              id=\"search-type\"\n              value={searchType}\n              onChange={handleSearchTypeChange}\n            >\n              <MenuItem value=\"image\">Search by Current Image</MenuItem>\n              <MenuItem value=\"class\">Search by Class Name</MenuItem>\n            </Select>\n          </FormControl>\n          \n          {searchType === 'class' && (\n            <FormControl className={classes.formControl}>\n              <TextField\n                label=\"Class Name\"\n                value={searchClass}\n                onChange={handleSearchClassChange}\n                placeholder=\"e.g. person, car, dog...\"\n                fullWidth\n              />\n            </FormControl>\n          )}\n          \n          {searchError && (\n            <Alert severity=\"error\" style={{ marginBottom: 16 }}>\n              {searchError}\n            </Alert>\n          )}\n          \n          <Box className={classes.searchResults}>\n            {isSearching ? (\n              <Box display=\"flex\" justifyContent=\"center\" alignItems=\"center\" p={4}>\n                <CircularProgress />\n                <Typography variant=\"body1\" style={{ marginLeft: 16 }}>\n                  Searching...\n                </Typography>\n              </Box>\n            ) : (\n              <>\n                {console.log('Search dialog render - searchResults:', searchResults)}\n                {searchResults.length > 0 ? renderSearchResults() : \n                  <Typography variant=\"body1\">No results found. Please try another search.</Typography>\n                }\n              </>\n            )}\n          </Box>\n        </DialogContent>\n        <DialogActions>\n          <Button onClick={handleCloseSearchDialog} color=\"default\">\n            Close\n          </Button>\n          <Button \n            onClick={handleSearch} \n            color=\"primary\" \n            variant=\"contained\"\n            disabled={isSearching || (searchType === 'class' && !searchClass.trim())}\n          >\n            Search\n          </Button>\n        </DialogActions>\n      </Dialog>\n    </Box>\n  );\n};\n\nexport default VectorDBActions;\n","import React from 'react';\nimport { \n  Paper, \n  Typography, \n  Box, \n  List, \n  ListItem, \n  ListItemText, \n  Divider,\n  Grid,\n  Chip\n} from '@material-ui/core';\nimport VectorDBActions from './VectorDBActions';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  paper: {\n    padding: theme.spacing(2)\n  },\n  marginBottom: {\n    marginBottom: theme.spacing(2)\n  },\n  resultImage: {\n    maxWidth: '100%',\n    maxHeight: '400px',\n    objectFit: 'contain'\n  },\n  dividerMargin: {\n    margin: `${theme.spacing(2)}px 0`\n  },\n  chipContainer: {\n    display: 'flex',\n    gap: theme.spacing(1),\n    flexWrap: 'wrap'\n  }\n}));\n\nconst ResultDisplay = ({ results }) => {\n  const classes = useStyles();\n  if (!results) return null;\n  \n  const { model, data } = results;\n  \n  // Helper to format times nicely\n  const formatTime = (ms) => {\n    if (ms === undefined || ms === null || isNaN(ms)) return '-';\n    const num = Number(ms);\n    if (num < 1000) return `${num.toFixed(2)} ms`;\n    return `${(num / 1000).toFixed(2)} s`;\n  };\n  \n  // Check if there's an error\n  if (data.error) {\n    return (\n      <Paper sx={{ p: 2, bgcolor: '#ffebee' }}>\n        <Typography color=\"error\">{data.error}</Typography>\n      </Paper>\n    );\n  }\n\n  // Display performance info\n  const renderPerformanceInfo = () => {\n    if (!data.performance) return null;\n    \n    return (\n      <Box className=\"performance-info\">\n        <Divider className={classes.dividerMargin} />\n        <Typography variant=\"body2\">\n          Inference time: {formatTime(data.performance.inference_time)} on {data.performance.device}\n        </Typography>\n      </Box>\n    );\n  };\n\n  // Render for YOLO and DETR (object detection)\n  if (model === 'yolo' || model === 'detr') {\n    return (\n      <Paper className={classes.paper}>\n        <Typography variant=\"h6\" gutterBottom>\n          {model === 'yolo' ? 'YOLOv8' : 'DETR'} Detection Results\n        </Typography>\n        \n        <Grid container spacing={3}>\n          <Grid item xs={12} md={6}>\n            {data.image && (\n              <Box className={classes.marginBottom}>\n                <Typography variant=\"subtitle1\" gutterBottom>\n                  Detection Result\n                </Typography>\n                <img \n                  src={`data:image/png;base64,${data.image}`} \n                  alt=\"Detection Result\" \n                  className={classes.resultImage}\n                />\n              </Box>\n            )}\n          </Grid>\n          \n          <Grid item xs={12} md={6}>\n            <Box className={classes.marginBottom}>\n              <Typography variant=\"subtitle1\" gutterBottom>\n                Detected Objects:\n              </Typography>\n              \n              {data.detections && data.detections.length > 0 ? (\n                <List>\n                  {data.detections.map((detection, index) => (\n                    <React.Fragment key={index}>\n                      <ListItem>\n                        <ListItemText \n                          primary={\n                            <Box style={{ display: 'flex', alignItems: 'center' }}>\n                              <Typography variant=\"body1\" component=\"span\">\n                                {detection.class}\n                              </Typography>\n                              <Chip \n                                label={`${(detection.confidence * 100).toFixed(0)}%`}\n                                size=\"small\"\n                                color=\"primary\"\n                                style={{ marginLeft: 8 }}\n                              />\n                            </Box>\n                          } \n                          secondary={`Bounding Box: [${detection.bbox.join(', ')}]`} \n                        />\n                      </ListItem>\n                      {index < data.detections.length - 1 && <Divider />}\n                    </React.Fragment>\n                  ))}\n                </List>\n              ) : (\n                <Typography variant=\"body1\">No objects detected</Typography>\n              )}\n            </Box>\n          </Grid>\n        </Grid>\n        \n        {renderPerformanceInfo()}\n        \n        {/* Vector DB Actions for Object Detection */}\n        <VectorDBActions results={results} />\n      </Paper>\n    );\n  }\n  \n  // Render for ViT (classification)\n  if (model === 'vit') {\n    return (\n      <Paper className={classes.paper}>\n        <Typography variant=\"h6\" gutterBottom>\n          ViT Classification Results\n        </Typography>\n        \n        <Typography variant=\"subtitle1\" gutterBottom>\n          Top Predictions:\n        </Typography>\n        \n        {data.top_predictions && data.top_predictions.length > 0 ? (\n          <List>\n            {data.top_predictions.map((prediction, index) => (\n              <React.Fragment key={index}>\n                <ListItem>\n                  <ListItemText \n                    primary={\n                      <Box style={{ display: 'flex', alignItems: 'center' }}>\n                        <Typography variant=\"body1\" component=\"span\">\n                          {prediction.rank}. {prediction.class}\n                        </Typography>\n                        <Chip \n                          label={`${(prediction.probability * 100).toFixed(1)}%`}\n                          size=\"small\"\n                          color={index === 0 ? \"primary\" : \"default\"}\n                          style={{ marginLeft: 8 }}\n                        />\n                      </Box>\n                    } \n                  />\n                </ListItem>\n                {index < data.top_predictions.length - 1 && <Divider />}\n              </React.Fragment>\n            ))}\n          </List>\n        ) : (\n          <Typography variant=\"body1\">No classifications available</Typography>\n        )}\n        \n        {renderPerformanceInfo()}\n        \n        {/* Vector DB Actions for ViT Classification */}\n        <VectorDBActions results={results} />\n      </Paper>\n    );\n  }\n  \n  return null;\n};\n\nexport default ResultDisplay;\n","import React, { useState } from 'react';\nimport { \n  Paper, \n  Typography, \n  Box, \n  TextField, \n  Button, \n  CircularProgress,\n  Divider\n} from '@material-ui/core';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  paper: {\n    padding: theme.spacing(2),\n    marginTop: theme.spacing(2)\n  },\n  marginBottom: {\n    marginBottom: theme.spacing(2)\n  },\n  dividerMargin: {\n    margin: `${theme.spacing(2)}px 0`\n  },\n  responseBox: {\n    padding: theme.spacing(2),\n    backgroundColor: '#f5f5f5',\n    borderRadius: theme.shape.borderRadius,\n    marginTop: theme.spacing(2),\n    whiteSpace: 'pre-wrap'\n  },\n  buttonProgress: {\n    marginLeft: theme.spacing(1)\n  }\n}));\n\nconst LlmAnalysis = ({ visionResults, model }) => {\n  const classes = useStyles();\n  const [userQuery, setUserQuery] = useState('');\n  const [isAnalyzing, setIsAnalyzing] = useState(false);\n  const [analysisResult, setAnalysisResult] = useState(null);\n  const [error, setError] = useState(null);\n\n  // Format time for display\n  const formatTime = (ms) => {\n    if (ms === undefined || ms === null || isNaN(ms)) return '-';\n    const num = Number(ms);\n    if (num < 1000) return `${num.toFixed(2)} ms`;\n    return `${(num / 1000).toFixed(2)} s`;\n  };\n\n  const handleAnalyze = async () => {\n    if (!userQuery.trim()) return;\n    \n    setIsAnalyzing(true);\n    setError(null);\n    \n    try {\n      const response = await fetch('/api/analyze', {\n        method: 'POST',\n        headers: {\n          'Content-Type': 'application/json',\n        },\n        body: JSON.stringify({\n          visionResults: visionResults,\n          userQuery: userQuery\n        }),\n      });\n\n      if (!response.ok) {\n        throw new Error(`HTTP error! Status: ${response.status}`);\n      }\n\n      const data = await response.json();\n      \n      if (data.error) {\n        setError(data.error);\n      } else {\n        setAnalysisResult(data);\n      }\n    } catch (err) {\n      console.error('Error analyzing with LLM:', err);\n      setError(`Error analyzing with LLM: ${err.message}`);\n    } finally {\n      setIsAnalyzing(false);\n    }\n  };\n\n  if (!visionResults) return null;\n\n  return (\n    <Paper className={classes.paper}>\n      <Typography variant=\"h6\" gutterBottom>\n        Ask AI about the {model === 'vit' ? 'Classification' : 'Detection'} Results\n      </Typography>\n      \n      <Typography variant=\"body2\" className={classes.marginBottom}>\n        Ask a question about the detected objects or classifications to get an AI-powered analysis.\n      </Typography>\n      \n      <TextField\n        fullWidth\n        label=\"Your question about the image\"\n        variant=\"outlined\"\n        value={userQuery}\n        onChange={(e) => setUserQuery(e.target.value)}\n        disabled={isAnalyzing}\n        className={classes.marginBottom}\n        placeholder={model === 'vit' \n          ? \"E.g., What category does this image belong to?\" \n          : \"E.g., How many people are in this image?\"}\n      />\n      \n      <Button \n        variant=\"contained\" \n        color=\"primary\"\n        onClick={handleAnalyze}\n        disabled={isAnalyzing || !userQuery.trim()}\n      >\n        Analyze with AI\n        {isAnalyzing && <CircularProgress size={24} className={classes.buttonProgress} />}\n      </Button>\n      \n      {error && (\n        <Box mt={2}>\n          <Typography color=\"error\">{error}</Typography>\n        </Box>\n      )}\n      \n      {analysisResult && (\n        <>\n          <Divider className={classes.dividerMargin} />\n          \n          <Typography variant=\"subtitle1\" gutterBottom>\n            AI Analysis:\n          </Typography>\n          \n          <Box className={classes.responseBox}>\n            <Typography variant=\"body1\">\n              {analysisResult.response}\n            </Typography>\n          </Box>\n          \n          {analysisResult.performance && (\n            <Box mt={1}>\n              <Typography variant=\"body2\" color=\"textSecondary\">\n                Analysis time: {formatTime(analysisResult.performance.inference_time)} on {analysisResult.performance.device}\n              </Typography>\n            </Box>\n          )}\n        </>\n      )}\n    </Paper>\n  );\n};\n\nexport default LlmAnalysis;\n","import React, { useState } from 'react';\nimport { \n  Paper,\n  Typography,\n  Grid,\n  TextField,\n  Button,\n  Divider\n} from '@material-ui/core';\n\nfunction OpenAIChat() {\n  const [model, setModel] = useState('gpt-4o-mini');\n  const [apiKey, setApiKey] = useState('');\n  const [system, setSystem] = useState('You are a helpful assistant.');\n  const [prompt, setPrompt] = useState('');\n  const [response, setResponse] = useState('');\n  const [loading, setLoading] = useState(false);\n  const [error, setError] = useState('');\n\n  const onSend = async () => {\n    setError('');\n    setResponse('');\n    const p = (prompt || '').trim();\n    if (!p) { setError('Please enter a question.'); return; }\n\n    setLoading(true);\n    try {\n      const res = await fetch('/api/openai/chat', {\n        method: 'POST',\n        headers: { 'Content-Type': 'application/json' },\n        credentials: 'include',\n        body: JSON.stringify({\n          prompt: p,\n          model: (model || '').trim() || 'gpt-4o-mini',\n          api_key: (apiKey || undefined),\n          system: (system || undefined)\n        })\n      });\n\n      if (!res.ok) {\n        let txt = await res.text();\n        try { txt = JSON.stringify(JSON.parse(txt), null, 2); } catch {}\n        throw new Error(txt);\n      }\n      const data = await res.json();\n      const meta = `Model: ${data.model} | Latency: ${data.latency_sec}s` + (data.usage ? ` | Usage: ${JSON.stringify(data.usage)}` : '');\n      setResponse((data.response || '(Empty response)') + '\\n\\n---\\n' + meta);\n    } catch (e) {\n      setError('Error: ' + e.message);\n    } finally {\n      setLoading(false);\n    }\n  };\n\n  const onClear = () => {\n    setPrompt('');\n    setResponse('');\n    setError('');\n  };\n\n  return (\n    <Paper style={{ padding: 16 }}>\n      <Typography variant=\"h5\" gutterBottom>\n        OpenAI Chat (OpenAI API)\n      </Typography>\n      <Typography variant=\"body2\" color=\"textSecondary\" gutterBottom>\n        If the server env var OPENAI_API_KEY is set, the API Key field is optional.\n      </Typography>\n      <Grid container spacing={2}>\n        <Grid item xs={12} md={6}>\n          <TextField\n            label=\"Model\"\n            value={model}\n            onChange={(e) => setModel(e.target.value)}\n            fullWidth\n            variant=\"outlined\"\n            size=\"small\"\n          />\n        </Grid>\n        <Grid item xs={12} md={6}>\n          <TextField\n            label=\"OpenAI API Key (optional)\"\n            value={apiKey}\n            onChange={(e) => setApiKey(e.target.value)}\n            fullWidth\n            variant=\"outlined\"\n            size=\"small\"\n            type=\"password\"\n            placeholder=\"sk-...\"\n          />\n        </Grid>\n        <Grid item xs={12}>\n          <TextField\n            label=\"System Prompt (optional)\"\n            value={system}\n            onChange={(e) => setSystem(e.target.value)}\n            fullWidth\n            variant=\"outlined\"\n            size=\"small\"\n          />\n        </Grid>\n        <Grid item xs={12}>\n          <TextField\n            label=\"User Question\"\n            value={prompt}\n            onChange={(e) => setPrompt(e.target.value)}\n            fullWidth\n            multiline\n            rows={4}\n            variant=\"outlined\"\n          />\n        </Grid>\n        {error && (\n          <Grid item xs={12}>\n            <Typography color=\"error\">{error}</Typography>\n          </Grid>\n        )}\n        <Grid item xs={12}>\n          <div style={{ display: 'flex', gap: 8 }}>\n            <Button color=\"primary\" variant=\"contained\" onClick={onSend} disabled={loading}>\n              {loading ? 'Sending...' : 'Send Question'}\n            </Button>\n            <Button variant=\"outlined\" onClick={onClear}>Clear</Button>\n          </div>\n        </Grid>\n        <Grid item xs={12}>\n          <Divider style={{ margin: '12px 0' }} />\n          <Typography variant=\"subtitle2\" color=\"textSecondary\">Response</Typography>\n          <pre style={{ whiteSpace: 'pre-wrap', fontFamily: 'ui-monospace, monospace' }}>{response}</pre>\n        </Grid>\n      </Grid>\n    </Paper>\n  );\n}\n\nexport default OpenAIChat;\n","import React, { useState, useEffect } from 'react';\nimport { \n  Container, \n  Typography, \n  Box, \n  Paper, \n  Grid, \n  CircularProgress,\n  AppBar,\n  Toolbar,\n  ThemeProvider,\n  createMuiTheme\n} from '@material-ui/core';\nimport ImageUploader from './components/ImageUploader';\nimport ModelSelector from './components/ModelSelector';\nimport ResultDisplay from './components/ResultDisplay';\nimport LlmAnalysis from './components/LlmAnalysis';\nimport OpenAIChat from './components/OpenAIChat';\nimport './App.css';\n\n// Create a theme\nconst theme = createMuiTheme({\n  palette: {\n    primary: {\n      main: '#3f51b5',\n    },\n    secondary: {\n      main: '#f50057',\n    },\n  },\n  typography: {\n    fontFamily: 'Roboto, Arial, sans-serif',\n  },\n});\n\nfunction App() {\n  const [selectedImage, setSelectedImage] = useState(null);\n  const [selectedModel, setSelectedModel] = useState('');\n  const [isProcessing, setIsProcessing] = useState(false);\n  const [results, setResults] = useState(null);\n  const [error, setError] = useState(null);\n  const [modelsStatus, setModelsStatus] = useState({\n    yolo: false,\n    detr: false,\n    vit: false\n  });\n\n  // Check API status on component mount\n  useEffect(() => {\n    fetch('/api/status')\n      .then(response => response.json())\n      .then(data => {\n        setModelsStatus(data.models);\n      })\n      .catch(err => {\n        console.error('Error checking API status:', err);\n        setError('Error connecting to the backend API. Please make sure the server is running.');\n      });\n  }, []);\n\n  const handleImageUpload = (image) => {\n    setSelectedImage(image);\n    setResults(null);\n    setError(null);\n  };\n\n  const handleModelSelect = (model) => {\n    setSelectedModel(model);\n    setResults(null);\n    setError(null);\n  };\n\n  const processImage = async () => {\n    if (!selectedImage || !selectedModel) {\n      setError('Please select both an image and a model');\n      return;\n    }\n\n    setIsProcessing(true);\n    setError(null);\n\n    // Create form data for the image\n    const formData = new FormData();\n    formData.append('image', selectedImage);\n\n    let endpoint = '';\n    switch (selectedModel) {\n      case 'yolo':\n        endpoint = '/api/detect/yolo';\n        break;\n      case 'detr':\n        endpoint = '/api/detect/detr';\n        break;\n      case 'vit':\n        endpoint = '/api/classify/vit';\n        break;\n      default:\n        setError('Invalid model selection');\n        setIsProcessing(false);\n        return;\n    }\n\n    try {\n      const response = await fetch(endpoint, {\n        method: 'POST',\n        body: formData,\n      });\n\n      if (!response.ok) {\n        throw new Error(`HTTP error! Status: ${response.status}`);\n      }\n\n      const data = await response.json();\n      setResults({ model: selectedModel, data });\n    } catch (err) {\n      console.error('Error processing image:', err);\n      setError(`Error processing image: ${err.message}`);\n    } finally {\n      setIsProcessing(false);\n    }\n  };\n\n  return (\n    <ThemeProvider theme={theme}>\n      <Box style={{ flexGrow: 1 }}>\n        <AppBar position=\"static\">\n          <Toolbar>\n            <Typography variant=\"h6\" style={{ flexGrow: 1 }}>\n              Multi-Model Object Detection Demo\n            </Typography>\n          </Toolbar>\n        </AppBar>\n        <Container maxWidth=\"lg\" style={{ marginTop: theme.spacing(4), marginBottom: theme.spacing(4) }}>\n          <Grid container spacing={3}>\n            <Grid item xs={12}>\n              <Paper style={{ padding: theme.spacing(2) }}>\n                <Typography variant=\"h5\" gutterBottom>\n                  Upload an image to see how each model performs!\n                </Typography>\n                <Typography variant=\"body1\" paragraph>\n                  This demo showcases three different object detection and image classification models:\n                </Typography>\n                <Typography variant=\"body1\" component=\"div\">\n                  <ul>\n                    <li><strong>YOLOv8</strong>: Fast and accurate object detection</li>\n                    <li><strong>DETR</strong>: DEtection TRansformer for object detection</li>\n                    <li><strong>ViT</strong>: Vision Transformer for image classification</li>\n                  </ul>\n                </Typography>\n              </Paper>\n            </Grid>\n            \n            <Grid item xs={12} md={6}>\n              <ImageUploader onImageUpload={handleImageUpload} />\n            </Grid>\n            \n            <Grid item xs={12} md={6}>\n              <ModelSelector \n                onModelSelect={handleModelSelect} \n                onProcess={processImage}\n                isProcessing={isProcessing}\n                modelsStatus={modelsStatus}\n                selectedModel={selectedModel}\n                imageSelected={!!selectedImage}\n              />\n            </Grid>\n            \n            {error && (\n              <Grid item xs={12}>\n                <Paper style={{ padding: theme.spacing(2), backgroundColor: '#ffebee' }}>\n                  <Typography color=\"error\">{error}</Typography>\n                </Paper>\n              </Grid>\n            )}\n            \n            {isProcessing && (\n              <Grid item xs={12} style={{ textAlign: 'center', margin: `${theme.spacing(4)}px 0` }}>\n                <CircularProgress />\n                <Typography variant=\"h6\" style={{ marginTop: theme.spacing(2) }}>\n                  Processing image...\n                </Typography>\n              </Grid>\n            )}\n            \n            {results && (\n              <>\n                <Grid item xs={12}>\n                  <ResultDisplay results={results} />\n                </Grid>\n                <Grid item xs={12}>\n                  <LlmAnalysis visionResults={results.data} model={results.model} />\n                </Grid>\n              </>\n            )}\n\n            {/* OpenAI Chat section at the end */}\n            <Grid item xs={12}>\n              <OpenAIChat />\n            </Grid>\n          </Grid>\n        </Container>\n      </Box>\n    </ThemeProvider>\n  );\n}\n\nexport default App;\n","const reportWebVitals = (onPerfEntry) => {\n  if (onPerfEntry && onPerfEntry instanceof Function) {\n    import('web-vitals').then(({ getCLS, getFID, getFCP, getLCP, getTTFB }) => {\n      getCLS(onPerfEntry);\n      getFID(onPerfEntry);\n      getFCP(onPerfEntry);\n      getLCP(onPerfEntry);\n      getTTFB(onPerfEntry);\n    });\n  }\n};\n\nexport default reportWebVitals;\n","import React from 'react';\nimport ReactDOM from 'react-dom';\nimport './index.css';\nimport App from './App';\nimport reportWebVitals from './reportWebVitals';\n\nReactDOM.render(\n  <React.StrictMode>\n    <App />\n  </React.StrictMode>,\n  document.getElementById('root')\n);\n\n// If you want to start measuring performance in your app, pass a function\n// to log results (for example: reportWebVitals(console.log))\n// or send to an analytics endpoint. Learn more: https://bit.ly/CRA-vitals\nreportWebVitals();\n"],"sourceRoot":""}

static/static/js/main.fb4939ae.chunk.js

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React, { useState, useRef } from 'react';\nimport { \n  Paper, \n  Typography, \n  Box, \n  Button, \n  IconButton \n} from '@material-ui/core';\nimport CloudUploadIcon from '@material-ui/icons/CloudUpload';\nimport DeleteIcon from '@material-ui/icons/Delete';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  paper: {\n    padding: theme.spacing(2),\n    display: 'flex',\n    flexDirection: 'column',\n    alignItems: 'center',\n    height: '100%',\n    minHeight: 300,\n    transition: 'all 0.3s ease'\n  },\n  dragActive: {\n    border: '2px dashed #3f51b5',\n    backgroundColor: 'rgba(63, 81, 181, 0.05)'\n  },\n  dragInactive: {\n    border: '2px dashed #ccc',\n    backgroundColor: 'white'\n  },\n  uploadBox: {\n    display: 'flex',\n    flexDirection: 'column',\n    alignItems: 'center',\n    justifyContent: 'center',\n    height: '100%',\n    width: '100%',\n    cursor: 'pointer'\n  },\n  uploadIcon: {\n    fontSize: 60,\n    color: '#3f51b5',\n    marginBottom: theme.spacing(2)\n  },\n  supportText: {\n    marginTop: theme.spacing(2)\n  },\n  previewBox: {\n    display: 'flex',\n    flexDirection: 'column',\n    alignItems: 'center',\n    width: '100%',\n    height: '100%',\n    position: 'relative'\n  },\n  imageContainer: {\n    position: 'relative',\n    width: '100%',\n    // Use viewport-based height so any aspect ratio fits inside\n    height: '60vh',\n    [theme.breakpoints.down('sm')]: {\n      height: '45vh',\n    },\n    display: 'flex',\n    justifyContent: 'center',\n    alignItems: 'center',\n    overflow: 'hidden',\n    marginTop: theme.spacing(2),\n  },\n  deleteButton: {\n    position: 'absolute',\n    top: 0,\n    right: 0,\n    backgroundColor: 'rgba(255, 255, 255, 0.7)',\n    '&:hover': {\n      backgroundColor: 'rgba(255, 255, 255, 0.9)',\n    }\n  }\n}));\n\nconst ImageUploader = ({ onImageUpload }) => {\n  const classes = useStyles();\n  const [previewUrl, setPreviewUrl] = useState(null);\n  const [dragActive, setDragActive] = useState(false);\n  const fileInputRef = useRef(null);\n\n  const handleDrag = (e) => {\n    e.preventDefault();\n    e.stopPropagation();\n    if (e.type === 'dragenter' || e.type === 'dragover') {\n      setDragActive(true);\n    } else if (e.type === 'dragleave') {\n      setDragActive(false);\n    }\n  };\n\n  const handleDrop = (e) => {\n    e.preventDefault();\n    e.stopPropagation();\n    setDragActive(false);\n    if (e.dataTransfer.files && e.dataTransfer.files[0]) {\n      handleFiles(e.dataTransfer.files[0]);\n    }\n  };\n\n  const handleChange = (e) => {\n    e.preventDefault();\n    if (e.target.files && e.target.files[0]) {\n      handleFiles(e.target.files[0]);\n    }\n  };\n\n  const handleFiles = (file) => {\n    if (file.type.startsWith('image/')) {\n      setPreviewUrl(URL.createObjectURL(file));\n      onImageUpload(file);\n    } else {\n      alert('Please upload an image file');\n    }\n  };\n\n  const onButtonClick = () => {\n    fileInputRef.current.click();\n  };\n\n  const handleRemoveImage = () => {\n    setPreviewUrl(null);\n    onImageUpload(null);\n    fileInputRef.current.value = \"\";\n  };\n\n  return (\n    <Paper \n      className={`${classes.paper} ${dragActive ? classes.dragActive : classes.dragInactive}`}\n      onDragEnter={handleDrag}\n      onDragLeave={handleDrag}\n      onDragOver={handleDrag}\n      onDrop={handleDrop}\n    >\n      <input\n        ref={fileInputRef}\n        type=\"file\"\n        accept=\"image/*\"\n        onChange={handleChange}\n        style={{ display: 'none' }}\n      />\n\n      {!previewUrl ? (\n        <Box \n          className={classes.uploadBox}\n          onClick={onButtonClick}\n        >\n          <CloudUploadIcon className={classes.uploadIcon} />\n          <Typography variant=\"h6\" gutterBottom>\n            Drag & Drop an image here\n          </Typography>\n          <Typography variant=\"body2\" color=\"textSecondary\" gutterBottom>\n            or\n          </Typography>\n          <Button\n            variant=\"contained\"\n            color=\"primary\"\n            component=\"span\"\n            startIcon={<CloudUploadIcon />}\n          >\n            Browse Files\n          </Button>\n          <Typography variant=\"body2\" color=\"textSecondary\" className={classes.supportText}>\n            Supported formats: JPG, PNG, GIF\n          </Typography>\n        </Box>\n      ) : (\n        <Box className={classes.previewBox}>\n          <Typography variant=\"h6\" gutterBottom>\n            Preview\n          </Typography>\n          <Box className={classes.imageContainer}>\n            <img\n              src={previewUrl}\n              alt=\"Preview\"\n              className=\"preview-image\"\n              style={{\n                width: '100%',\n                height: 'auto',\n                maxHeight: '100%',\n                objectFit: 'contain',\n                display: 'block'\n              }}\n            />\n            <IconButton\n              aria-label=\"delete\"\n              className={classes.deleteButton}\n              onClick={handleRemoveImage}\n            >\n              <DeleteIcon />\n            </IconButton>\n          </Box>\n        </Box>\n      )}\n    </Paper>\n  );\n};\n\nexport default ImageUploader;\n","import React from 'react';\nimport { \n  Grid, \n  Card, \n  CardContent, \n  CardActions, \n  Typography, \n  Button, \n  Chip,\n  Box\n} from '@material-ui/core';\nimport VisibilityIcon from '@material-ui/icons/Visibility';\nimport CategoryIcon from '@material-ui/icons/Category';\nimport PlayArrowIcon from '@material-ui/icons/PlayArrow';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  card: {\n    height: '100%',\n    display: 'flex',\n    flexDirection: 'column',\n  },\n  selectedCard: {\n    border: '2px solid #3f51b5',\n  },\n  unavailableCard: {\n    opacity: 0.6,\n  },\n  cardContent: {\n    flexGrow: 1,\n  },\n  chipContainer: {\n    marginBottom: theme.spacing(1.5),\n  },\n  successChip: {\n    backgroundColor: '#34C759',\n    color: '#fff',\n  },\n  errorChip: {\n    backgroundColor: '#FF3B3F',\n    color: '#fff',\n  },\n  modelType: {\n    marginTop: theme.spacing(1),\n  },\n  processButton: {\n    marginTop: theme.spacing(3),\n    textAlign: 'center',\n  }\n}));\n\nconst ModelSelector = ({ \n  onModelSelect, \n  onProcess, \n  isProcessing, \n  modelsStatus, \n  selectedModel,\n  imageSelected \n}) => {\n  const classes = useStyles();\n  \n  const models = [\n    {\n      id: 'yolo',\n      name: 'YOLOv8',\n      description: 'Fast and accurate object detection',\n      icon: <VisibilityIcon />,\n      available: modelsStatus.yolo\n    },\n    {\n      id: 'detr',\n      name: 'DETR',\n      description: 'DEtection TRansformer for object detection',\n      icon: <VisibilityIcon />,\n      available: modelsStatus.detr\n    },\n    {\n      id: 'vit',\n      name: 'ViT',\n      description: 'Vision Transformer for image classification',\n      icon: <CategoryIcon />,\n      available: modelsStatus.vit\n    }\n  ];\n\n  const handleModelClick = (modelId) => {\n    if (models.find(m => m.id === modelId).available) {\n      onModelSelect(modelId);\n    }\n  };\n\n  return (\n    <Box sx={{ p: 2, height: '100%' }}>\n      <Typography variant=\"h6\" gutterBottom>\n        Select Model\n      </Typography>\n      \n      <Grid container spacing={2}>\n        {models.map((model) => (\n          <Grid item xs={12} sm={4} key={model.id}>\n            <Card \n              className={`\n                ${classes.card} \n                ${selectedModel === model.id ? classes.selectedCard : ''} \n                ${!model.available ? classes.unavailableCard : ''}\n              `}\n              onClick={() => handleModelClick(model.id)}\n            >\n              <CardContent className={classes.cardContent}>\n                <Box sx={{ mb: 2, color: 'primary' }}>\n                  {model.icon}\n                </Box>\n                <Typography variant=\"h5\" component=\"div\" gutterBottom>\n                  {model.name}\n                </Typography>\n                <div className={classes.chipContainer}>\n                  {model.available ? (\n                    <Chip \n                      label=\"Available\" \n                      className={classes.successChip}\n                      size=\"small\" \n                    />\n                  ) : (\n                    <Chip \n                      label=\"Not Available\" \n                      className={classes.errorChip}\n                      size=\"small\" \n                    />\n                  )}\n                </div>\n                <Typography variant=\"body2\" color=\"textSecondary\">\n                  {model.description}\n                </Typography>\n              </CardContent>\n              <CardActions>\n                <Button \n                  size=\"small\" \n                  onClick={() => handleModelClick(model.id)}\n                  disabled={!model.available}\n                  color={selectedModel === model.id ? \"primary\" : \"default\"}\n                  variant={selectedModel === model.id ? \"contained\" : \"outlined\"}\n                  fullWidth\n                >\n                  {selectedModel === model.id ? 'Selected' : 'Select'}\n                </Button>\n              </CardActions>\n            </Card>\n          </Grid>\n        ))}\n      </Grid>\n\n      <div className={classes.processButton}>\n        <Button\n          variant=\"contained\"\n          color=\"primary\"\n          size=\"large\"\n          startIcon={<PlayArrowIcon />}\n          onClick={onProcess}\n          disabled={!selectedModel || !imageSelected || isProcessing}\n        >\n          {isProcessing ? 'Processing...' : 'Process Image'}\n        </Button>\n      </div>\n    </Box>\n  );\n};\n\nexport default ModelSelector;\n","import React, { useState } from 'react';\nimport { \n  Button, \n  Box, \n  Typography, \n  CircularProgress, \n  Snackbar,\n  Dialog,\n  DialogTitle,\n  DialogContent,\n  DialogActions,\n  TextField,\n  FormControl,\n  InputLabel,\n  Select,\n  MenuItem,\n  Grid,\n  Card,\n  CardMedia,\n  CardContent,\n  Chip\n} from '@material-ui/core';\nimport { Alert } from '@material-ui/lab';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  root: {\n    marginTop: theme.spacing(2),\n    marginBottom: theme.spacing(2),\n    padding: theme.spacing(2),\n    backgroundColor: '#f5f5f5',\n    borderRadius: theme.shape.borderRadius,\n  },\n  button: {\n    marginRight: theme.spacing(2),\n  },\n  searchDialog: {\n    minWidth: '500px',\n  },\n  formControl: {\n    marginBottom: theme.spacing(2),\n    minWidth: '100%',\n  },\n  searchResults: {\n    marginTop: theme.spacing(2),\n  },\n  resultCard: {\n    marginBottom: theme.spacing(2),\n  },\n  resultImage: {\n    height: 140,\n    objectFit: 'contain',\n  },\n  chip: {\n    margin: theme.spacing(0.5),\n  },\n  similarityChip: {\n    backgroundColor: theme.palette.primary.main,\n    color: 'white',\n  }\n}));\n\nconst VectorDBActions = ({ results }) => {\n  const classes = useStyles();\n  const [isSaving, setIsSaving] = useState(false);\n  const [saveSuccess, setSaveSuccess] = useState(false);\n  const [saveError, setSaveError] = useState(null);\n  const [openSearchDialog, setOpenSearchDialog] = useState(false);\n  const [searchType, setSearchType] = useState('image');\n  const [searchClass, setSearchClass] = useState('');\n  const [searchResults, setSearchResults] = useState([]);\n  const [isSearching, setIsSearching] = useState(false);\n  const [searchError, setSearchError] = useState(null);\n  \n  // Extract model and data from results\n  const { model, data } = results;\n  \n  // Handle saving to vector DB\n  const handleSaveToVectorDB = async () => {\n    setIsSaving(true);\n    setSaveError(null);\n    \n    try {\n      let response;\n      \n      if (model === 'vit') {\n        // For ViT, save the whole image with classifications\n        response = await fetch('/api/add-to-collection', {\n          method: 'POST',\n          headers: {\n            'Content-Type': 'application/json',\n          },\n          body: JSON.stringify({\n            image: data.image,\n            metadata: {\n              model: 'vit',\n              classifications: data.classifications\n            }\n          })\n        });\n      } else {\n        // For YOLO and DETR, save detected objects\n        response = await fetch('/api/add-detected-objects', {\n          method: 'POST',\n          headers: {\n            'Content-Type': 'application/json',\n          },\n          body: JSON.stringify({\n            image: data.image,\n            objects: data.detections,\n            imageId: generateUUID()\n          })\n        });\n      }\n      \n      if (!response.ok) {\n        throw new Error(`HTTP error! Status: ${response.status}`);\n      }\n      \n      const result = await response.json();\n      \n      if (result.error) {\n        throw new Error(result.error);\n      }\n      \n      setSaveSuccess(true);\n      setTimeout(() => setSaveSuccess(false), 5000);\n    } catch (err) {\n      console.error('Error saving to vector DB:', err);\n      setSaveError(`Error saving to vector DB: ${err.message}`);\n    } finally {\n      setIsSaving(false);\n    }\n  };\n  \n  // Handle opening search dialog\n  const handleOpenSearchDialog = () => {\n    setOpenSearchDialog(true);\n    setSearchResults([]);\n    setSearchError(null);\n  };\n  \n  // Handle closing search dialog\n  const handleCloseSearchDialog = () => {\n    setOpenSearchDialog(false);\n  };\n  \n  // Handle search type change\n  const handleSearchTypeChange = (event) => {\n    setSearchType(event.target.value);\n    setSearchResults([]);\n    setSearchError(null);\n  };\n  \n  // Handle search class change\n  const handleSearchClassChange = (event) => {\n    setSearchClass(event.target.value);\n  };\n  \n  // Handle search\n  const handleSearch = async () => {\n    setIsSearching(true);\n    setSearchError(null);\n    \n    try {\n      let requestBody = {};\n      \n      if (searchType === 'image') {\n        // Search by current image\n        requestBody = {\n          searchType: 'image',\n          image: data.image,\n          n_results: 5\n        };\n      } else {\n        // Search by class name\n        if (!searchClass.trim()) {\n          throw new Error('Please enter a class name');\n        }\n        \n        requestBody = {\n          searchType: 'class',\n          class_name: searchClass.trim(),\n          n_results: 5\n        };\n      }\n      \n      const response = await fetch('/api/search-similar-objects', {\n        method: 'POST',\n        headers: {\n          'Content-Type': 'application/json',\n        },\n        body: JSON.stringify(requestBody)\n      });\n      \n      if (!response.ok) {\n        throw new Error(`HTTP error! Status: ${response.status}`);\n      }\n      \n      const result = await response.json();\n      \n      if (result.error) {\n        throw new Error(result.error);\n      }\n      \n      console.log('Search API response:', result);\n      \n      // The backend responds with {success, searchType, results} structure, so extract only the results array\n      if (result.success && Array.isArray(result.results)) {\n        console.log('Setting search results array:', result.results);\n        console.log('Results array length:', result.results.length);\n        console.log('First result item:', result.results[0]);\n        setSearchResults(result.results);\n      } else {\n        console.error('Unexpected API response format:', result);\n        throw new Error('Unexpected API response format');\n      }\n    } catch (err) {\n      console.error('Error searching vector DB:', err);\n      setSearchError(`Error searching vector DB: ${err.message}`);\n    } finally {\n      setIsSearching(false);\n    }\n  };\n  \n  // Generate UUID for image ID\n  const generateUUID = () => {\n    return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, function(c) {\n      const r = Math.random() * 16 | 0;\n      const v = c === 'x' ? r : (r & 0x3 | 0x8);\n      return v.toString(16);\n    });\n  };\n  \n  // Render search results\n  const renderSearchResults = () => {\n    console.log('Rendering search results:', searchResults);\n    console.log('Search results length:', searchResults.length);\n    \n    if (searchResults.length === 0) {\n      console.log('No results to render');\n      return (\n        <Typography variant=\"body1\">No results found.</Typography>\n      );\n    }\n    \n    return (\n      <Grid container spacing={2}>\n        {searchResults.map((result, index) => {\n          const similarity = (1 - result.distance) * 100;\n          \n          return (\n            <Grid item xs={12} sm={6} key={index}>\n              <Card className={classes.resultCard}>\n                {result.metadata && result.metadata.image_data ? (\n                  <CardMedia\n                    className={classes.resultImage}\n                    component=\"img\"\n                    height=\"200\"\n                    image={`data:image/jpeg;base64,${result.metadata.image_data}`}\n                    alt={result.metadata && result.metadata.class ? result.metadata.class : 'Object'}\n                  />\n                ) : (\n                  <Box \n                    className={classes.resultImage}\n                    style={{ \n                      backgroundColor: '#f0f0f0', \n                      display: 'flex', \n                      alignItems: 'center', \n                      justifyContent: 'center',\n                      height: 200\n                    }}\n                  >\n                    <Typography variant=\"body2\" color=\"textSecondary\">\n                      {result.metadata && result.metadata.class ? result.metadata.class : 'Object'} Image\n                    </Typography>\n                  </Box>\n                )}\n                <CardContent>\n                  <Box display=\"flex\" justifyContent=\"space-between\" alignItems=\"center\" mb={1}>\n                    <Typography variant=\"subtitle1\">Result #{index + 1}</Typography>\n                    <Chip \n                      label={`Similarity: ${similarity.toFixed(2)}%`}\n                      className={classes.similarityChip}\n                      size=\"small\"\n                    />\n                  </Box>\n                  <Typography variant=\"body2\" color=\"textSecondary\">\n                    <strong>Class:</strong> {result.metadata.class || 'N/A'}\n                  </Typography>\n                  {result.metadata.confidence && (\n                    <Typography variant=\"body2\" color=\"textSecondary\">\n                      <strong>Confidence:</strong> {(result.metadata.confidence * 100).toFixed(2)}%\n                    </Typography>\n                  )}\n                  <Typography variant=\"body2\" color=\"textSecondary\">\n                    <strong>Object ID:</strong> {result.id}\n                  </Typography>\n                </CardContent>\n              </Card>\n            </Grid>\n          );\n        })}\n      </Grid>\n    );\n  };\n  \n  return (\n    <Box className={classes.root}>\n      <Typography variant=\"h6\" gutterBottom>\n        Vector Database Actions\n      </Typography>\n      \n      <Box display=\"flex\" alignItems=\"center\" mb={2}>\n        <Button\n          variant=\"contained\"\n          color=\"primary\"\n          onClick={handleSaveToVectorDB}\n          disabled={isSaving}\n          className={classes.button}\n        >\n          {isSaving ? (\n            <>\n              <CircularProgress size={20} color=\"inherit\" style={{ marginRight: 8 }} />\n              Saving...\n            </>\n          ) : (\n            'Save to Vector DB'\n          )}\n        </Button>\n        \n        <Button\n          variant=\"outlined\"\n          color=\"primary\"\n          onClick={handleOpenSearchDialog}\n          className={classes.button}\n        >\n          Search Similar\n        </Button>\n      </Box>\n      \n      {saveError && (\n        <Alert severity=\"error\" style={{ marginTop: 8 }}>\n          {saveError}\n        </Alert>\n      )}\n      \n      <Snackbar open={saveSuccess} autoHideDuration={5000} onClose={() => setSaveSuccess(false)}>\n        <Alert severity=\"success\">\n          {model === 'vit' ? (\n            'Image and classifications successfully saved to vector DB!'\n          ) : (\n            'Detected objects successfully saved to vector DB!'\n          )}\n        </Alert>\n      </Snackbar>\n      \n      {/* Search Dialog */}\n      <Dialog\n        open={openSearchDialog}\n        onClose={handleCloseSearchDialog}\n        maxWidth=\"md\"\n        fullWidth\n      >\n        <DialogTitle>Search Vector Database</DialogTitle>\n        <DialogContent>\n          <FormControl className={classes.formControl}>\n            <InputLabel id=\"search-type-label\">Search Type</InputLabel>\n            <Select\n              labelId=\"search-type-label\"\n              id=\"search-type\"\n              value={searchType}\n              onChange={handleSearchTypeChange}\n            >\n              <MenuItem value=\"image\">Search by Current Image</MenuItem>\n              <MenuItem value=\"class\">Search by Class Name</MenuItem>\n            </Select>\n          </FormControl>\n          \n          {searchType === 'class' && (\n            <FormControl className={classes.formControl}>\n              <TextField\n                label=\"Class Name\"\n                value={searchClass}\n                onChange={handleSearchClassChange}\n                placeholder=\"e.g. person, car, dog...\"\n                fullWidth\n              />\n            </FormControl>\n          )}\n          \n          {searchError && (\n            <Alert severity=\"error\" style={{ marginBottom: 16 }}>\n              {searchError}\n            </Alert>\n          )}\n          \n          <Box className={classes.searchResults}>\n            {isSearching ? (\n              <Box display=\"flex\" justifyContent=\"center\" alignItems=\"center\" p={4}>\n                <CircularProgress />\n                <Typography variant=\"body1\" style={{ marginLeft: 16 }}>\n                  Searching...\n                </Typography>\n              </Box>\n            ) : (\n              <>\n                {console.log('Search dialog render - searchResults:', searchResults)}\n                {searchResults.length > 0 ? renderSearchResults() : \n                  <Typography variant=\"body1\">No results found. Please try another search.</Typography>\n                }\n              </>\n            )}\n          </Box>\n        </DialogContent>\n        <DialogActions>\n          <Button onClick={handleCloseSearchDialog} color=\"default\">\n            Close\n          </Button>\n          <Button \n            onClick={handleSearch} \n            color=\"primary\" \n            variant=\"contained\"\n            disabled={isSearching || (searchType === 'class' && !searchClass.trim())}\n          >\n            Search\n          </Button>\n        </DialogActions>\n      </Dialog>\n    </Box>\n  );\n};\n\nexport default VectorDBActions;\n","import React from 'react';\nimport { \n  Paper, \n  Typography, \n  Box, \n  List, \n  ListItem, \n  ListItemText, \n  Divider,\n  Grid,\n  Chip\n} from '@material-ui/core';\nimport VectorDBActions from './VectorDBActions';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  paper: {\n    padding: theme.spacing(2)\n  },\n  marginBottom: {\n    marginBottom: theme.spacing(2)\n  },\n  resultImage: {\n    maxWidth: '100%',\n    maxHeight: '400px',\n    objectFit: 'contain'\n  },\n  dividerMargin: {\n    margin: `${theme.spacing(2)}px 0`\n  },\n  chipContainer: {\n    display: 'flex',\n    gap: theme.spacing(1),\n    flexWrap: 'wrap'\n  }\n}));\n\nconst ResultDisplay = ({ results }) => {\n  const classes = useStyles();\n  if (!results) return null;\n  \n  const { model, data } = results;\n  \n  // Helper to format times nicely\n  const formatTime = (ms) => {\n    if (ms === undefined || ms === null || isNaN(ms)) return '-';\n    const num = Number(ms);\n    if (num < 1000) return `${num.toFixed(2)} ms`;\n    return `${(num / 1000).toFixed(2)} s`;\n  };\n  \n  // Check if there's an error\n  if (data.error) {\n    return (\n      <Paper sx={{ p: 2, bgcolor: '#ffebee' }}>\n        <Typography color=\"error\">{data.error}</Typography>\n      </Paper>\n    );\n  }\n\n  // Display performance info\n  const renderPerformanceInfo = () => {\n    if (!data.performance) return null;\n    \n    return (\n      <Box className=\"performance-info\">\n        <Divider className={classes.dividerMargin} />\n        <Typography variant=\"body2\">\n          Inference time: {formatTime(data.performance.inference_time)} on {data.performance.device}\n        </Typography>\n      </Box>\n    );\n  };\n\n  // Render for YOLO and DETR (object detection)\n  if (model === 'yolo' || model === 'detr') {\n    return (\n      <Paper className={classes.paper}>\n        <Typography variant=\"h6\" gutterBottom>\n          {model === 'yolo' ? 'YOLOv8' : 'DETR'} Detection Results\n        </Typography>\n        \n        <Grid container spacing={3}>\n          <Grid item xs={12} md={6}>\n            {data.image && (\n              <Box className={classes.marginBottom}>\n                <Typography variant=\"subtitle1\" gutterBottom>\n                  Detection Result\n                </Typography>\n                <img \n                  src={`data:image/png;base64,${data.image}`} \n                  alt=\"Detection Result\" \n                  className={classes.resultImage}\n                />\n              </Box>\n            )}\n          </Grid>\n          \n          <Grid item xs={12} md={6}>\n            <Box className={classes.marginBottom}>\n              <Typography variant=\"subtitle1\" gutterBottom>\n                Detected Objects:\n              </Typography>\n              \n              {data.detections && data.detections.length > 0 ? (\n                <List>\n                  {data.detections.map((detection, index) => (\n                    <React.Fragment key={index}>\n                      <ListItem>\n                        <ListItemText \n                          primary={\n                            <Box style={{ display: 'flex', alignItems: 'center' }}>\n                              <Typography variant=\"body1\" component=\"span\">\n                                {detection.class}\n                              </Typography>\n                              <Chip \n                                label={`${(detection.confidence * 100).toFixed(0)}%`}\n                                size=\"small\"\n                                color=\"primary\"\n                                style={{ marginLeft: 8 }}\n                              />\n                            </Box>\n                          } \n                          secondary={`Bounding Box: [${detection.bbox.join(', ')}]`} \n                        />\n                      </ListItem>\n                      {index < data.detections.length - 1 && <Divider />}\n                    </React.Fragment>\n                  ))}\n                </List>\n              ) : (\n                <Typography variant=\"body1\">No objects detected</Typography>\n              )}\n            </Box>\n          </Grid>\n        </Grid>\n        \n        {renderPerformanceInfo()}\n        \n        {/* Vector DB Actions for Object Detection */}\n        <VectorDBActions results={results} />\n      </Paper>\n    );\n  }\n  \n  // Render for ViT (classification)\n  if (model === 'vit') {\n    return (\n      <Paper className={classes.paper}>\n        <Typography variant=\"h6\" gutterBottom>\n          ViT Classification Results\n        </Typography>\n        \n        <Typography variant=\"subtitle1\" gutterBottom>\n          Top Predictions:\n        </Typography>\n        \n        {data.top_predictions && data.top_predictions.length > 0 ? (\n          <List>\n            {data.top_predictions.map((prediction, index) => (\n              <React.Fragment key={index}>\n                <ListItem>\n                  <ListItemText \n                    primary={\n                      <Box style={{ display: 'flex', alignItems: 'center' }}>\n                        <Typography variant=\"body1\" component=\"span\">\n                          {prediction.rank}. {prediction.class}\n                        </Typography>\n                        <Chip \n                          label={`${(prediction.probability * 100).toFixed(1)}%`}\n                          size=\"small\"\n                          color={index === 0 ? \"primary\" : \"default\"}\n                          style={{ marginLeft: 8 }}\n                        />\n                      </Box>\n                    } \n                  />\n                </ListItem>\n                {index < data.top_predictions.length - 1 && <Divider />}\n              </React.Fragment>\n            ))}\n          </List>\n        ) : (\n          <Typography variant=\"body1\">No classifications available</Typography>\n        )}\n        \n        {renderPerformanceInfo()}\n        \n        {/* Vector DB Actions for ViT Classification */}\n        <VectorDBActions results={results} />\n      </Paper>\n    );\n  }\n  \n  return null;\n};\n\nexport default ResultDisplay;\n","import React, { useState } from 'react';\nimport { \n  Paper, \n  Typography, \n  Box, \n  TextField, \n  Button, \n  CircularProgress,\n  Divider\n} from '@material-ui/core';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  paper: {\n    padding: theme.spacing(2),\n    marginTop: theme.spacing(2)\n  },\n  marginBottom: {\n    marginBottom: theme.spacing(2)\n  },\n  dividerMargin: {\n    margin: `${theme.spacing(2)}px 0`\n  },\n  responseBox: {\n    padding: theme.spacing(2),\n    backgroundColor: '#f5f5f5',\n    borderRadius: theme.shape.borderRadius,\n    marginTop: theme.spacing(2),\n    whiteSpace: 'pre-wrap'\n  },\n  buttonProgress: {\n    marginLeft: theme.spacing(1)\n  }\n}));\n\nconst LlmAnalysis = ({ visionResults, model }) => {\n  const classes = useStyles();\n  const [userQuery, setUserQuery] = useState('');\n  const [isAnalyzing, setIsAnalyzing] = useState(false);\n  const [analysisResult, setAnalysisResult] = useState(null);\n  const [error, setError] = useState(null);\n\n  // Format time for display\n  const formatTime = (ms) => {\n    if (ms === undefined || ms === null || isNaN(ms)) return '-';\n    const num = Number(ms);\n    if (num < 1000) return `${num.toFixed(2)} ms`;\n    return `${(num / 1000).toFixed(2)} s`;\n  };\n\n  const handleAnalyze = async () => {\n    if (!userQuery.trim()) return;\n    \n    setIsAnalyzing(true);\n    setError(null);\n    \n    try {\n      const response = await fetch('/api/analyze', {\n        method: 'POST',\n        headers: {\n          'Content-Type': 'application/json',\n        },\n        body: JSON.stringify({\n          visionResults: visionResults,\n          userQuery: userQuery\n        }),\n      });\n\n      if (!response.ok) {\n        throw new Error(`HTTP error! Status: ${response.status}`);\n      }\n\n      const data = await response.json();\n      \n      if (data.error) {\n        setError(data.error);\n      } else {\n        setAnalysisResult(data);\n      }\n    } catch (err) {\n      console.error('Error analyzing with LLM:', err);\n      setError(`Error analyzing with LLM: ${err.message}`);\n    } finally {\n      setIsAnalyzing(false);\n    }\n  };\n\n  if (!visionResults) return null;\n\n  return (\n    <Paper className={classes.paper}>\n      <Typography variant=\"h6\" gutterBottom>\n        Ask AI about the {model === 'vit' ? 'Classification' : 'Detection'} Results\n      </Typography>\n      \n      <Typography variant=\"body2\" className={classes.marginBottom}>\n        Ask a question about the detected objects or classifications to get an AI-powered analysis.\n      </Typography>\n      \n      <TextField\n        fullWidth\n        label=\"Your question about the image\"\n        variant=\"outlined\"\n        value={userQuery}\n        onChange={(e) => setUserQuery(e.target.value)}\n        disabled={isAnalyzing}\n        className={classes.marginBottom}\n        placeholder={model === 'vit' \n          ? \"E.g., What category does this image belong to?\" \n          : \"E.g., How many people are in this image?\"}\n      />\n      \n      <Button \n        variant=\"contained\" \n        color=\"primary\"\n        onClick={handleAnalyze}\n        disabled={isAnalyzing || !userQuery.trim()}\n      >\n        Analyze with AI\n        {isAnalyzing && <CircularProgress size={24} className={classes.buttonProgress} />}\n      </Button>\n      \n      {error && (\n        <Box mt={2}>\n          <Typography color=\"error\">{error}</Typography>\n        </Box>\n      )}\n      \n      {analysisResult && (\n        <>\n          <Divider className={classes.dividerMargin} />\n          \n          <Typography variant=\"subtitle1\" gutterBottom>\n            AI Analysis:\n          </Typography>\n          \n          <Box className={classes.responseBox}>\n            <Typography variant=\"body1\">\n              {analysisResult.response}\n            </Typography>\n          </Box>\n          \n          {analysisResult.performance && (\n            <Box mt={1}>\n              <Typography variant=\"body2\" color=\"textSecondary\">\n                Analysis time: {formatTime(analysisResult.performance.inference_time)} on {analysisResult.performance.device}\n              </Typography>\n            </Box>\n          )}\n        </>\n      )}\n    </Paper>\n  );\n};\n\nexport default LlmAnalysis;\n","import React, { useState } from 'react';\nimport {\n  Paper,\n  Typography,\n  Grid,\n  TextField,\n  Button,\n  Divider,\n  MenuItem,\n  Select,\n  FormControl,\n  InputLabel\n} from '@material-ui/core';\n\n// Props:\n// - imageBase64: data URL string of the currently uploaded image (preferred for searchType=image)\nfunction OpenAIChat({ imageBase64 }) {\n  const [apiKey, setApiKey] = useState('');\n  const [prompt, setPrompt] = useState('');\n  const [response, setResponse] = useState('');\n  const [loading, setLoading] = useState(false);\n  const [error, setError] = useState('');\n\n  // Vision RAG parameters\n  const [searchType, setSearchType] = useState('image');\n  const [objectId, setObjectId] = useState('');\n  const [className, setClassName] = useState('');\n  const [nResults, setNResults] = useState(5);\n\n  const onSend = async () => {\n    setError('');\n    setResponse('');\n    const q = (prompt || '').trim();\n    if (!q) { setError('Please enter a question.'); return; }\n\n    // Build request body for /api/vision-rag/query\n    const body = {\n      userQuery: q,\n      searchType: searchType,\n      n_results: Number(nResults) || 5,\n    };\n    if (apiKey) body.api_key = apiKey;\n    if (searchType === 'image') {\n      if (!imageBase64) { setError('Image is required. Please upload an image first.'); return; }\n      body.image = imageBase64;\n    } else if (searchType === 'object') {\n      if (!objectId.trim()) { setError('Please enter an objectId.'); return; }\n      body.objectId = objectId.trim();\n    } else if (searchType === 'class') {\n      if (!className.trim()) { setError('Please enter a class_name.'); return; }\n      body.class_name = className.trim();\n    }\n\n    setLoading(true);\n    try {\n      const res = await fetch('/api/vision-rag/query', {\n        method: 'POST',\n        headers: { 'Content-Type': 'application/json' },\n        credentials: 'include',\n        body: JSON.stringify(body)\n      });\n\n      if (!res.ok) {\n        let txt = await res.text();\n        try { txt = JSON.stringify(JSON.parse(txt), null, 2); } catch {}\n        throw new Error(txt);\n      }\n      const data = await res.json();\n      const meta = `Model: ${data.model || '-'} | Latency: ${data.latency_sec || '-'}s`;\n      \n      // Format search results\n      let retrievedText = '';\n      if (data.retrieved && data.retrieved.length > 0) {\n        retrievedText = '\\n\\nSearch Results:\\n';\n        data.retrieved.forEach((item, index) => {\n          retrievedText += `${index + 1}. ID: ${item.id || '-'}\\n`;\n          if (item.meta) {\n            retrievedText += `   Class: ${item.meta.class || '-'}\\n`;\n            retrievedText += `   Confidence: ${item.meta.confidence || '-'}\\n`;\n          }\n          retrievedText += `   Similarity: ${item.distance ? item.distance.toFixed(4) : '-'}\\n`;\n        });\n      }\n      \n      setResponse((data.answer || '(No response)') + retrievedText + '\\n\\n---\\n' + meta);\n    } catch (e) {\n      setError('Error: ' + e.message);\n    } finally {\n      setLoading(false);\n    }\n  };\n\n  const onClear = () => {\n    setPrompt('');\n    setResponse('');\n    setError('');\n  };\n\n  return (\n    <Paper style={{ padding: 16 }}>\n      <Typography variant=\"h5\" gutterBottom>\n        Vision RAG (LangChain)\n      </Typography>\n      <Typography variant=\"body2\" color=\"textSecondary\" gutterBottom>\n        If OPENAI_API_KEY is set on the server, API Key is optional. Select a search type and send a question to get answers based on context retrieved from the vector DB.\n      </Typography>\n      <Grid container spacing={2}>\n        <Grid item xs={12} md={6}>\n          <FormControl fullWidth variant=\"outlined\" size=\"small\">\n            <InputLabel id=\"search-type-label\">Search Type</InputLabel>\n            <Select\n              labelId=\"search-type-label\"\n              value={searchType}\n              onChange={(e) => setSearchType(e.target.value)}\n              label=\"Search Type\"\n            >\n              <MenuItem value=\"image\">image (current upload)</MenuItem>\n              <MenuItem value=\"object\">object (objectId)</MenuItem>\n              <MenuItem value=\"class\">class (class_name)</MenuItem>\n            </Select>\n          </FormControl>\n        </Grid>\n        <Grid item xs={12} md={6}>\n          <TextField\n            label=\"OpenAI API Key (optional)\"\n            value={apiKey}\n            onChange={(e) => setApiKey(e.target.value)}\n            fullWidth\n            variant=\"outlined\"\n            size=\"small\"\n            type=\"password\"\n            placeholder=\"sk-...\"\n          />\n        </Grid>\n\n        {searchType === 'object' && (\n          <Grid item xs={12} md={6}>\n            <TextField\n              label=\"objectId\"\n              value={objectId}\n              onChange={(e) => setObjectId(e.target.value)}\n              fullWidth\n              variant=\"outlined\"\n              size=\"small\"\n            />\n          </Grid>\n        )}\n        {searchType === 'class' && (\n          <Grid item xs={12} md={6}>\n            <TextField\n              label=\"class_name\"\n              value={className}\n              onChange={(e) => setClassName(e.target.value)}\n              fullWidth\n              variant=\"outlined\"\n              size=\"small\"\n              placeholder=\"e.g. person, car, dog\"\n            />\n          </Grid>\n        )}\n        <Grid item xs={12} md={6}>\n          <TextField\n            label=\"n_results\"\n            value={nResults}\n            onChange={(e) => setNResults(e.target.value)}\n            fullWidth\n            variant=\"outlined\"\n            size=\"small\"\n            type=\"number\"\n            inputProps={{ min: 1, max: 50 }}\n          />\n        </Grid>\n\n        <Grid item xs={12}>\n          <TextField\n            label=\"User Question\"\n            value={prompt}\n            onChange={(e) => setPrompt(e.target.value)}\n            fullWidth\n            multiline\n            rows={4}\n            variant=\"outlined\"\n            placeholder={searchType === 'image' ? 'Answer based on the uploaded image' : 'Answer using the retrieved object context'}\n          />\n        </Grid>\n        {error && (\n          <Grid item xs={12}>\n            <Typography color=\"error\">{error}</Typography>\n          </Grid>\n        )}\n        <Grid item xs={12}>\n          <div style={{ display: 'flex', gap: 8 }}>\n            <Button color=\"primary\" variant=\"contained\" onClick={onSend} disabled={loading}>\n              {loading ? 'Sending...' : 'Send Question'}\n            </Button>\n            <Button variant=\"outlined\" onClick={onClear}>Clear</Button>\n          </div>\n        </Grid>\n        <Grid item xs={12}>\n          <Divider style={{ margin: '12px 0' }} />\n          <Typography variant=\"subtitle2\" color=\"textSecondary\">Response</Typography>\n          <pre style={{ whiteSpace: 'pre-wrap', fontFamily: 'ui-monospace, monospace' }}>{response}</pre>\n        </Grid>\n      </Grid>\n    </Paper>\n  );\n}\n\nexport default OpenAIChat;\n","import React, { useState, useEffect } from 'react';\nimport { \n  Container, \n  Typography, \n  Box, \n  Paper, \n  Grid, \n  CircularProgress,\n  AppBar,\n  Toolbar,\n  ThemeProvider,\n  createMuiTheme\n} from '@material-ui/core';\nimport ImageUploader from './components/ImageUploader';\nimport ModelSelector from './components/ModelSelector';\nimport ResultDisplay from './components/ResultDisplay';\nimport LlmAnalysis from './components/LlmAnalysis';\nimport OpenAIChat from './components/OpenAIChat';\nimport './App.css';\n\n// Create a theme\nconst theme = createMuiTheme({\n  palette: {\n    primary: {\n      main: '#3f51b5',\n    },\n    secondary: {\n      main: '#f50057',\n    },\n  },\n  typography: {\n    fontFamily: 'Roboto, Arial, sans-serif',\n  },\n});\n\nfunction App() {\n  const [selectedImage, setSelectedImage] = useState(null);\n  const [imageBase64, setImageBase64] = useState('');\n  const [selectedModel, setSelectedModel] = useState('');\n  const [isProcessing, setIsProcessing] = useState(false);\n  const [results, setResults] = useState(null);\n  const [error, setError] = useState(null);\n  const [modelsStatus, setModelsStatus] = useState({\n    yolo: false,\n    detr: false,\n    vit: false\n  });\n\n  // Check API status on component mount\n  useEffect(() => {\n    fetch('/api/status')\n      .then(response => response.json())\n      .then(data => {\n        setModelsStatus(data.models);\n      })\n      .catch(err => {\n        console.error('Error checking API status:', err);\n        setError('Error connecting to the backend API. Please make sure the server is running.');\n      });\n  }, []);\n\n  const handleImageUpload = (image) => {\n    setSelectedImage(image);\n    setResults(null);\n    setError(null);\n\n    // Convert uploaded File -> base64 data URL for Vision RAG image search\n    if (image instanceof File) {\n      const reader = new FileReader();\n      reader.onload = () => {\n        const dataUrl = reader.result; // e.g., data:image/png;base64,....\n        setImageBase64(typeof dataUrl === 'string' ? dataUrl : '');\n      };\n      reader.onerror = () => setImageBase64('');\n      reader.readAsDataURL(image);\n    } else {\n      setImageBase64('');\n    }\n  };\n\n  const handleModelSelect = (model) => {\n    setSelectedModel(model);\n    setResults(null);\n    setError(null);\n  };\n\n  const processImage = async () => {\n    if (!selectedImage || !selectedModel) {\n      setError('Please select both an image and a model');\n      return;\n    }\n\n    setIsProcessing(true);\n    setError(null);\n\n    // Create form data for the image\n    const formData = new FormData();\n    formData.append('image', selectedImage);\n\n    let endpoint = '';\n    switch (selectedModel) {\n      case 'yolo':\n        endpoint = '/api/detect/yolo';\n        break;\n      case 'detr':\n        endpoint = '/api/detect/detr';\n        break;\n      case 'vit':\n        endpoint = '/api/classify/vit';\n        break;\n      default:\n        setError('Invalid model selection');\n        setIsProcessing(false);\n        return;\n    }\n\n    try {\n      const response = await fetch(endpoint, {\n        method: 'POST',\n        body: formData,\n      });\n\n      if (!response.ok) {\n        throw new Error(`HTTP error! Status: ${response.status}`);\n      }\n\n      const data = await response.json();\n      setResults({ model: selectedModel, data });\n    } catch (err) {\n      console.error('Error processing image:', err);\n      setError(`Error processing image: ${err.message}`);\n    } finally {\n      setIsProcessing(false);\n    }\n  };\n\n  return (\n    <ThemeProvider theme={theme}>\n      <Box style={{ flexGrow: 1 }}>\n        <AppBar position=\"static\">\n          <Toolbar>\n            <Typography variant=\"h6\" style={{ flexGrow: 1 }}>\n              Multi-Model Object Detection Demo\n            </Typography>\n          </Toolbar>\n        </AppBar>\n        <Container maxWidth=\"lg\" style={{ marginTop: theme.spacing(4), marginBottom: theme.spacing(4) }}>\n          <Grid container spacing={3}>\n            <Grid item xs={12}>\n              <Paper style={{ padding: theme.spacing(2) }}>\n                <Typography variant=\"h5\" gutterBottom>\n                  Upload an image to see how each model performs!\n                </Typography>\n                <Typography variant=\"body1\" paragraph>\n                  This demo showcases three different object detection and image classification models:\n                </Typography>\n                <Typography variant=\"body1\" component=\"div\">\n                  <ul>\n                    <li><strong>YOLOv8</strong>: Fast and accurate object detection</li>\n                    <li><strong>DETR</strong>: DEtection TRansformer for object detection</li>\n                    <li><strong>ViT</strong>: Vision Transformer for image classification</li>\n                  </ul>\n                </Typography>\n              </Paper>\n            </Grid>\n            \n            <Grid item xs={12} md={6}>\n              <ImageUploader onImageUpload={handleImageUpload} />\n            </Grid>\n            \n            <Grid item xs={12} md={6}>\n              <ModelSelector \n                onModelSelect={handleModelSelect} \n                onProcess={processImage}\n                isProcessing={isProcessing}\n                modelsStatus={modelsStatus}\n                selectedModel={selectedModel}\n                imageSelected={!!selectedImage}\n              />\n            </Grid>\n            \n            {error && (\n              <Grid item xs={12}>\n                <Paper style={{ padding: theme.spacing(2), backgroundColor: '#ffebee' }}>\n                  <Typography color=\"error\">{error}</Typography>\n                </Paper>\n              </Grid>\n            )}\n            \n            {isProcessing && (\n              <Grid item xs={12} style={{ textAlign: 'center', margin: `${theme.spacing(4)}px 0` }}>\n                <CircularProgress />\n                <Typography variant=\"h6\" style={{ marginTop: theme.spacing(2) }}>\n                  Processing image...\n                </Typography>\n              </Grid>\n            )}\n            \n            {results && (\n              <>\n                <Grid item xs={12}>\n                  <ResultDisplay results={results} />\n                </Grid>\n                <Grid item xs={12}>\n                  <LlmAnalysis visionResults={results.data} model={results.model} />\n                </Grid>\n              </>\n            )}\n\n            {/* Vision RAG (LangChain) section at the end */}\n            <Grid item xs={12}>\n              <OpenAIChat imageBase64={imageBase64} />\n            </Grid>\n          </Grid>\n        </Container>\n      </Box>\n    </ThemeProvider>\n  );\n}\n\nexport default App;\n","const reportWebVitals = (onPerfEntry) => {\n  if (onPerfEntry && onPerfEntry instanceof Function) {\n    import('web-vitals').then(({ getCLS, getFID, getFCP, getLCP, getTTFB }) => {\n      getCLS(onPerfEntry);\n      getFID(onPerfEntry);\n      getFCP(onPerfEntry);\n      getLCP(onPerfEntry);\n      getTTFB(onPerfEntry);\n    });\n  }\n};\n\nexport default reportWebVitals;\n","import React from 'react';\nimport ReactDOM from 'react-dom';\nimport './index.css';\nimport App from './App';\nimport reportWebVitals from './reportWebVitals';\n\nReactDOM.render(\n  <React.StrictMode>\n    <App />\n  </React.StrictMode>,\n  document.getElementById('root')\n);\n\n// If you want to start measuring performance in your app, pass a function\n// to log results (for example: reportWebVitals(console.log))\n// or send to an analytics endpoint. Learn more: https://bit.ly/CRA-vitals\nreportWebVitals();\n"],"sourceRoot":""}

static/static/js/main.fc0e2dc7.chunk.js

@@ -1,2 +0,0 @@
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React, { useState, useRef } from 'react';\nimport { \n  Paper, \n  Typography, \n  Box, \n  Button, \n  IconButton \n} from '@material-ui/core';\nimport CloudUploadIcon from '@material-ui/icons/CloudUpload';\nimport DeleteIcon from '@material-ui/icons/Delete';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  paper: {\n    padding: theme.spacing(2),\n    display: 'flex',\n    flexDirection: 'column',\n    alignItems: 'center',\n    height: '100%',\n    minHeight: 300,\n    transition: 'all 0.3s ease'\n  },\n  dragActive: {\n    border: '2px dashed #3f51b5',\n    backgroundColor: 'rgba(63, 81, 181, 0.05)'\n  },\n  dragInactive: {\n    border: '2px dashed #ccc',\n    backgroundColor: 'white'\n  },\n  uploadBox: {\n    display: 'flex',\n    flexDirection: 'column',\n    alignItems: 'center',\n    justifyContent: 'center',\n    height: '100%',\n    width: '100%',\n    cursor: 'pointer'\n  },\n  uploadIcon: {\n    fontSize: 60,\n    color: '#3f51b5',\n    marginBottom: theme.spacing(2)\n  },\n  supportText: {\n    marginTop: theme.spacing(2)\n  },\n  previewBox: {\n    display: 'flex',\n    flexDirection: 'column',\n    alignItems: 'center',\n    width: '100%',\n    height: '100%',\n    position: 'relative'\n  },\n  imageContainer: {\n    position: 'relative',\n    width: '100%',\n    // Use viewport-based height so any aspect ratio fits inside\n    height: '60vh',\n    [theme.breakpoints.down('sm')]: {\n      height: '45vh',\n    },\n    display: 'flex',\n    justifyContent: 'center',\n    alignItems: 'center',\n    overflow: 'hidden',\n    marginTop: theme.spacing(2),\n  },\n  deleteButton: {\n    position: 'absolute',\n    top: 0,\n    right: 0,\n    backgroundColor: 'rgba(255, 255, 255, 0.7)',\n    '&:hover': {\n      backgroundColor: 'rgba(255, 255, 255, 0.9)',\n    }\n  }\n}));\n\nconst ImageUploader = ({ onImageUpload }) => {\n  const classes = useStyles();\n  const [previewUrl, setPreviewUrl] = useState(null);\n  const [dragActive, setDragActive] = useState(false);\n  const fileInputRef = useRef(null);\n\n  const handleDrag = (e) => {\n    e.preventDefault();\n    e.stopPropagation();\n    if (e.type === 'dragenter' || e.type === 'dragover') {\n      setDragActive(true);\n    } else if (e.type === 'dragleave') {\n      setDragActive(false);\n    }\n  };\n\n  const handleDrop = (e) => {\n    e.preventDefault();\n    e.stopPropagation();\n    setDragActive(false);\n    if (e.dataTransfer.files && e.dataTransfer.files[0]) {\n      handleFiles(e.dataTransfer.files[0]);\n    }\n  };\n\n  const handleChange = (e) => {\n    e.preventDefault();\n    if (e.target.files && e.target.files[0]) {\n      handleFiles(e.target.files[0]);\n    }\n  };\n\n  const handleFiles = (file) => {\n    if (file.type.startsWith('image/')) {\n      setPreviewUrl(URL.createObjectURL(file));\n      onImageUpload(file);\n    } else {\n      alert('Please upload an image file');\n    }\n  };\n\n  const onButtonClick = () => {\n    fileInputRef.current.click();\n  };\n\n  const handleRemoveImage = () => {\n    setPreviewUrl(null);\n    onImageUpload(null);\n    fileInputRef.current.value = \"\";\n  };\n\n  return (\n    <Paper \n      className={`${classes.paper} ${dragActive ? classes.dragActive : classes.dragInactive}`}\n      onDragEnter={handleDrag}\n      onDragLeave={handleDrag}\n      onDragOver={handleDrag}\n      onDrop={handleDrop}\n    >\n      <input\n        ref={fileInputRef}\n        type=\"file\"\n        accept=\"image/*\"\n        onChange={handleChange}\n        style={{ display: 'none' }}\n      />\n\n      {!previewUrl ? (\n        <Box \n          className={classes.uploadBox}\n          onClick={onButtonClick}\n        >\n          <CloudUploadIcon className={classes.uploadIcon} />\n          <Typography variant=\"h6\" gutterBottom>\n            Drag & Drop an image here\n          </Typography>\n          <Typography variant=\"body2\" color=\"textSecondary\" gutterBottom>\n            or\n          </Typography>\n          <Button\n            variant=\"contained\"\n            color=\"primary\"\n            component=\"span\"\n            startIcon={<CloudUploadIcon />}\n          >\n            Browse Files\n          </Button>\n          <Typography variant=\"body2\" color=\"textSecondary\" className={classes.supportText}>\n            Supported formats: JPG, PNG, GIF\n          </Typography>\n        </Box>\n      ) : (\n        <Box className={classes.previewBox}>\n          <Typography variant=\"h6\" gutterBottom>\n            Preview\n          </Typography>\n          <Box className={classes.imageContainer}>\n            <img\n              src={previewUrl}\n              alt=\"Preview\"\n              className=\"preview-image\"\n              style={{\n                width: '100%',\n                height: 'auto',\n                maxHeight: '100%',\n                objectFit: 'contain',\n                display: 'block'\n              }}\n            />\n            <IconButton\n              aria-label=\"delete\"\n              className={classes.deleteButton}\n              onClick={handleRemoveImage}\n            >\n              <DeleteIcon />\n            </IconButton>\n          </Box>\n        </Box>\n      )}\n    </Paper>\n  );\n};\n\nexport default ImageUploader;\n","import React from 'react';\nimport { \n  Grid, \n  Card, \n  CardContent, \n  CardActions, \n  Typography, \n  Button, \n  Chip,\n  Box\n} from '@material-ui/core';\nimport VisibilityIcon from '@material-ui/icons/Visibility';\nimport CategoryIcon from '@material-ui/icons/Category';\nimport PlayArrowIcon from '@material-ui/icons/PlayArrow';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  card: {\n    height: '100%',\n    display: 'flex',\n    flexDirection: 'column',\n  },\n  selectedCard: {\n    border: '2px solid #3f51b5',\n  },\n  unavailableCard: {\n    opacity: 0.6,\n  },\n  cardContent: {\n    flexGrow: 1,\n  },\n  chipContainer: {\n    marginBottom: theme.spacing(1.5),\n  },\n  successChip: {\n    backgroundColor: '#34C759',\n    color: '#fff',\n  },\n  errorChip: {\n    backgroundColor: '#FF3B3F',\n    color: '#fff',\n  },\n  modelType: {\n    marginTop: theme.spacing(1),\n  },\n  processButton: {\n    marginTop: theme.spacing(3),\n    textAlign: 'center',\n  }\n}));\n\nconst ModelSelector = ({ \n  onModelSelect, \n  onProcess, \n  isProcessing, \n  modelsStatus, \n  selectedModel,\n  imageSelected \n}) => {\n  const classes = useStyles();\n  \n  const models = [\n    {\n      id: 'yolo',\n      name: 'YOLOv8',\n      description: 'Fast and accurate object detection',\n      icon: <VisibilityIcon />,\n      available: modelsStatus.yolo\n    },\n    {\n      id: 'detr',\n      name: 'DETR',\n      description: 'DEtection TRansformer for object detection',\n      icon: <VisibilityIcon />,\n      available: modelsStatus.detr\n    },\n    {\n      id: 'vit',\n      name: 'ViT',\n      description: 'Vision Transformer for image classification',\n      icon: <CategoryIcon />,\n      available: modelsStatus.vit\n    }\n  ];\n\n  const handleModelClick = (modelId) => {\n    if (models.find(m => m.id === modelId).available) {\n      onModelSelect(modelId);\n    }\n  };\n\n  return (\n    <Box sx={{ p: 2, height: '100%' }}>\n      <Typography variant=\"h6\" gutterBottom>\n        Select Model\n      </Typography>\n      \n      <Grid container spacing={2}>\n        {models.map((model) => (\n          <Grid item xs={12} sm={4} key={model.id}>\n            <Card \n              className={`\n                ${classes.card} \n                ${selectedModel === model.id ? classes.selectedCard : ''} \n                ${!model.available ? classes.unavailableCard : ''}\n              `}\n              onClick={() => handleModelClick(model.id)}\n            >\n              <CardContent className={classes.cardContent}>\n                <Box sx={{ mb: 2, color: 'primary' }}>\n                  {model.icon}\n                </Box>\n                <Typography variant=\"h5\" component=\"div\" gutterBottom>\n                  {model.name}\n                </Typography>\n                <div className={classes.chipContainer}>\n                  {model.available ? (\n                    <Chip \n                      label=\"Available\" \n                      className={classes.successChip}\n                      size=\"small\" \n                    />\n                  ) : (\n                    <Chip \n                      label=\"Not Available\" \n                      className={classes.errorChip}\n                      size=\"small\" \n                    />\n                  )}\n                </div>\n                <Typography variant=\"body2\" color=\"textSecondary\">\n                  {model.description}\n                </Typography>\n              </CardContent>\n              <CardActions>\n                <Button \n                  size=\"small\" \n                  onClick={() => handleModelClick(model.id)}\n                  disabled={!model.available}\n                  color={selectedModel === model.id ? \"primary\" : \"default\"}\n                  variant={selectedModel === model.id ? \"contained\" : \"outlined\"}\n                  fullWidth\n                >\n                  {selectedModel === model.id ? 'Selected' : 'Select'}\n                </Button>\n              </CardActions>\n            </Card>\n          </Grid>\n        ))}\n      </Grid>\n\n      <div className={classes.processButton}>\n        <Button\n          variant=\"contained\"\n          color=\"primary\"\n          size=\"large\"\n          startIcon={<PlayArrowIcon />}\n          onClick={onProcess}\n          disabled={!selectedModel || !imageSelected || isProcessing}\n        >\n          {isProcessing ? 'Processing...' : 'Process Image'}\n        </Button>\n      </div>\n    </Box>\n  );\n};\n\nexport default ModelSelector;\n","import React, { useState } from 'react';\nimport { \n  Button, \n  Box, \n  Typography, \n  CircularProgress, \n  Snackbar,\n  Dialog,\n  DialogTitle,\n  DialogContent,\n  DialogActions,\n  TextField,\n  FormControl,\n  InputLabel,\n  Select,\n  MenuItem,\n  Grid,\n  Card,\n  CardMedia,\n  CardContent,\n  Chip\n} from '@material-ui/core';\nimport { Alert } from '@material-ui/lab';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  root: {\n    marginTop: theme.spacing(2),\n    marginBottom: theme.spacing(2),\n    padding: theme.spacing(2),\n    backgroundColor: '#f5f5f5',\n    borderRadius: theme.shape.borderRadius,\n  },\n  button: {\n    marginRight: theme.spacing(2),\n  },\n  searchDialog: {\n    minWidth: '500px',\n  },\n  formControl: {\n    marginBottom: theme.spacing(2),\n    minWidth: '100%',\n  },\n  searchResults: {\n    marginTop: theme.spacing(2),\n  },\n  resultCard: {\n    marginBottom: theme.spacing(2),\n  },\n  resultImage: {\n    height: 140,\n    objectFit: 'contain',\n  },\n  chip: {\n    margin: theme.spacing(0.5),\n  },\n  similarityChip: {\n    backgroundColor: theme.palette.primary.main,\n    color: 'white',\n  }\n}));\n\nconst VectorDBActions = ({ results }) => {\n  const classes = useStyles();\n  const [isSaving, setIsSaving] = useState(false);\n  const [saveSuccess, setSaveSuccess] = useState(false);\n  const [saveError, setSaveError] = useState(null);\n  const [openSearchDialog, setOpenSearchDialog] = useState(false);\n  const [searchType, setSearchType] = useState('image');\n  const [searchClass, setSearchClass] = useState('');\n  const [searchResults, setSearchResults] = useState([]);\n  const [isSearching, setIsSearching] = useState(false);\n  const [searchError, setSearchError] = useState(null);\n  \n  // Extract model and data from results\n  const { model, data } = results;\n  \n  // Handle saving to vector DB\n  const handleSaveToVectorDB = async () => {\n    setIsSaving(true);\n    setSaveError(null);\n    \n    try {\n      let response;\n      \n      if (model === 'vit') {\n        // For ViT, save the whole image with classifications\n        response = await fetch('/api/add-to-collection', {\n          method: 'POST',\n          headers: {\n            'Content-Type': 'application/json',\n          },\n          body: JSON.stringify({\n            image: data.image,\n            metadata: {\n              model: 'vit',\n              classifications: data.classifications\n            }\n          })\n        });\n      } else {\n        // For YOLO and DETR, save detected objects\n        response = await fetch('/api/add-detected-objects', {\n          method: 'POST',\n          headers: {\n            'Content-Type': 'application/json',\n          },\n          body: JSON.stringify({\n            image: data.image,\n            objects: data.detections,\n            imageId: generateUUID()\n          })\n        });\n      }\n      \n      if (!response.ok) {\n        throw new Error(`HTTP error! Status: ${response.status}`);\n      }\n      \n      const result = await response.json();\n      \n      if (result.error) {\n        throw new Error(result.error);\n      }\n      \n      setSaveSuccess(true);\n      setTimeout(() => setSaveSuccess(false), 5000);\n    } catch (err) {\n      console.error('Error saving to vector DB:', err);\n      setSaveError(`Error saving to vector DB: ${err.message}`);\n    } finally {\n      setIsSaving(false);\n    }\n  };\n  \n  // Handle opening search dialog\n  const handleOpenSearchDialog = () => {\n    setOpenSearchDialog(true);\n    setSearchResults([]);\n    setSearchError(null);\n  };\n  \n  // Handle closing search dialog\n  const handleCloseSearchDialog = () => {\n    setOpenSearchDialog(false);\n  };\n  \n  // Handle search type change\n  const handleSearchTypeChange = (event) => {\n    setSearchType(event.target.value);\n    setSearchResults([]);\n    setSearchError(null);\n  };\n  \n  // Handle search class change\n  const handleSearchClassChange = (event) => {\n    setSearchClass(event.target.value);\n  };\n  \n  // Handle search\n  const handleSearch = async () => {\n    setIsSearching(true);\n    setSearchError(null);\n    \n    try {\n      let requestBody = {};\n      \n      if (searchType === 'image') {\n        // Search by current image\n        requestBody = {\n          searchType: 'image',\n          image: data.image,\n          n_results: 5\n        };\n      } else {\n        // Search by class name\n        if (!searchClass.trim()) {\n          throw new Error('Please enter a class name');\n        }\n        \n        requestBody = {\n          searchType: 'class',\n          class_name: searchClass.trim(),\n          n_results: 5\n        };\n      }\n      \n      const response = await fetch('/api/search-similar-objects', {\n        method: 'POST',\n        headers: {\n          'Content-Type': 'application/json',\n        },\n        body: JSON.stringify(requestBody)\n      });\n      \n      if (!response.ok) {\n        throw new Error(`HTTP error! Status: ${response.status}`);\n      }\n      \n      const result = await response.json();\n      \n      if (result.error) {\n        throw new Error(result.error);\n      }\n      \n      console.log('Search API response:', result);\n      \n      // The backend responds with {success, searchType, results} structure, so extract only the results array\n      if (result.success && Array.isArray(result.results)) {\n        console.log('Setting search results array:', result.results);\n        console.log('Results array length:', result.results.length);\n        console.log('First result item:', result.results[0]);\n        setSearchResults(result.results);\n      } else {\n        console.error('Unexpected API response format:', result);\n        throw new Error('Unexpected API response format');\n      }\n    } catch (err) {\n      console.error('Error searching vector DB:', err);\n      setSearchError(`Error searching vector DB: ${err.message}`);\n    } finally {\n      setIsSearching(false);\n    }\n  };\n  \n  // Generate UUID for image ID\n  const generateUUID = () => {\n    return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, function(c) {\n      const r = Math.random() * 16 | 0;\n      const v = c === 'x' ? r : (r & 0x3 | 0x8);\n      return v.toString(16);\n    });\n  };\n  \n  // Render search results\n  const renderSearchResults = () => {\n    console.log('Rendering search results:', searchResults);\n    console.log('Search results length:', searchResults.length);\n    \n    if (searchResults.length === 0) {\n      console.log('No results to render');\n      return (\n        <Typography variant=\"body1\">No results found.</Typography>\n      );\n    }\n    \n    return (\n      <Grid container spacing={2}>\n        {searchResults.map((result, index) => {\n          const similarity = (1 - result.distance) * 100;\n          \n          return (\n            <Grid item xs={12} sm={6} key={index}>\n              <Card className={classes.resultCard}>\n                {result.metadata && result.metadata.image_data ? (\n                  <CardMedia\n                    className={classes.resultImage}\n                    component=\"img\"\n                    height=\"200\"\n                    image={`data:image/jpeg;base64,${result.metadata.image_data}`}\n                    alt={result.metadata && result.metadata.class ? result.metadata.class : 'Object'}\n                  />\n                ) : (\n                  <Box \n                    className={classes.resultImage}\n                    style={{ \n                      backgroundColor: '#f0f0f0', \n                      display: 'flex', \n                      alignItems: 'center', \n                      justifyContent: 'center',\n                      height: 200\n                    }}\n                  >\n                    <Typography variant=\"body2\" color=\"textSecondary\">\n                      {result.metadata && result.metadata.class ? result.metadata.class : 'Object'} Image\n                    </Typography>\n                  </Box>\n                )}\n                <CardContent>\n                  <Box display=\"flex\" justifyContent=\"space-between\" alignItems=\"center\" mb={1}>\n                    <Typography variant=\"subtitle1\">Result #{index + 1}</Typography>\n                    <Chip \n                      label={`Similarity: ${similarity.toFixed(2)}%`}\n                      className={classes.similarityChip}\n                      size=\"small\"\n                    />\n                  </Box>\n                  <Typography variant=\"body2\" color=\"textSecondary\">\n                    <strong>Class:</strong> {result.metadata.class || 'N/A'}\n                  </Typography>\n                  {result.metadata.confidence && (\n                    <Typography variant=\"body2\" color=\"textSecondary\">\n                      <strong>Confidence:</strong> {(result.metadata.confidence * 100).toFixed(2)}%\n                    </Typography>\n                  )}\n                  <Typography variant=\"body2\" color=\"textSecondary\">\n                    <strong>Object ID:</strong> {result.id}\n                  </Typography>\n                </CardContent>\n              </Card>\n            </Grid>\n          );\n        })}\n      </Grid>\n    );\n  };\n  \n  return (\n    <Box className={classes.root}>\n      <Typography variant=\"h6\" gutterBottom>\n        Vector Database Actions\n      </Typography>\n      \n      <Box display=\"flex\" alignItems=\"center\" mb={2}>\n        <Button\n          variant=\"contained\"\n          color=\"primary\"\n          onClick={handleSaveToVectorDB}\n          disabled={isSaving}\n          className={classes.button}\n        >\n          {isSaving ? (\n            <>\n              <CircularProgress size={20} color=\"inherit\" style={{ marginRight: 8 }} />\n              Saving...\n            </>\n          ) : (\n            'Save to Vector DB'\n          )}\n        </Button>\n        \n        <Button\n          variant=\"outlined\"\n          color=\"primary\"\n          onClick={handleOpenSearchDialog}\n          className={classes.button}\n        >\n          Search Similar\n        </Button>\n      </Box>\n      \n      {saveError && (\n        <Alert severity=\"error\" style={{ marginTop: 8 }}>\n          {saveError}\n        </Alert>\n      )}\n      \n      <Snackbar open={saveSuccess} autoHideDuration={5000} onClose={() => setSaveSuccess(false)}>\n        <Alert severity=\"success\">\n          {model === 'vit' ? (\n            'Image and classifications successfully saved to vector DB!'\n          ) : (\n            'Detected objects successfully saved to vector DB!'\n          )}\n        </Alert>\n      </Snackbar>\n      \n      {/* Search Dialog */}\n      <Dialog\n        open={openSearchDialog}\n        onClose={handleCloseSearchDialog}\n        maxWidth=\"md\"\n        fullWidth\n      >\n        <DialogTitle>Search Vector Database</DialogTitle>\n        <DialogContent>\n          <FormControl className={classes.formControl}>\n            <InputLabel id=\"search-type-label\">Search Type</InputLabel>\n            <Select\n              labelId=\"search-type-label\"\n              id=\"search-type\"\n              value={searchType}\n              onChange={handleSearchTypeChange}\n            >\n              <MenuItem value=\"image\">Search by Current Image</MenuItem>\n              <MenuItem value=\"class\">Search by Class Name</MenuItem>\n            </Select>\n          </FormControl>\n          \n          {searchType === 'class' && (\n            <FormControl className={classes.formControl}>\n              <TextField\n                label=\"Class Name\"\n                value={searchClass}\n                onChange={handleSearchClassChange}\n                placeholder=\"e.g. person, car, dog...\"\n                fullWidth\n              />\n            </FormControl>\n          )}\n          \n          {searchError && (\n            <Alert severity=\"error\" style={{ marginBottom: 16 }}>\n              {searchError}\n            </Alert>\n          )}\n          \n          <Box className={classes.searchResults}>\n            {isSearching ? (\n              <Box display=\"flex\" justifyContent=\"center\" alignItems=\"center\" p={4}>\n                <CircularProgress />\n                <Typography variant=\"body1\" style={{ marginLeft: 16 }}>\n                  Searching...\n                </Typography>\n              </Box>\n            ) : (\n              <>\n                {console.log('Search dialog render - searchResults:', searchResults)}\n                {searchResults.length > 0 ? renderSearchResults() : \n                  <Typography variant=\"body1\">No results found. Please try another search.</Typography>\n                }\n              </>\n            )}\n          </Box>\n        </DialogContent>\n        <DialogActions>\n          <Button onClick={handleCloseSearchDialog} color=\"default\">\n            Close\n          </Button>\n          <Button \n            onClick={handleSearch} \n            color=\"primary\" \n            variant=\"contained\"\n            disabled={isSearching || (searchType === 'class' && !searchClass.trim())}\n          >\n            Search\n          </Button>\n        </DialogActions>\n      </Dialog>\n    </Box>\n  );\n};\n\nexport default VectorDBActions;\n","import React from 'react';\nimport { \n  Paper, \n  Typography, \n  Box, \n  List, \n  ListItem, \n  ListItemText, \n  Divider,\n  Grid,\n  Chip\n} from '@material-ui/core';\nimport VectorDBActions from './VectorDBActions';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  paper: {\n    padding: theme.spacing(2)\n  },\n  marginBottom: {\n    marginBottom: theme.spacing(2)\n  },\n  resultImage: {\n    maxWidth: '100%',\n    maxHeight: '400px',\n    objectFit: 'contain'\n  },\n  dividerMargin: {\n    margin: `${theme.spacing(2)}px 0`\n  },\n  chipContainer: {\n    display: 'flex',\n    gap: theme.spacing(1),\n    flexWrap: 'wrap'\n  }\n}));\n\nconst ResultDisplay = ({ results }) => {\n  const classes = useStyles();\n  if (!results) return null;\n  \n  const { model, data } = results;\n  \n  // Helper to format times nicely\n  const formatTime = (ms) => {\n    if (ms === undefined || ms === null || isNaN(ms)) return '-';\n    const num = Number(ms);\n    if (num < 1000) return `${num.toFixed(2)} ms`;\n    return `${(num / 1000).toFixed(2)} s`;\n  };\n  \n  // Check if there's an error\n  if (data.error) {\n    return (\n      <Paper sx={{ p: 2, bgcolor: '#ffebee' }}>\n        <Typography color=\"error\">{data.error}</Typography>\n      </Paper>\n    );\n  }\n\n  // Display performance info\n  const renderPerformanceInfo = () => {\n    if (!data.performance) return null;\n    \n    return (\n      <Box className=\"performance-info\">\n        <Divider className={classes.dividerMargin} />\n        <Typography variant=\"body2\">\n          Inference time: {formatTime(data.performance.inference_time)} on {data.performance.device}\n        </Typography>\n      </Box>\n    );\n  };\n\n  // Render for YOLO and DETR (object detection)\n  if (model === 'yolo' || model === 'detr') {\n    return (\n      <Paper className={classes.paper}>\n        <Typography variant=\"h6\" gutterBottom>\n          {model === 'yolo' ? 'YOLOv8' : 'DETR'} Detection Results\n        </Typography>\n        \n        <Grid container spacing={3}>\n          <Grid item xs={12} md={6}>\n            {data.image && (\n              <Box className={classes.marginBottom}>\n                <Typography variant=\"subtitle1\" gutterBottom>\n                  Detection Result\n                </Typography>\n                <img \n                  src={`data:image/png;base64,${data.image}`} \n                  alt=\"Detection Result\" \n                  className={classes.resultImage}\n                />\n              </Box>\n            )}\n          </Grid>\n          \n          <Grid item xs={12} md={6}>\n            <Box className={classes.marginBottom}>\n              <Typography variant=\"subtitle1\" gutterBottom>\n                Detected Objects:\n              </Typography>\n              \n              {data.detections && data.detections.length > 0 ? (\n                <List>\n                  {data.detections.map((detection, index) => (\n                    <React.Fragment key={index}>\n                      <ListItem>\n                        <ListItemText \n                          primary={\n                            <Box style={{ display: 'flex', alignItems: 'center' }}>\n                              <Typography variant=\"body1\" component=\"span\">\n                                {detection.class}\n                              </Typography>\n                              <Chip \n                                label={`${(detection.confidence * 100).toFixed(0)}%`}\n                                size=\"small\"\n                                color=\"primary\"\n                                style={{ marginLeft: 8 }}\n                              />\n                            </Box>\n                          } \n                          secondary={`Bounding Box: [${detection.bbox.join(', ')}]`} \n                        />\n                      </ListItem>\n                      {index < data.detections.length - 1 && <Divider />}\n                    </React.Fragment>\n                  ))}\n                </List>\n              ) : (\n                <Typography variant=\"body1\">No objects detected</Typography>\n              )}\n            </Box>\n          </Grid>\n        </Grid>\n        \n        {renderPerformanceInfo()}\n        \n        {/* Vector DB Actions for Object Detection */}\n        <VectorDBActions results={results} />\n      </Paper>\n    );\n  }\n  \n  // Render for ViT (classification)\n  if (model === 'vit') {\n    return (\n      <Paper className={classes.paper}>\n        <Typography variant=\"h6\" gutterBottom>\n          ViT Classification Results\n        </Typography>\n        \n        <Typography variant=\"subtitle1\" gutterBottom>\n          Top Predictions:\n        </Typography>\n        \n        {data.top_predictions && data.top_predictions.length > 0 ? (\n          <List>\n            {data.top_predictions.map((prediction, index) => (\n              <React.Fragment key={index}>\n                <ListItem>\n                  <ListItemText \n                    primary={\n                      <Box style={{ display: 'flex', alignItems: 'center' }}>\n                        <Typography variant=\"body1\" component=\"span\">\n                          {prediction.rank}. {prediction.class}\n                        </Typography>\n                        <Chip \n                          label={`${(prediction.probability * 100).toFixed(1)}%`}\n                          size=\"small\"\n                          color={index === 0 ? \"primary\" : \"default\"}\n                          style={{ marginLeft: 8 }}\n                        />\n                      </Box>\n                    } \n                  />\n                </ListItem>\n                {index < data.top_predictions.length - 1 && <Divider />}\n              </React.Fragment>\n            ))}\n          </List>\n        ) : (\n          <Typography variant=\"body1\">No classifications available</Typography>\n        )}\n        \n        {renderPerformanceInfo()}\n        \n        {/* Vector DB Actions for ViT Classification */}\n        <VectorDBActions results={results} />\n      </Paper>\n    );\n  }\n  \n  return null;\n};\n\nexport default ResultDisplay;\n","import React, { useState } from 'react';\nimport { \n  Paper, \n  Typography, \n  Box, \n  TextField, \n  Button, \n  CircularProgress,\n  Divider\n} from '@material-ui/core';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  paper: {\n    padding: theme.spacing(2),\n    marginTop: theme.spacing(2)\n  },\n  marginBottom: {\n    marginBottom: theme.spacing(2)\n  },\n  dividerMargin: {\n    margin: `${theme.spacing(2)}px 0`\n  },\n  responseBox: {\n    padding: theme.spacing(2),\n    backgroundColor: '#f5f5f5',\n    borderRadius: theme.shape.borderRadius,\n    marginTop: theme.spacing(2),\n    whiteSpace: 'pre-wrap'\n  },\n  buttonProgress: {\n    marginLeft: theme.spacing(1)\n  }\n}));\n\nconst LlmAnalysis = ({ visionResults, model }) => {\n  const classes = useStyles();\n  const [userQuery, setUserQuery] = useState('');\n  const [isAnalyzing, setIsAnalyzing] = useState(false);\n  const [analysisResult, setAnalysisResult] = useState(null);\n  const [error, setError] = useState(null);\n\n  // Format time for display\n  const formatTime = (ms) => {\n    if (ms === undefined || ms === null || isNaN(ms)) return '-';\n    const num = Number(ms);\n    if (num < 1000) return `${num.toFixed(2)} ms`;\n    return `${(num / 1000).toFixed(2)} s`;\n  };\n\n  const handleAnalyze = async () => {\n    if (!userQuery.trim()) return;\n    \n    setIsAnalyzing(true);\n    setError(null);\n    \n    try {\n      const response = await fetch('/api/analyze', {\n        method: 'POST',\n        headers: {\n          'Content-Type': 'application/json',\n        },\n        body: JSON.stringify({\n          visionResults: visionResults,\n          userQuery: userQuery\n        }),\n      });\n\n      if (!response.ok) {\n        throw new Error(`HTTP error! Status: ${response.status}`);\n      }\n\n      const data = await response.json();\n      \n      if (data.error) {\n        setError(data.error);\n      } else {\n        setAnalysisResult(data);\n      }\n    } catch (err) {\n      console.error('Error analyzing with LLM:', err);\n      setError(`Error analyzing with LLM: ${err.message}`);\n    } finally {\n      setIsAnalyzing(false);\n    }\n  };\n\n  if (!visionResults) return null;\n\n  return (\n    <Paper className={classes.paper}>\n      <Typography variant=\"h6\" gutterBottom>\n        Ask AI about the {model === 'vit' ? 'Classification' : 'Detection'} Results\n      </Typography>\n      \n      <Typography variant=\"body2\" className={classes.marginBottom}>\n        Ask a question about the detected objects or classifications to get an AI-powered analysis.\n      </Typography>\n      \n      <TextField\n        fullWidth\n        label=\"Your question about the image\"\n        variant=\"outlined\"\n        value={userQuery}\n        onChange={(e) => setUserQuery(e.target.value)}\n        disabled={isAnalyzing}\n        className={classes.marginBottom}\n        placeholder={model === 'vit' \n          ? \"E.g., What category does this image belong to?\" \n          : \"E.g., How many people are in this image?\"}\n      />\n      \n      <Button \n        variant=\"contained\" \n        color=\"primary\"\n        onClick={handleAnalyze}\n        disabled={isAnalyzing || !userQuery.trim()}\n      >\n        Analyze with AI\n        {isAnalyzing && <CircularProgress size={24} className={classes.buttonProgress} />}\n      </Button>\n      \n      {error && (\n        <Box mt={2}>\n          <Typography color=\"error\">{error}</Typography>\n        </Box>\n      )}\n      \n      {analysisResult && (\n        <>\n          <Divider className={classes.dividerMargin} />\n          \n          <Typography variant=\"subtitle1\" gutterBottom>\n            AI Analysis:\n          </Typography>\n          \n          <Box className={classes.responseBox}>\n            <Typography variant=\"body1\">\n              {analysisResult.response}\n            </Typography>\n          </Box>\n          \n          {analysisResult.performance && (\n            <Box mt={1}>\n              <Typography variant=\"body2\" color=\"textSecondary\">\n                Analysis time: {formatTime(analysisResult.performance.inference_time)} on {analysisResult.performance.device}\n              </Typography>\n            </Box>\n          )}\n        </>\n      )}\n    </Paper>\n  );\n};\n\nexport default LlmAnalysis;\n","import React, { useState } from 'react';\nimport { \n  Paper,\n  Typography,\n  Grid,\n  TextField,\n  Button,\n  Divider\n} from '@material-ui/core';\n\nfunction OpenAIChat() {\n  const [model, setModel] = useState('gpt-4');\n  const [apiKey, setApiKey] = useState('');\n  const [system, setSystem] = useState('You are a helpful assistant.');\n  const [prompt, setPrompt] = useState('');\n  const [response, setResponse] = useState('');\n  const [loading, setLoading] = useState(false);\n  const [error, setError] = useState('');\n\n  const onSend = async () => {\n    setError('');\n    setResponse('');\n    const p = (prompt || '').trim();\n    if (!p) { setError('Please enter a question.'); return; }\n\n    setLoading(true);\n    try {\n      const res = await fetch('/api/openai/chat', {\n        method: 'POST',\n        headers: { 'Content-Type': 'application/json' },\n        credentials: 'include',\n        body: JSON.stringify({\n          prompt: p,\n          model: (model || '').trim() || 'gpt-4',\n          api_key: (apiKey || undefined),\n          system: (system || undefined)\n        })\n      });\n\n      if (!res.ok) {\n        let txt = await res.text();\n        try { txt = JSON.stringify(JSON.parse(txt), null, 2); } catch {}\n        throw new Error(txt);\n      }\n      const data = await res.json();\n      const meta = `Model: ${data.model} | Latency: ${data.latency_sec}s` + (data.usage ? ` | Usage: ${JSON.stringify(data.usage)}` : '');\n      setResponse((data.response || '(Empty response)') + '\\n\\n---\\n' + meta);\n    } catch (e) {\n      setError('Error: ' + e.message);\n    } finally {\n      setLoading(false);\n    }\n  };\n\n  const onClear = () => {\n    setPrompt('');\n    setResponse('');\n    setError('');\n  };\n\n  return (\n    <Paper style={{ padding: 16 }}>\n      <Typography variant=\"h5\" gutterBottom>\n        OpenAI Chat (OpenAI API)\n      </Typography>\n      <Typography variant=\"body2\" color=\"textSecondary\" gutterBottom>\n        If the server env var OPENAI_API_KEY is set, the API Key field is optional.\n      </Typography>\n      <Grid container spacing={2}>\n        <Grid item xs={12} md={6}>\n          <TextField\n            label=\"Model\"\n            value={model}\n            onChange={(e) => setModel(e.target.value)}\n            fullWidth\n            variant=\"outlined\"\n            size=\"small\"\n          />\n        </Grid>\n        <Grid item xs={12} md={6}>\n          <TextField\n            label=\"OpenAI API Key (optional)\"\n            value={apiKey}\n            onChange={(e) => setApiKey(e.target.value)}\n            fullWidth\n            variant=\"outlined\"\n            size=\"small\"\n            type=\"password\"\n            placeholder=\"sk-...\"\n          />\n        </Grid>\n        <Grid item xs={12}>\n          <TextField\n            label=\"System Prompt (optional)\"\n            value={system}\n            onChange={(e) => setSystem(e.target.value)}\n            fullWidth\n            variant=\"outlined\"\n            size=\"small\"\n          />\n        </Grid>\n        <Grid item xs={12}>\n          <TextField\n            label=\"User Question\"\n            value={prompt}\n            onChange={(e) => setPrompt(e.target.value)}\n            fullWidth\n            multiline\n            rows={4}\n            variant=\"outlined\"\n          />\n        </Grid>\n        {error && (\n          <Grid item xs={12}>\n            <Typography color=\"error\">{error}</Typography>\n          </Grid>\n        )}\n        <Grid item xs={12}>\n          <div style={{ display: 'flex', gap: 8 }}>\n            <Button color=\"primary\" variant=\"contained\" onClick={onSend} disabled={loading}>\n              {loading ? 'Sending...' : 'Send Question'}\n            </Button>\n            <Button variant=\"outlined\" onClick={onClear}>Clear</Button>\n          </div>\n        </Grid>\n        <Grid item xs={12}>\n          <Divider style={{ margin: '12px 0' }} />\n          <Typography variant=\"subtitle2\" color=\"textSecondary\">Response</Typography>\n          <pre style={{ whiteSpace: 'pre-wrap', fontFamily: 'ui-monospace, monospace' }}>{response}</pre>\n        </Grid>\n      </Grid>\n    </Paper>\n  );\n}\n\nexport default OpenAIChat;\n","import React, { useState, useEffect } from 'react';\nimport { \n  Container, \n  Typography, \n  Box, \n  Paper, \n  Grid, \n  CircularProgress,\n  AppBar,\n  Toolbar,\n  ThemeProvider,\n  createMuiTheme\n} from '@material-ui/core';\nimport ImageUploader from './components/ImageUploader';\nimport ModelSelector from './components/ModelSelector';\nimport ResultDisplay from './components/ResultDisplay';\nimport LlmAnalysis from './components/LlmAnalysis';\nimport OpenAIChat from './components/OpenAIChat';\nimport './App.css';\n\n// Create a theme\nconst theme = createMuiTheme({\n  palette: {\n    primary: {\n      main: '#3f51b5',\n    },\n    secondary: {\n      main: '#f50057',\n    },\n  },\n  typography: {\n    fontFamily: 'Roboto, Arial, sans-serif',\n  },\n});\n\nfunction App() {\n  const [selectedImage, setSelectedImage] = useState(null);\n  const [selectedModel, setSelectedModel] = useState('');\n  const [isProcessing, setIsProcessing] = useState(false);\n  const [results, setResults] = useState(null);\n  const [error, setError] = useState(null);\n  const [modelsStatus, setModelsStatus] = useState({\n    yolo: false,\n    detr: false,\n    vit: false\n  });\n\n  // Check API status on component mount\n  useEffect(() => {\n    fetch('/api/status')\n      .then(response => response.json())\n      .then(data => {\n        setModelsStatus(data.models);\n      })\n      .catch(err => {\n        console.error('Error checking API status:', err);\n        setError('Error connecting to the backend API. Please make sure the server is running.');\n      });\n  }, []);\n\n  const handleImageUpload = (image) => {\n    setSelectedImage(image);\n    setResults(null);\n    setError(null);\n  };\n\n  const handleModelSelect = (model) => {\n    setSelectedModel(model);\n    setResults(null);\n    setError(null);\n  };\n\n  const processImage = async () => {\n    if (!selectedImage || !selectedModel) {\n      setError('Please select both an image and a model');\n      return;\n    }\n\n    setIsProcessing(true);\n    setError(null);\n\n    // Create form data for the image\n    const formData = new FormData();\n    formData.append('image', selectedImage);\n\n    let endpoint = '';\n    switch (selectedModel) {\n      case 'yolo':\n        endpoint = '/api/detect/yolo';\n        break;\n      case 'detr':\n        endpoint = '/api/detect/detr';\n        break;\n      case 'vit':\n        endpoint = '/api/classify/vit';\n        break;\n      default:\n        setError('Invalid model selection');\n        setIsProcessing(false);\n        return;\n    }\n\n    try {\n      const response = await fetch(endpoint, {\n        method: 'POST',\n        body: formData,\n      });\n\n      if (!response.ok) {\n        throw new Error(`HTTP error! Status: ${response.status}`);\n      }\n\n      const data = await response.json();\n      setResults({ model: selectedModel, data });\n    } catch (err) {\n      console.error('Error processing image:', err);\n      setError(`Error processing image: ${err.message}`);\n    } finally {\n      setIsProcessing(false);\n    }\n  };\n\n  return (\n    <ThemeProvider theme={theme}>\n      <Box style={{ flexGrow: 1 }}>\n        <AppBar position=\"static\">\n          <Toolbar>\n            <Typography variant=\"h6\" style={{ flexGrow: 1 }}>\n              Multi-Model Object Detection Demo\n            </Typography>\n          </Toolbar>\n        </AppBar>\n        <Container maxWidth=\"lg\" style={{ marginTop: theme.spacing(4), marginBottom: theme.spacing(4) }}>\n          <Grid container spacing={3}>\n            <Grid item xs={12}>\n              <Paper style={{ padding: theme.spacing(2) }}>\n                <Typography variant=\"h5\" gutterBottom>\n                  Upload an image to see how each model performs!\n                </Typography>\n                <Typography variant=\"body1\" paragraph>\n                  This demo showcases three different object detection and image classification models:\n                </Typography>\n                <Typography variant=\"body1\" component=\"div\">\n                  <ul>\n                    <li><strong>YOLOv8</strong>: Fast and accurate object detection</li>\n                    <li><strong>DETR</strong>: DEtection TRansformer for object detection</li>\n                    <li><strong>ViT</strong>: Vision Transformer for image classification</li>\n                  </ul>\n                </Typography>\n              </Paper>\n            </Grid>\n            \n            <Grid item xs={12} md={6}>\n              <ImageUploader onImageUpload={handleImageUpload} />\n            </Grid>\n            \n            <Grid item xs={12} md={6}>\n              <ModelSelector \n                onModelSelect={handleModelSelect} \n                onProcess={processImage}\n                isProcessing={isProcessing}\n                modelsStatus={modelsStatus}\n                selectedModel={selectedModel}\n                imageSelected={!!selectedImage}\n              />\n            </Grid>\n            \n            {error && (\n              <Grid item xs={12}>\n                <Paper style={{ padding: theme.spacing(2), backgroundColor: '#ffebee' }}>\n                  <Typography color=\"error\">{error}</Typography>\n                </Paper>\n              </Grid>\n            )}\n            \n            {isProcessing && (\n              <Grid item xs={12} style={{ textAlign: 'center', margin: `${theme.spacing(4)}px 0` }}>\n                <CircularProgress />\n                <Typography variant=\"h6\" style={{ marginTop: theme.spacing(2) }}>\n                  Processing image...\n                </Typography>\n              </Grid>\n            )}\n            \n            {results && (\n              <>\n                <Grid item xs={12}>\n                  <ResultDisplay results={results} />\n                </Grid>\n                <Grid item xs={12}>\n                  <LlmAnalysis visionResults={results.data} model={results.model} />\n                </Grid>\n              </>\n            )}\n\n            {/* OpenAI Chat section at the end */}\n            <Grid item xs={12}>\n              <OpenAIChat />\n            </Grid>\n          </Grid>\n        </Container>\n      </Box>\n    </ThemeProvider>\n  );\n}\n\nexport default App;\n","const reportWebVitals = (onPerfEntry) => {\n  if (onPerfEntry && onPerfEntry instanceof Function) {\n    import('web-vitals').then(({ getCLS, getFID, getFCP, getLCP, getTTFB }) => {\n      getCLS(onPerfEntry);\n      getFID(onPerfEntry);\n      getFCP(onPerfEntry);\n      getLCP(onPerfEntry);\n      getTTFB(onPerfEntry);\n    });\n  }\n};\n\nexport default reportWebVitals;\n","import React from 'react';\nimport ReactDOM from 'react-dom';\nimport './index.css';\nimport App from './App';\nimport reportWebVitals from './reportWebVitals';\n\nReactDOM.render(\n  <React.StrictMode>\n    <App />\n  </React.StrictMode>,\n  document.getElementById('root')\n);\n\n// If you want to start measuring performance in your app, pass a function\n// to log results (for example: reportWebVitals(console.log))\n// or send to an analytics endpoint. Learn more: https://bit.ly/CRA-vitals\nreportWebVitals();\n"],"sourceRoot":""}

static/static/js/main.fe247ab2.chunk.js

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React, { useState, useRef } from 'react';\nimport { \n  Paper, \n  Typography, \n  Box, \n  Button, \n  IconButton \n} from '@material-ui/core';\nimport CloudUploadIcon from '@material-ui/icons/CloudUpload';\nimport DeleteIcon from '@material-ui/icons/Delete';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  paper: {\n    padding: theme.spacing(2),\n    display: 'flex',\n    flexDirection: 'column',\n    alignItems: 'center',\n    height: '100%',\n    minHeight: 300,\n    transition: 'all 0.3s ease'\n  },\n  dragActive: {\n    border: '2px dashed #3f51b5',\n    backgroundColor: 'rgba(63, 81, 181, 0.05)'\n  },\n  dragInactive: {\n    border: '2px dashed #ccc',\n    backgroundColor: 'white'\n  },\n  uploadBox: {\n    display: 'flex',\n    flexDirection: 'column',\n    alignItems: 'center',\n    justifyContent: 'center',\n    height: '100%',\n    width: '100%',\n    cursor: 'pointer'\n  },\n  uploadIcon: {\n    fontSize: 60,\n    color: '#3f51b5',\n    marginBottom: theme.spacing(2)\n  },\n  supportText: {\n    marginTop: theme.spacing(2)\n  },\n  previewBox: {\n    display: 'flex',\n    flexDirection: 'column',\n    alignItems: 'center',\n    width: '100%',\n    height: '100%',\n    position: 'relative'\n  },\n  imageContainer: {\n    position: 'relative',\n    width: '100%',\n    // Use viewport-based height so any aspect ratio fits inside\n    height: '60vh',\n    [theme.breakpoints.down('sm')]: {\n      height: '45vh',\n    },\n    display: 'flex',\n    justifyContent: 'center',\n    alignItems: 'center',\n    overflow: 'hidden',\n    marginTop: theme.spacing(2),\n  },\n  deleteButton: {\n    position: 'absolute',\n    top: 0,\n    right: 0,\n    backgroundColor: 'rgba(255, 255, 255, 0.7)',\n    '&:hover': {\n      backgroundColor: 'rgba(255, 255, 255, 0.9)',\n    }\n  }\n}));\n\nconst ImageUploader = ({ onImageUpload }) => {\n  const classes = useStyles();\n  const [previewUrl, setPreviewUrl] = useState(null);\n  const [dragActive, setDragActive] = useState(false);\n  const fileInputRef = useRef(null);\n\n  const handleDrag = (e) => {\n    e.preventDefault();\n    e.stopPropagation();\n    if (e.type === 'dragenter' || e.type === 'dragover') {\n      setDragActive(true);\n    } else if (e.type === 'dragleave') {\n      setDragActive(false);\n    }\n  };\n\n  const handleDrop = (e) => {\n    e.preventDefault();\n    e.stopPropagation();\n    setDragActive(false);\n    if (e.dataTransfer.files && e.dataTransfer.files[0]) {\n      handleFiles(e.dataTransfer.files[0]);\n    }\n  };\n\n  const handleChange = (e) => {\n    e.preventDefault();\n    if (e.target.files && e.target.files[0]) {\n      handleFiles(e.target.files[0]);\n    }\n  };\n\n  const handleFiles = (file) => {\n    if (file.type.startsWith('image/')) {\n      setPreviewUrl(URL.createObjectURL(file));\n      onImageUpload(file);\n    } else {\n      alert('Please upload an image file');\n    }\n  };\n\n  const onButtonClick = () => {\n    fileInputRef.current.click();\n  };\n\n  const handleRemoveImage = () => {\n    setPreviewUrl(null);\n    onImageUpload(null);\n    fileInputRef.current.value = \"\";\n  };\n\n  return (\n    <Paper \n      className={`${classes.paper} ${dragActive ? classes.dragActive : classes.dragInactive}`}\n      onDragEnter={handleDrag}\n      onDragLeave={handleDrag}\n      onDragOver={handleDrag}\n      onDrop={handleDrop}\n    >\n      <input\n        ref={fileInputRef}\n        type=\"file\"\n        accept=\"image/*\"\n        onChange={handleChange}\n        style={{ display: 'none' }}\n      />\n\n      {!previewUrl ? (\n        <Box \n          className={classes.uploadBox}\n          onClick={onButtonClick}\n        >\n          <CloudUploadIcon className={classes.uploadIcon} />\n          <Typography variant=\"h6\" gutterBottom>\n            Drag & Drop an image here\n          </Typography>\n          <Typography variant=\"body2\" color=\"textSecondary\" gutterBottom>\n            or\n          </Typography>\n          <Button\n            variant=\"contained\"\n            color=\"primary\"\n            component=\"span\"\n            startIcon={<CloudUploadIcon />}\n          >\n            Browse Files\n          </Button>\n          <Typography variant=\"body2\" color=\"textSecondary\" className={classes.supportText}>\n            Supported formats: JPG, PNG, GIF\n          </Typography>\n        </Box>\n      ) : (\n        <Box className={classes.previewBox}>\n          <Typography variant=\"h6\" gutterBottom>\n            Preview\n          </Typography>\n          <Box className={classes.imageContainer}>\n            <img\n              src={previewUrl}\n              alt=\"Preview\"\n              className=\"preview-image\"\n              style={{\n                width: '100%',\n                height: 'auto',\n                maxHeight: '100%',\n                objectFit: 'contain',\n                display: 'block'\n              }}\n            />\n            <IconButton\n              aria-label=\"delete\"\n              className={classes.deleteButton}\n              onClick={handleRemoveImage}\n            >\n              <DeleteIcon />\n            </IconButton>\n          </Box>\n        </Box>\n      )}\n    </Paper>\n  );\n};\n\nexport default ImageUploader;\n","import React from 'react';\nimport { \n  Grid, \n  Card, \n  CardContent, \n  CardActions, \n  Typography, \n  Button, \n  Chip,\n  Box\n} from '@material-ui/core';\nimport VisibilityIcon from '@material-ui/icons/Visibility';\nimport CategoryIcon from '@material-ui/icons/Category';\nimport PlayArrowIcon from '@material-ui/icons/PlayArrow';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  card: {\n    height: '100%',\n    display: 'flex',\n    flexDirection: 'column',\n  },\n  selectedCard: {\n    border: '2px solid #3f51b5',\n  },\n  unavailableCard: {\n    opacity: 0.6,\n  },\n  cardContent: {\n    flexGrow: 1,\n  },\n  chipContainer: {\n    marginBottom: theme.spacing(1.5),\n  },\n  successChip: {\n    backgroundColor: '#34C759',\n    color: '#fff',\n  },\n  errorChip: {\n    backgroundColor: '#FF3B3F',\n    color: '#fff',\n  },\n  modelType: {\n    marginTop: theme.spacing(1),\n  },\n  processButton: {\n    marginTop: theme.spacing(3),\n    textAlign: 'center',\n  }\n}));\n\nconst ModelSelector = ({ \n  onModelSelect, \n  onProcess, \n  isProcessing, \n  modelsStatus, \n  selectedModel,\n  imageSelected \n}) => {\n  const classes = useStyles();\n  \n  const models = [\n    {\n      id: 'yolo',\n      name: 'YOLOv8',\n      description: 'Fast and accurate object detection',\n      icon: <VisibilityIcon />,\n      available: modelsStatus.yolo\n    },\n    {\n      id: 'detr',\n      name: 'DETR',\n      description: 'DEtection TRansformer for object detection',\n      icon: <VisibilityIcon />,\n      available: modelsStatus.detr\n    },\n    {\n      id: 'vit',\n      name: 'ViT',\n      description: 'Vision Transformer for image classification',\n      icon: <CategoryIcon />,\n      available: modelsStatus.vit\n    }\n  ];\n\n  const handleModelClick = (modelId) => {\n    if (models.find(m => m.id === modelId).available) {\n      onModelSelect(modelId);\n    }\n  };\n\n  return (\n    <Box sx={{ p: 2, height: '100%' }}>\n      <Typography variant=\"h6\" gutterBottom>\n        Select Model\n      </Typography>\n      \n      <Grid container spacing={2}>\n        {models.map((model) => (\n          <Grid item xs={12} sm={4} key={model.id}>\n            <Card \n              className={`\n                ${classes.card} \n                ${selectedModel === model.id ? classes.selectedCard : ''} \n                ${!model.available ? classes.unavailableCard : ''}\n              `}\n              onClick={() => handleModelClick(model.id)}\n            >\n              <CardContent className={classes.cardContent}>\n                <Box sx={{ mb: 2, color: 'primary' }}>\n                  {model.icon}\n                </Box>\n                <Typography variant=\"h5\" component=\"div\" gutterBottom>\n                  {model.name}\n                </Typography>\n                <div className={classes.chipContainer}>\n                  {model.available ? (\n                    <Chip \n                      label=\"Available\" \n                      className={classes.successChip}\n                      size=\"small\" \n                    />\n                  ) : (\n                    <Chip \n                      label=\"Not Available\" \n                      className={classes.errorChip}\n                      size=\"small\" \n                    />\n                  )}\n                </div>\n                <Typography variant=\"body2\" color=\"textSecondary\">\n                  {model.description}\n                </Typography>\n              </CardContent>\n              <CardActions>\n                <Button \n                  size=\"small\" \n                  onClick={() => handleModelClick(model.id)}\n                  disabled={!model.available}\n                  color={selectedModel === model.id ? \"primary\" : \"default\"}\n                  variant={selectedModel === model.id ? \"contained\" : \"outlined\"}\n                  fullWidth\n                >\n                  {selectedModel === model.id ? 'Selected' : 'Select'}\n                </Button>\n              </CardActions>\n            </Card>\n          </Grid>\n        ))}\n      </Grid>\n\n      <div className={classes.processButton}>\n        <Button\n          variant=\"contained\"\n          color=\"primary\"\n          size=\"large\"\n          startIcon={<PlayArrowIcon />}\n          onClick={onProcess}\n          disabled={!selectedModel || !imageSelected || isProcessing}\n        >\n          {isProcessing ? 'Processing...' : 'Process Image'}\n        </Button>\n      </div>\n    </Box>\n  );\n};\n\nexport default ModelSelector;\n","import React, { useState } from 'react';\nimport { \n  Button, \n  Box, \n  Typography, \n  CircularProgress, \n  Snackbar,\n  Dialog,\n  DialogTitle,\n  DialogContent,\n  DialogActions,\n  TextField,\n  FormControl,\n  InputLabel,\n  Select,\n  MenuItem,\n  Grid,\n  Card,\n  CardMedia,\n  CardContent,\n  Chip\n} from '@material-ui/core';\nimport { Alert } from '@material-ui/lab';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  root: {\n    marginTop: theme.spacing(2),\n    marginBottom: theme.spacing(2),\n    padding: theme.spacing(2),\n    backgroundColor: '#f5f5f5',\n    borderRadius: theme.shape.borderRadius,\n  },\n  button: {\n    marginRight: theme.spacing(2),\n  },\n  searchDialog: {\n    minWidth: '500px',\n  },\n  formControl: {\n    marginBottom: theme.spacing(2),\n    minWidth: '100%',\n  },\n  searchResults: {\n    marginTop: theme.spacing(2),\n  },\n  resultCard: {\n    marginBottom: theme.spacing(2),\n  },\n  resultImage: {\n    height: 140,\n    objectFit: 'contain',\n  },\n  chip: {\n    margin: theme.spacing(0.5),\n  },\n  similarityChip: {\n    backgroundColor: theme.palette.primary.main,\n    color: 'white',\n  }\n}));\n\nconst VectorDBActions = ({ results }) => {\n  const classes = useStyles();\n  const [isSaving, setIsSaving] = useState(false);\n  const [saveSuccess, setSaveSuccess] = useState(false);\n  const [saveError, setSaveError] = useState(null);\n  const [openSearchDialog, setOpenSearchDialog] = useState(false);\n  const [searchType, setSearchType] = useState('image');\n  const [searchClass, setSearchClass] = useState('');\n  const [searchResults, setSearchResults] = useState([]);\n  const [isSearching, setIsSearching] = useState(false);\n  const [searchError, setSearchError] = useState(null);\n  \n  // Extract model and data from results\n  const { model, data } = results;\n  \n  // Handle saving to vector DB\n  const handleSaveToVectorDB = async () => {\n    setIsSaving(true);\n    setSaveError(null);\n    \n    try {\n      let response;\n      \n      if (model === 'vit') {\n        // For ViT, save the whole image with classifications\n        response = await fetch('/api/add-to-collection', {\n          method: 'POST',\n          headers: {\n            'Content-Type': 'application/json',\n          },\n          body: JSON.stringify({\n            image: data.image,\n            metadata: {\n              model: 'vit',\n              classifications: data.classifications\n            }\n          })\n        });\n      } else {\n        // For YOLO and DETR, save detected objects\n        response = await fetch('/api/add-detected-objects', {\n          method: 'POST',\n          headers: {\n            'Content-Type': 'application/json',\n          },\n          body: JSON.stringify({\n            image: data.image,\n            objects: data.detections,\n            imageId: generateUUID()\n          })\n        });\n      }\n      \n      if (!response.ok) {\n        throw new Error(`HTTP error! Status: ${response.status}`);\n      }\n      \n      const result = await response.json();\n      \n      if (result.error) {\n        throw new Error(result.error);\n      }\n      \n      setSaveSuccess(true);\n      setTimeout(() => setSaveSuccess(false), 5000);\n    } catch (err) {\n      console.error('Error saving to vector DB:', err);\n      setSaveError(`Error saving to vector DB: ${err.message}`);\n    } finally {\n      setIsSaving(false);\n    }\n  };\n  \n  // Handle opening search dialog\n  const handleOpenSearchDialog = () => {\n    setOpenSearchDialog(true);\n    setSearchResults([]);\n    setSearchError(null);\n  };\n  \n  // Handle closing search dialog\n  const handleCloseSearchDialog = () => {\n    setOpenSearchDialog(false);\n  };\n  \n  // Handle search type change\n  const handleSearchTypeChange = (event) => {\n    setSearchType(event.target.value);\n    setSearchResults([]);\n    setSearchError(null);\n  };\n  \n  // Handle search class change\n  const handleSearchClassChange = (event) => {\n    setSearchClass(event.target.value);\n  };\n  \n  // Handle search\n  const handleSearch = async () => {\n    setIsSearching(true);\n    setSearchError(null);\n    \n    try {\n      let requestBody = {};\n      \n      if (searchType === 'image') {\n        // Search by current image\n        requestBody = {\n          searchType: 'image',\n          image: data.image,\n          n_results: 5\n        };\n      } else {\n        // Search by class name\n        if (!searchClass.trim()) {\n          throw new Error('Please enter a class name');\n        }\n        \n        requestBody = {\n          searchType: 'class',\n          class_name: searchClass.trim(),\n          n_results: 5\n        };\n      }\n      \n      const response = await fetch('/api/search-similar-objects', {\n        method: 'POST',\n        headers: {\n          'Content-Type': 'application/json',\n        },\n        body: JSON.stringify(requestBody)\n      });\n      \n      if (!response.ok) {\n        throw new Error(`HTTP error! Status: ${response.status}`);\n      }\n      \n      const result = await response.json();\n      \n      if (result.error) {\n        throw new Error(result.error);\n      }\n      \n      console.log('Search API response:', result);\n      \n      // The backend responds with {success, searchType, results} structure, so extract only the results array\n      if (result.success && Array.isArray(result.results)) {\n        console.log('Setting search results array:', result.results);\n        console.log('Results array length:', result.results.length);\n        console.log('First result item:', result.results[0]);\n        setSearchResults(result.results);\n      } else {\n        console.error('Unexpected API response format:', result);\n        throw new Error('Unexpected API response format');\n      }\n    } catch (err) {\n      console.error('Error searching vector DB:', err);\n      setSearchError(`Error searching vector DB: ${err.message}`);\n    } finally {\n      setIsSearching(false);\n    }\n  };\n  \n  // Generate UUID for image ID\n  const generateUUID = () => {\n    return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, function(c) {\n      const r = Math.random() * 16 | 0;\n      const v = c === 'x' ? r : (r & 0x3 | 0x8);\n      return v.toString(16);\n    });\n  };\n  \n  // Render search results\n  const renderSearchResults = () => {\n    console.log('Rendering search results:', searchResults);\n    console.log('Search results length:', searchResults.length);\n    \n    if (searchResults.length === 0) {\n      console.log('No results to render');\n      return (\n        <Typography variant=\"body1\">No results found.</Typography>\n      );\n    }\n    \n    return (\n      <Grid container spacing={2}>\n        {searchResults.map((result, index) => {\n          const similarity = (1 - result.distance) * 100;\n          \n          return (\n            <Grid item xs={12} sm={6} key={index}>\n              <Card className={classes.resultCard}>\n                {result.metadata && result.metadata.image_data ? (\n                  <CardMedia\n                    className={classes.resultImage}\n                    component=\"img\"\n                    height=\"200\"\n                    image={`data:image/jpeg;base64,${result.metadata.image_data}`}\n                    alt={result.metadata && result.metadata.class ? result.metadata.class : 'Object'}\n                  />\n                ) : (\n                  <Box \n                    className={classes.resultImage}\n                    style={{ \n                      backgroundColor: '#f0f0f0', \n                      display: 'flex', \n                      alignItems: 'center', \n                      justifyContent: 'center',\n                      height: 200\n                    }}\n                  >\n                    <Typography variant=\"body2\" color=\"textSecondary\">\n                      {result.metadata && result.metadata.class ? result.metadata.class : 'Object'} Image\n                    </Typography>\n                  </Box>\n                )}\n                <CardContent>\n                  <Box display=\"flex\" justifyContent=\"space-between\" alignItems=\"center\" mb={1}>\n                    <Typography variant=\"subtitle1\">Result #{index + 1}</Typography>\n                    <Chip \n                      label={`Similarity: ${similarity.toFixed(2)}%`}\n                      className={classes.similarityChip}\n                      size=\"small\"\n                    />\n                  </Box>\n                  <Typography variant=\"body2\" color=\"textSecondary\">\n                    <strong>Class:</strong> {result.metadata.class || 'N/A'}\n                  </Typography>\n                  {result.metadata.confidence && (\n                    <Typography variant=\"body2\" color=\"textSecondary\">\n                      <strong>Confidence:</strong> {(result.metadata.confidence * 100).toFixed(2)}%\n                    </Typography>\n                  )}\n                  <Typography variant=\"body2\" color=\"textSecondary\">\n                    <strong>Object ID:</strong> {result.id}\n                  </Typography>\n                </CardContent>\n              </Card>\n            </Grid>\n          );\n        })}\n      </Grid>\n    );\n  };\n  \n  return (\n    <Box className={classes.root}>\n      <Typography variant=\"h6\" gutterBottom>\n        Vector Database Actions\n      </Typography>\n      \n      <Box display=\"flex\" alignItems=\"center\" mb={2}>\n        <Button\n          variant=\"contained\"\n          color=\"primary\"\n          onClick={handleSaveToVectorDB}\n          disabled={isSaving}\n          className={classes.button}\n        >\n          {isSaving ? (\n            <>\n              <CircularProgress size={20} color=\"inherit\" style={{ marginRight: 8 }} />\n              Saving...\n            </>\n          ) : (\n            'Save to Vector DB'\n          )}\n        </Button>\n        \n        <Button\n          variant=\"outlined\"\n          color=\"primary\"\n          onClick={handleOpenSearchDialog}\n          className={classes.button}\n        >\n          Search Similar\n        </Button>\n      </Box>\n      \n      {saveError && (\n        <Alert severity=\"error\" style={{ marginTop: 8 }}>\n          {saveError}\n        </Alert>\n      )}\n      \n      <Snackbar open={saveSuccess} autoHideDuration={5000} onClose={() => setSaveSuccess(false)}>\n        <Alert severity=\"success\">\n          {model === 'vit' ? (\n            'Image and classifications successfully saved to vector DB!'\n          ) : (\n            'Detected objects successfully saved to vector DB!'\n          )}\n        </Alert>\n      </Snackbar>\n      \n      {/* Search Dialog */}\n      <Dialog\n        open={openSearchDialog}\n        onClose={handleCloseSearchDialog}\n        maxWidth=\"md\"\n        fullWidth\n      >\n        <DialogTitle>Search Vector Database</DialogTitle>\n        <DialogContent>\n          <FormControl className={classes.formControl}>\n            <InputLabel id=\"search-type-label\">Search Type</InputLabel>\n            <Select\n              labelId=\"search-type-label\"\n              id=\"search-type\"\n              value={searchType}\n              onChange={handleSearchTypeChange}\n            >\n              <MenuItem value=\"image\">Search by Current Image</MenuItem>\n              <MenuItem value=\"class\">Search by Class Name</MenuItem>\n            </Select>\n          </FormControl>\n          \n          {searchType === 'class' && (\n            <FormControl className={classes.formControl}>\n              <TextField\n                label=\"Class Name\"\n                value={searchClass}\n                onChange={handleSearchClassChange}\n                placeholder=\"e.g. person, car, dog...\"\n                fullWidth\n              />\n            </FormControl>\n          )}\n          \n          {searchError && (\n            <Alert severity=\"error\" style={{ marginBottom: 16 }}>\n              {searchError}\n            </Alert>\n          )}\n          \n          <Box className={classes.searchResults}>\n            {isSearching ? (\n              <Box display=\"flex\" justifyContent=\"center\" alignItems=\"center\" p={4}>\n                <CircularProgress />\n                <Typography variant=\"body1\" style={{ marginLeft: 16 }}>\n                  Searching...\n                </Typography>\n              </Box>\n            ) : (\n              <>\n                {console.log('Search dialog render - searchResults:', searchResults)}\n                {searchResults.length > 0 ? renderSearchResults() : \n                  <Typography variant=\"body1\">No results found. Please try another search.</Typography>\n                }\n              </>\n            )}\n          </Box>\n        </DialogContent>\n        <DialogActions>\n          <Button onClick={handleCloseSearchDialog} color=\"default\">\n            Close\n          </Button>\n          <Button \n            onClick={handleSearch} \n            color=\"primary\" \n            variant=\"contained\"\n            disabled={isSearching || (searchType === 'class' && !searchClass.trim())}\n          >\n            Search\n          </Button>\n        </DialogActions>\n      </Dialog>\n    </Box>\n  );\n};\n\nexport default VectorDBActions;\n","import React from 'react';\nimport { \n  Paper, \n  Typography, \n  Box, \n  List, \n  ListItem, \n  ListItemText, \n  Divider,\n  Grid,\n  Chip\n} from '@material-ui/core';\nimport VectorDBActions from './VectorDBActions';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  paper: {\n    padding: theme.spacing(2)\n  },\n  marginBottom: {\n    marginBottom: theme.spacing(2)\n  },\n  resultImage: {\n    maxWidth: '100%',\n    maxHeight: '400px',\n    objectFit: 'contain'\n  },\n  dividerMargin: {\n    margin: `${theme.spacing(2)}px 0`\n  },\n  chipContainer: {\n    display: 'flex',\n    gap: theme.spacing(1),\n    flexWrap: 'wrap'\n  }\n}));\n\nconst ResultDisplay = ({ results }) => {\n  const classes = useStyles();\n  if (!results) return null;\n  \n  const { model, data } = results;\n  \n  // Helper to format times nicely\n  const formatTime = (ms) => {\n    if (ms === undefined || ms === null || isNaN(ms)) return '-';\n    const num = Number(ms);\n    if (num < 1000) return `${num.toFixed(2)} ms`;\n    return `${(num / 1000).toFixed(2)} s`;\n  };\n  \n  // Check if there's an error\n  if (data.error) {\n    return (\n      <Paper sx={{ p: 2, bgcolor: '#ffebee' }}>\n        <Typography color=\"error\">{data.error}</Typography>\n      </Paper>\n    );\n  }\n\n  // Display performance info\n  const renderPerformanceInfo = () => {\n    if (!data.performance) return null;\n    \n    return (\n      <Box className=\"performance-info\">\n        <Divider className={classes.dividerMargin} />\n        <Typography variant=\"body2\">\n          Inference time: {formatTime(data.performance.inference_time)} on {data.performance.device}\n        </Typography>\n      </Box>\n    );\n  };\n\n  // Render for YOLO and DETR (object detection)\n  if (model === 'yolo' || model === 'detr') {\n    return (\n      <Paper className={classes.paper}>\n        <Typography variant=\"h6\" gutterBottom>\n          {model === 'yolo' ? 'YOLOv8' : 'DETR'} Detection Results\n        </Typography>\n        \n        <Grid container spacing={3}>\n          <Grid item xs={12} md={6}>\n            {data.image && (\n              <Box className={classes.marginBottom}>\n                <Typography variant=\"subtitle1\" gutterBottom>\n                  Detection Result\n                </Typography>\n                <img \n                  src={`data:image/png;base64,${data.image}`} \n                  alt=\"Detection Result\" \n                  className={classes.resultImage}\n                />\n              </Box>\n            )}\n          </Grid>\n          \n          <Grid item xs={12} md={6}>\n            <Box className={classes.marginBottom}>\n              <Typography variant=\"subtitle1\" gutterBottom>\n                Detected Objects:\n              </Typography>\n              \n              {data.detections && data.detections.length > 0 ? (\n                <List>\n                  {data.detections.map((detection, index) => (\n                    <React.Fragment key={index}>\n                      <ListItem>\n                        <ListItemText \n                          primary={\n                            <Box style={{ display: 'flex', alignItems: 'center' }}>\n                              <Typography variant=\"body1\" component=\"span\">\n                                {detection.class}\n                              </Typography>\n                              <Chip \n                                label={`${(detection.confidence * 100).toFixed(0)}%`}\n                                size=\"small\"\n                                color=\"primary\"\n                                style={{ marginLeft: 8 }}\n                              />\n                            </Box>\n                          } \n                          secondary={`Bounding Box: [${detection.bbox.join(', ')}]`} \n                        />\n                      </ListItem>\n                      {index < data.detections.length - 1 && <Divider />}\n                    </React.Fragment>\n                  ))}\n                </List>\n              ) : (\n                <Typography variant=\"body1\">No objects detected</Typography>\n              )}\n            </Box>\n          </Grid>\n        </Grid>\n        \n        {renderPerformanceInfo()}\n        \n        {/* Vector DB Actions for Object Detection */}\n        <VectorDBActions results={results} />\n      </Paper>\n    );\n  }\n  \n  // Render for ViT (classification)\n  if (model === 'vit') {\n    return (\n      <Paper className={classes.paper}>\n        <Typography variant=\"h6\" gutterBottom>\n          ViT Classification Results\n        </Typography>\n        \n        <Typography variant=\"subtitle1\" gutterBottom>\n          Top Predictions:\n        </Typography>\n        \n        {data.top_predictions && data.top_predictions.length > 0 ? (\n          <List>\n            {data.top_predictions.map((prediction, index) => (\n              <React.Fragment key={index}>\n                <ListItem>\n                  <ListItemText \n                    primary={\n                      <Box style={{ display: 'flex', alignItems: 'center' }}>\n                        <Typography variant=\"body1\" component=\"span\">\n                          {prediction.rank}. {prediction.class}\n                        </Typography>\n                        <Chip \n                          label={`${(prediction.probability * 100).toFixed(1)}%`}\n                          size=\"small\"\n                          color={index === 0 ? \"primary\" : \"default\"}\n                          style={{ marginLeft: 8 }}\n                        />\n                      </Box>\n                    } \n                  />\n                </ListItem>\n                {index < data.top_predictions.length - 1 && <Divider />}\n              </React.Fragment>\n            ))}\n          </List>\n        ) : (\n          <Typography variant=\"body1\">No classifications available</Typography>\n        )}\n        \n        {renderPerformanceInfo()}\n        \n        {/* Vector DB Actions for ViT Classification */}\n        <VectorDBActions results={results} />\n      </Paper>\n    );\n  }\n  \n  return null;\n};\n\nexport default ResultDisplay;\n","import React, { useState } from 'react';\nimport { \n  Paper, \n  Typography, \n  Box, \n  TextField, \n  Button, \n  CircularProgress,\n  Divider\n} from '@material-ui/core';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  paper: {\n    padding: theme.spacing(2),\n    marginTop: theme.spacing(2)\n  },\n  marginBottom: {\n    marginBottom: theme.spacing(2)\n  },\n  dividerMargin: {\n    margin: `${theme.spacing(2)}px 0`\n  },\n  responseBox: {\n    padding: theme.spacing(2),\n    backgroundColor: '#f5f5f5',\n    borderRadius: theme.shape.borderRadius,\n    marginTop: theme.spacing(2),\n    whiteSpace: 'pre-wrap'\n  },\n  buttonProgress: {\n    marginLeft: theme.spacing(1)\n  }\n}));\n\nconst LlmAnalysis = ({ visionResults, model }) => {\n  const classes = useStyles();\n  const [userQuery, setUserQuery] = useState('');\n  const [isAnalyzing, setIsAnalyzing] = useState(false);\n  const [analysisResult, setAnalysisResult] = useState(null);\n  const [error, setError] = useState(null);\n\n  // Format time for display\n  const formatTime = (ms) => {\n    if (ms === undefined || ms === null || isNaN(ms)) return '-';\n    const num = Number(ms);\n    if (num < 1000) return `${num.toFixed(2)} ms`;\n    return `${(num / 1000).toFixed(2)} s`;\n  };\n\n  const handleAnalyze = async () => {\n    if (!userQuery.trim()) return;\n    \n    setIsAnalyzing(true);\n    setError(null);\n    \n    try {\n      const response = await fetch('/api/analyze', {\n        method: 'POST',\n        headers: {\n          'Content-Type': 'application/json',\n        },\n        body: JSON.stringify({\n          visionResults: visionResults,\n          userQuery: userQuery\n        }),\n      });\n\n      if (!response.ok) {\n        throw new Error(`HTTP error! Status: ${response.status}`);\n      }\n\n      const data = await response.json();\n      \n      if (data.error) {\n        setError(data.error);\n      } else {\n        setAnalysisResult(data);\n      }\n    } catch (err) {\n      console.error('Error analyzing with LLM:', err);\n      setError(`Error analyzing with LLM: ${err.message}`);\n    } finally {\n      setIsAnalyzing(false);\n    }\n  };\n\n  if (!visionResults) return null;\n\n  return (\n    <Paper className={classes.paper}>\n      <Typography variant=\"h6\" gutterBottom>\n        Ask AI about the {model === 'vit' ? 'Classification' : 'Detection'} Results\n      </Typography>\n      \n      <Typography variant=\"body2\" className={classes.marginBottom}>\n        Ask a question about the detected objects or classifications to get an AI-powered analysis.\n      </Typography>\n      \n      <TextField\n        fullWidth\n        label=\"Your question about the image\"\n        variant=\"outlined\"\n        value={userQuery}\n        onChange={(e) => setUserQuery(e.target.value)}\n        disabled={isAnalyzing}\n        className={classes.marginBottom}\n        placeholder={model === 'vit' \n          ? \"E.g., What category does this image belong to?\" \n          : \"E.g., How many people are in this image?\"}\n      />\n      \n      <Button \n        variant=\"contained\" \n        color=\"primary\"\n        onClick={handleAnalyze}\n        disabled={isAnalyzing || !userQuery.trim()}\n      >\n        Analyze with AI\n        {isAnalyzing && <CircularProgress size={24} className={classes.buttonProgress} />}\n      </Button>\n      \n      {error && (\n        <Box mt={2}>\n          <Typography color=\"error\">{error}</Typography>\n        </Box>\n      )}\n      \n      {analysisResult && (\n        <>\n          <Divider className={classes.dividerMargin} />\n          \n          <Typography variant=\"subtitle1\" gutterBottom>\n            AI Analysis:\n          </Typography>\n          \n          <Box className={classes.responseBox}>\n            <Typography variant=\"body1\">\n              {analysisResult.response}\n            </Typography>\n          </Box>\n          \n          {analysisResult.performance && (\n            <Box mt={1}>\n              <Typography variant=\"body2\" color=\"textSecondary\">\n                Analysis time: {formatTime(analysisResult.performance.inference_time)} on {analysisResult.performance.device}\n              </Typography>\n            </Box>\n          )}\n        </>\n      )}\n    </Paper>\n  );\n};\n\nexport default LlmAnalysis;\n","import React, { useState } from 'react';\nimport {\n  Paper,\n  Typography,\n  Grid,\n  TextField,\n  Button,\n  Divider,\n  MenuItem,\n  Select,\n  FormControl,\n  InputLabel\n} from '@material-ui/core';\n\n// Props:\n// - imageBase64: data URL string of the currently uploaded image (preferred for searchType=image)\nfunction OpenAIChat({ imageBase64 }) {\n  const [apiKey, setApiKey] = useState('');\n  const [prompt, setPrompt] = useState('');\n  const [response, setResponse] = useState('');\n  const [loading, setLoading] = useState(false);\n  const [error, setError] = useState('');\n\n  // Vision RAG parameters\n  const [searchType, setSearchType] = useState('image');\n  const [objectId, setObjectId] = useState('');\n  const [className, setClassName] = useState('');\n  const [nResults, setNResults] = useState(5);\n\n  const onSend = async () => {\n    setError('');\n    setResponse('');\n    const q = (prompt || '').trim();\n    if (!q) { setError('질문을 입력하세요.'); return; }\n\n    // Build request body for /api/vision-rag/query\n    const body = {\n      userQuery: q,\n      searchType: searchType,\n      n_results: Number(nResults) || 5,\n    };\n    if (apiKey) body.api_key = apiKey;\n    if (searchType === 'image') {\n      if (!imageBase64) { setError('이미지가 필요합니다. 먼저 이미지를 업로드하세요.'); return; }\n      body.image = imageBase64;\n    } else if (searchType === 'object') {\n      if (!objectId.trim()) { setError('objectId를 입력하세요.'); return; }\n      body.objectId = objectId.trim();\n    } else if (searchType === 'class') {\n      if (!className.trim()) { setError('class_name을 입력하세요.'); return; }\n      body.class_name = className.trim();\n    }\n\n    setLoading(true);\n    try {\n      const res = await fetch('/api/vision-rag/query', {\n        method: 'POST',\n        headers: { 'Content-Type': 'application/json' },\n        credentials: 'include',\n        body: JSON.stringify(body)\n      });\n\n      if (!res.ok) {\n        let txt = await res.text();\n        try { txt = JSON.stringify(JSON.parse(txt), null, 2); } catch {}\n        throw new Error(txt);\n      }\n      const data = await res.json();\n      const meta = `Model: ${data.model || '-'} | Latency: ${data.latency_sec || '-'}s`;\n      \n      // 검색 결과 포맷팅\n      let retrievedText = '';\n      if (data.retrieved && data.retrieved.length > 0) {\n        retrievedText = '\\n\\n검색 결과:\\n';\n        data.retrieved.forEach((item, index) => {\n          retrievedText += `${index + 1}. ID: ${item.id || '-'}\\n`;\n          if (item.meta) {\n            retrievedText += `   클래스: ${item.meta.class || '-'}\\n`;\n            retrievedText += `   신뢰도: ${item.meta.confidence || '-'}\\n`;\n          }\n          retrievedText += `   유사도: ${item.distance ? item.distance.toFixed(4) : '-'}\\n`;\n        });\n      }\n      \n      setResponse((data.answer || '(빈 응답)') + retrievedText + '\\n\\n---\\n' + meta);\n    } catch (e) {\n      setError('Error: ' + e.message);\n    } finally {\n      setLoading(false);\n    }\n  };\n\n  const onClear = () => {\n    setPrompt('');\n    setResponse('');\n    setError('');\n  };\n\n  return (\n    <Paper style={{ padding: 16 }}>\n      <Typography variant=\"h5\" gutterBottom>\n        Vision RAG (LangChain)\n      </Typography>\n      <Typography variant=\"body2\" color=\"textSecondary\" gutterBottom>\n        서버에 OPENAI_API_KEY가 설정되어 있다면 API Key는 생략 가능합니다. 검색 유형을 선택하고 질문을 보내면, 벡터 DB에서 검색된 컨텍스트로 답변합니다.\n      </Typography>\n      <Grid container spacing={2}>\n        <Grid item xs={12} md={6}>\n          <FormControl fullWidth variant=\"outlined\" size=\"small\">\n            <InputLabel id=\"search-type-label\">Search Type</InputLabel>\n            <Select\n              labelId=\"search-type-label\"\n              value={searchType}\n              onChange={(e) => setSearchType(e.target.value)}\n              label=\"Search Type\"\n            >\n              <MenuItem value=\"image\">image (current upload)</MenuItem>\n              <MenuItem value=\"object\">object (objectId)</MenuItem>\n              <MenuItem value=\"class\">class (class_name)</MenuItem>\n            </Select>\n          </FormControl>\n        </Grid>\n        <Grid item xs={12} md={6}>\n          <TextField\n            label=\"OpenAI API Key (optional)\"\n            value={apiKey}\n            onChange={(e) => setApiKey(e.target.value)}\n            fullWidth\n            variant=\"outlined\"\n            size=\"small\"\n            type=\"password\"\n            placeholder=\"sk-...\"\n          />\n        </Grid>\n\n        {searchType === 'object' && (\n          <Grid item xs={12} md={6}>\n            <TextField\n              label=\"objectId\"\n              value={objectId}\n              onChange={(e) => setObjectId(e.target.value)}\n              fullWidth\n              variant=\"outlined\"\n              size=\"small\"\n            />\n          </Grid>\n        )}\n        {searchType === 'class' && (\n          <Grid item xs={12} md={6}>\n            <TextField\n              label=\"class_name\"\n              value={className}\n              onChange={(e) => setClassName(e.target.value)}\n              fullWidth\n              variant=\"outlined\"\n              size=\"small\"\n              placeholder=\"e.g. person, car, dog\"\n            />\n          </Grid>\n        )}\n        <Grid item xs={12} md={6}>\n          <TextField\n            label=\"n_results\"\n            value={nResults}\n            onChange={(e) => setNResults(e.target.value)}\n            fullWidth\n            variant=\"outlined\"\n            size=\"small\"\n            type=\"number\"\n            inputProps={{ min: 1, max: 50 }}\n          />\n        </Grid>\n\n        <Grid item xs={12}>\n          <TextField\n            label=\"User Question\"\n            value={prompt}\n            onChange={(e) => setPrompt(e.target.value)}\n            fullWidth\n            multiline\n            rows={4}\n            variant=\"outlined\"\n            placeholder={searchType === 'image' ? '업로드한 이미지 기반으로 답변해줘' : '검색된 객체 컨텍스트를 사용해 답변해줘'}\n          />\n        </Grid>\n        {error && (\n          <Grid item xs={12}>\n            <Typography color=\"error\">{error}</Typography>\n          </Grid>\n        )}\n        <Grid item xs={12}>\n          <div style={{ display: 'flex', gap: 8 }}>\n            <Button color=\"primary\" variant=\"contained\" onClick={onSend} disabled={loading}>\n              {loading ? 'Sending...' : 'Send Question'}\n            </Button>\n            <Button variant=\"outlined\" onClick={onClear}>Clear</Button>\n          </div>\n        </Grid>\n        <Grid item xs={12}>\n          <Divider style={{ margin: '12px 0' }} />\n          <Typography variant=\"subtitle2\" color=\"textSecondary\">Response</Typography>\n          <pre style={{ whiteSpace: 'pre-wrap', fontFamily: 'ui-monospace, monospace' }}>{response}</pre>\n        </Grid>\n      </Grid>\n    </Paper>\n  );\n}\n\nexport default OpenAIChat;\n","import React, { useState, useEffect } from 'react';\nimport { \n  Container, \n  Typography, \n  Box, \n  Paper, \n  Grid, \n  CircularProgress,\n  AppBar,\n  Toolbar,\n  ThemeProvider,\n  createMuiTheme\n} from '@material-ui/core';\nimport ImageUploader from './components/ImageUploader';\nimport ModelSelector from './components/ModelSelector';\nimport ResultDisplay from './components/ResultDisplay';\nimport LlmAnalysis from './components/LlmAnalysis';\nimport OpenAIChat from './components/OpenAIChat';\nimport './App.css';\n\n// Create a theme\nconst theme = createMuiTheme({\n  palette: {\n    primary: {\n      main: '#3f51b5',\n    },\n    secondary: {\n      main: '#f50057',\n    },\n  },\n  typography: {\n    fontFamily: 'Roboto, Arial, sans-serif',\n  },\n});\n\nfunction App() {\n  const [selectedImage, setSelectedImage] = useState(null);\n  const [imageBase64, setImageBase64] = useState('');\n  const [selectedModel, setSelectedModel] = useState('');\n  const [isProcessing, setIsProcessing] = useState(false);\n  const [results, setResults] = useState(null);\n  const [error, setError] = useState(null);\n  const [modelsStatus, setModelsStatus] = useState({\n    yolo: false,\n    detr: false,\n    vit: false\n  });\n\n  // Check API status on component mount\n  useEffect(() => {\n    fetch('/api/status')\n      .then(response => response.json())\n      .then(data => {\n        setModelsStatus(data.models);\n      })\n      .catch(err => {\n        console.error('Error checking API status:', err);\n        setError('Error connecting to the backend API. Please make sure the server is running.');\n      });\n  }, []);\n\n  const handleImageUpload = (image) => {\n    setSelectedImage(image);\n    setResults(null);\n    setError(null);\n\n    // Convert uploaded File -> base64 data URL for Vision RAG image search\n    if (image instanceof File) {\n      const reader = new FileReader();\n      reader.onload = () => {\n        const dataUrl = reader.result; // e.g., data:image/png;base64,....\n        setImageBase64(typeof dataUrl === 'string' ? dataUrl : '');\n      };\n      reader.onerror = () => setImageBase64('');\n      reader.readAsDataURL(image);\n    } else {\n      setImageBase64('');\n    }\n  };\n\n  const handleModelSelect = (model) => {\n    setSelectedModel(model);\n    setResults(null);\n    setError(null);\n  };\n\n  const processImage = async () => {\n    if (!selectedImage || !selectedModel) {\n      setError('Please select both an image and a model');\n      return;\n    }\n\n    setIsProcessing(true);\n    setError(null);\n\n    // Create form data for the image\n    const formData = new FormData();\n    formData.append('image', selectedImage);\n\n    let endpoint = '';\n    switch (selectedModel) {\n      case 'yolo':\n        endpoint = '/api/detect/yolo';\n        break;\n      case 'detr':\n        endpoint = '/api/detect/detr';\n        break;\n      case 'vit':\n        endpoint = '/api/classify/vit';\n        break;\n      default:\n        setError('Invalid model selection');\n        setIsProcessing(false);\n        return;\n    }\n\n    try {\n      const response = await fetch(endpoint, {\n        method: 'POST',\n        body: formData,\n      });\n\n      if (!response.ok) {\n        throw new Error(`HTTP error! Status: ${response.status}`);\n      }\n\n      const data = await response.json();\n      setResults({ model: selectedModel, data });\n    } catch (err) {\n      console.error('Error processing image:', err);\n      setError(`Error processing image: ${err.message}`);\n    } finally {\n      setIsProcessing(false);\n    }\n  };\n\n  return (\n    <ThemeProvider theme={theme}>\n      <Box style={{ flexGrow: 1 }}>\n        <AppBar position=\"static\">\n          <Toolbar>\n            <Typography variant=\"h6\" style={{ flexGrow: 1 }}>\n              Multi-Model Object Detection Demo\n            </Typography>\n          </Toolbar>\n        </AppBar>\n        <Container maxWidth=\"lg\" style={{ marginTop: theme.spacing(4), marginBottom: theme.spacing(4) }}>\n          <Grid container spacing={3}>\n            <Grid item xs={12}>\n              <Paper style={{ padding: theme.spacing(2) }}>\n                <Typography variant=\"h5\" gutterBottom>\n                  Upload an image to see how each model performs!\n                </Typography>\n                <Typography variant=\"body1\" paragraph>\n                  This demo showcases three different object detection and image classification models:\n                </Typography>\n                <Typography variant=\"body1\" component=\"div\">\n                  <ul>\n                    <li><strong>YOLOv8</strong>: Fast and accurate object detection</li>\n                    <li><strong>DETR</strong>: DEtection TRansformer for object detection</li>\n                    <li><strong>ViT</strong>: Vision Transformer for image classification</li>\n                  </ul>\n                </Typography>\n              </Paper>\n            </Grid>\n            \n            <Grid item xs={12} md={6}>\n              <ImageUploader onImageUpload={handleImageUpload} />\n            </Grid>\n            \n            <Grid item xs={12} md={6}>\n              <ModelSelector \n                onModelSelect={handleModelSelect} \n                onProcess={processImage}\n                isProcessing={isProcessing}\n                modelsStatus={modelsStatus}\n                selectedModel={selectedModel}\n                imageSelected={!!selectedImage}\n              />\n            </Grid>\n            \n            {error && (\n              <Grid item xs={12}>\n                <Paper style={{ padding: theme.spacing(2), backgroundColor: '#ffebee' }}>\n                  <Typography color=\"error\">{error}</Typography>\n                </Paper>\n              </Grid>\n            )}\n            \n            {isProcessing && (\n              <Grid item xs={12} style={{ textAlign: 'center', margin: `${theme.spacing(4)}px 0` }}>\n                <CircularProgress />\n                <Typography variant=\"h6\" style={{ marginTop: theme.spacing(2) }}>\n                  Processing image...\n                </Typography>\n              </Grid>\n            )}\n            \n            {results && (\n              <>\n                <Grid item xs={12}>\n                  <ResultDisplay results={results} />\n                </Grid>\n                <Grid item xs={12}>\n                  <LlmAnalysis visionResults={results.data} model={results.model} />\n                </Grid>\n              </>\n            )}\n\n            {/* Vision RAG (LangChain) section at the end */}\n            <Grid item xs={12}>\n              <OpenAIChat imageBase64={imageBase64} />\n            </Grid>\n          </Grid>\n        </Container>\n      </Box>\n    </ThemeProvider>\n  );\n}\n\nexport default App;\n","const reportWebVitals = (onPerfEntry) => {\n  if (onPerfEntry && onPerfEntry instanceof Function) {\n    import('web-vitals').then(({ getCLS, getFID, getFCP, getLCP, getTTFB }) => {\n      getCLS(onPerfEntry);\n      getFID(onPerfEntry);\n      getFCP(onPerfEntry);\n      getLCP(onPerfEntry);\n      getTTFB(onPerfEntry);\n    });\n  }\n};\n\nexport default reportWebVitals;\n","import React from 'react';\nimport ReactDOM from 'react-dom';\nimport './index.css';\nimport App from './App';\nimport reportWebVitals from './reportWebVitals';\n\nReactDOM.render(\n  <React.StrictMode>\n    <App />\n  </React.StrictMode>,\n  document.getElementById('root')\n);\n\n// If you want to start measuring performance in your app, pass a function\n// to log results (for example: reportWebVitals(console.log))\n// or send to an analytics endpoint. Learn more: https://bit.ly/CRA-vitals\nreportWebVitals();\n"],"sourceRoot":""}

static_bak_20250820_085943/asset-manifest.json

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static_bak_20250820_085943/index.html

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-<!doctype html><html lang="en"><head><meta charset="utf-8"/><link rel="icon" href="/favicon.ico"/><meta name="viewport" content="width=device-width,initial-scale=1"/><meta name="theme-color" content="#000000"/><meta name="description" content="Multi-Model Object Detection Demo"/><link rel="apple-touch-icon" href="/logo192.png"/><link rel="manifest" href="/manifest.json"/><title>Vision Web App</title><link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Roboto:300,400,500,700&display=swap"/><link href="/static/css/main.3b4eede1.chunk.css" rel="stylesheet"></head><body><noscript>You need to enable JavaScript to run this app.</noscript><div id="root"></div><script>!function(e){function r(r){for(var n,i,a=r[0],c=r[1],l=r[2],p=0,s=[];p<a.length;p++)i=a[p],Object.prototype.hasOwnProperty.call(o,i)&&o[i]&&s.push(o[i][0]),o[i]=0;for(n in c)Object.prototype.hasOwnProperty.call(c,n)&&(e[n]=c[n]);for(f&&f(r);s.length;)s.shift()();return u.push.apply(u,l||[]),t()}function t(){for(var e,r=0;r<u.length;r++){for(var t=u[r],n=!0,a=1;a<t.length;a++){var c=t[a];0!==o[c]&&(n=!1)}n&&(u.splice(r--,1),e=i(i.s=t[0]))}return e}var n={},o={1:0},u=[];function i(r){if(n[r])return n[r].exports;var t=n[r]={i:r,l:!1,exports:{}};return e[r].call(t.exports,t,t.exports,i),t.l=!0,t.exports}i.e=function(e){var r=[],t=o[e];if(0!==t)if(t)r.push(t[2]);else{var n=new Promise((function(r,n){t=o[e]=[r,n]}));r.push(t[2]=n);var u,a=document.createElement("script");a.charset="utf-8",a.timeout=120,i.nc&&a.setAttribute("nonce",i.nc),a.src=function(e){return i.p+"static/js/"+({}[e]||e)+"."+{3:"9013e23f"}[e]+".chunk.js"}(e);var c=new Error;u=function(r){a.onerror=a.onload=null,clearTimeout(l);var t=o[e];if(0!==t){if(t){var n=r&&("load"===r.type?"missing":r.type),u=r&&r.target&&r.target.src;c.message="Loading chunk "+e+" failed.\n("+n+": "+u+")",c.name="ChunkLoadError",c.type=n,c.request=u,t[1](c)}o[e]=void 0}};var l=setTimeout((function(){u({type:"timeout",target:a})}),12e4);a.onerror=a.onload=u,document.head.appendChild(a)}return Promise.all(r)},i.m=e,i.c=n,i.d=function(e,r,t){i.o(e,r)||Object.defineProperty(e,r,{enumerable:!0,get:t})},i.r=function(e){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},i.t=function(e,r){if(1&r&&(e=i(e)),8&r)return e;if(4&r&&"object"==typeof e&&e&&e.__esModule)return e;var t=Object.create(null);if(i.r(t),Object.defineProperty(t,"default",{enumerable:!0,value:e}),2&r&&"string"!=typeof e)for(var n in e)i.d(t,n,function(r){return e[r]}.bind(null,n));return t},i.n=function(e){var r=e&&e.__esModule?function(){return e.default}:function(){return e};return i.d(r,"a",r),r},i.o=function(e,r){return Object.prototype.hasOwnProperty.call(e,r)},i.p="/",i.oe=function(e){throw console.error(e),e};var a=this["webpackJsonpvision-web-app"]=this["webpackJsonpvision-web-app"]||[],c=a.push.bind(a);a.push=r,a=a.slice();for(var l=0;l<a.length;l++)r(a[l]);var f=c;t()}([])</script><script src="/static/js/2.9b5b80e8.chunk.js"></script><script src="/static/js/main.fc0e2dc7.chunk.js"></script></body></html>

static_bak_20250820_085943/manifest.json

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-{
-  "short_name": "Vision Web App",
-  "name": "Multi-Model Object Detection Demo",
-  "icons": [
-    {
-      "src": "favicon.ico",
-      "sizes": "64x64 32x32 24x24 16x16",
-      "type": "image/x-icon"
-    }
-  ],
-  "start_url": ".",
-  "display": "standalone",
-  "theme_color": "#000000",
-  "background_color": "#ffffff"
-}

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-    "url": "/static/css/main.3b4eede1.chunk.css"
-  },
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-    "url": "/static/js/2.9b5b80e8.chunk.js.LICENSE.txt"
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-]);

static_bak_20250820_085943/service-worker.js

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-/**
- * Welcome to your Workbox-powered service worker!
- *
- * You'll need to register this file in your web app and you should
- * disable HTTP caching for this file too.
- * See https://goo.gl/nhQhGp
- *
- * The rest of the code is auto-generated. Please don't update this file
- * directly; instead, make changes to your Workbox build configuration
- * and re-run your build process.
- * See https://goo.gl/2aRDsh
- */
-
-importScripts("https://storage.googleapis.com/workbox-cdn/releases/4.3.1/workbox-sw.js");
-
-importScripts(
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-
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-    self.skipWaiting();
-  }
-});
-
-workbox.core.clientsClaim();
-
-/**
- * The workboxSW.precacheAndRoute() method efficiently caches and responds to
- * requests for URLs in the manifest.
- * See https://goo.gl/S9QRab
- */
-self.__precacheManifest = [].concat(self.__precacheManifest || []);
-workbox.precaching.precacheAndRoute(self.__precacheManifest, {});
-
-workbox.routing.registerNavigationRoute(workbox.precaching.getCacheKeyForURL("/index.html"), {
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static_bak_20250820_085943/static/css/main.3b4eede1.chunk.css

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static_bak_20250820_085943/static/js/2.9b5b80e8.chunk.js.LICENSE.txt

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-/** @license React v16.14.0
- * react-dom.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
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- */
-
-/** @license React v16.14.0
- * react.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */
-
-/** @license React v17.0.2
- * react-is.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
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React, { useState, useRef } from 'react';\nimport { \n  Paper, \n  Typography, \n  Box, \n  Button, \n  IconButton \n} from '@material-ui/core';\nimport CloudUploadIcon from '@material-ui/icons/CloudUpload';\nimport DeleteIcon from '@material-ui/icons/Delete';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  paper: {\n    padding: theme.spacing(2),\n    display: 'flex',\n    flexDirection: 'column',\n    alignItems: 'center',\n    height: '100%',\n    minHeight: 300,\n    transition: 'all 0.3s ease'\n  },\n  dragActive: {\n    border: '2px dashed #3f51b5',\n    backgroundColor: 'rgba(63, 81, 181, 0.05)'\n  },\n  dragInactive: {\n    border: '2px dashed #ccc',\n    backgroundColor: 'white'\n  },\n  uploadBox: {\n    display: 'flex',\n    flexDirection: 'column',\n    alignItems: 'center',\n    justifyContent: 'center',\n    height: '100%',\n    width: '100%',\n    cursor: 'pointer'\n  },\n  uploadIcon: {\n    fontSize: 60,\n    color: '#3f51b5',\n    marginBottom: theme.spacing(2)\n  },\n  supportText: {\n    marginTop: theme.spacing(2)\n  },\n  previewBox: {\n    display: 'flex',\n    flexDirection: 'column',\n    alignItems: 'center',\n    width: '100%',\n    height: '100%',\n    position: 'relative'\n  },\n  imageContainer: {\n    position: 'relative',\n    width: '100%',\n    // Use viewport-based height so any aspect ratio fits inside\n    height: '60vh',\n    [theme.breakpoints.down('sm')]: {\n      height: '45vh',\n    },\n    display: 'flex',\n    justifyContent: 'center',\n    alignItems: 'center',\n    overflow: 'hidden',\n    marginTop: theme.spacing(2),\n  },\n  deleteButton: {\n    position: 'absolute',\n    top: 0,\n    right: 0,\n    backgroundColor: 'rgba(255, 255, 255, 0.7)',\n    '&:hover': {\n      backgroundColor: 'rgba(255, 255, 255, 0.9)',\n    }\n  }\n}));\n\nconst ImageUploader = ({ onImageUpload }) => {\n  const classes = useStyles();\n  const [previewUrl, setPreviewUrl] = useState(null);\n  const [dragActive, setDragActive] = useState(false);\n  const fileInputRef = useRef(null);\n\n  const handleDrag = (e) => {\n    e.preventDefault();\n    e.stopPropagation();\n    if (e.type === 'dragenter' || e.type === 'dragover') {\n      setDragActive(true);\n    } else if (e.type === 'dragleave') {\n      setDragActive(false);\n    }\n  };\n\n  const handleDrop = (e) => {\n    e.preventDefault();\n    e.stopPropagation();\n    setDragActive(false);\n    if (e.dataTransfer.files && e.dataTransfer.files[0]) {\n      handleFiles(e.dataTransfer.files[0]);\n    }\n  };\n\n  const handleChange = (e) => {\n    e.preventDefault();\n    if (e.target.files && e.target.files[0]) {\n      handleFiles(e.target.files[0]);\n    }\n  };\n\n  const handleFiles = (file) => {\n    if (file.type.startsWith('image/')) {\n      setPreviewUrl(URL.createObjectURL(file));\n      onImageUpload(file);\n    } else {\n      alert('Please upload an image file');\n    }\n  };\n\n  const onButtonClick = () => {\n    fileInputRef.current.click();\n  };\n\n  const handleRemoveImage = () => {\n    setPreviewUrl(null);\n    onImageUpload(null);\n    fileInputRef.current.value = \"\";\n  };\n\n  return (\n    <Paper \n      className={`${classes.paper} ${dragActive ? classes.dragActive : classes.dragInactive}`}\n      onDragEnter={handleDrag}\n      onDragLeave={handleDrag}\n      onDragOver={handleDrag}\n      onDrop={handleDrop}\n    >\n      <input\n        ref={fileInputRef}\n        type=\"file\"\n        accept=\"image/*\"\n        onChange={handleChange}\n        style={{ display: 'none' }}\n      />\n\n      {!previewUrl ? (\n        <Box \n          className={classes.uploadBox}\n          onClick={onButtonClick}\n        >\n          <CloudUploadIcon className={classes.uploadIcon} />\n          <Typography variant=\"h6\" gutterBottom>\n            Drag & Drop an image here\n          </Typography>\n          <Typography variant=\"body2\" color=\"textSecondary\" gutterBottom>\n            or\n          </Typography>\n          <Button\n            variant=\"contained\"\n            color=\"primary\"\n            component=\"span\"\n            startIcon={<CloudUploadIcon />}\n          >\n            Browse Files\n          </Button>\n          <Typography variant=\"body2\" color=\"textSecondary\" className={classes.supportText}>\n            Supported formats: JPG, PNG, GIF\n          </Typography>\n        </Box>\n      ) : (\n        <Box className={classes.previewBox}>\n          <Typography variant=\"h6\" gutterBottom>\n            Preview\n          </Typography>\n          <Box className={classes.imageContainer}>\n            <img\n              src={previewUrl}\n              alt=\"Preview\"\n              className=\"preview-image\"\n              style={{\n                width: '100%',\n                height: 'auto',\n                maxHeight: '100%',\n                objectFit: 'contain',\n                display: 'block'\n              }}\n            />\n            <IconButton\n              aria-label=\"delete\"\n              className={classes.deleteButton}\n              onClick={handleRemoveImage}\n            >\n              <DeleteIcon />\n            </IconButton>\n          </Box>\n        </Box>\n      )}\n    </Paper>\n  );\n};\n\nexport default ImageUploader;\n","import React from 'react';\nimport { \n  Grid, \n  Card, \n  CardContent, \n  CardActions, \n  Typography, \n  Button, \n  Chip,\n  Box\n} from '@material-ui/core';\nimport VisibilityIcon from '@material-ui/icons/Visibility';\nimport CategoryIcon from '@material-ui/icons/Category';\nimport PlayArrowIcon from '@material-ui/icons/PlayArrow';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  card: {\n    height: '100%',\n    display: 'flex',\n    flexDirection: 'column',\n  },\n  selectedCard: {\n    border: '2px solid #3f51b5',\n  },\n  unavailableCard: {\n    opacity: 0.6,\n  },\n  cardContent: {\n    flexGrow: 1,\n  },\n  chipContainer: {\n    marginBottom: theme.spacing(1.5),\n  },\n  successChip: {\n    backgroundColor: '#34C759',\n    color: '#fff',\n  },\n  errorChip: {\n    backgroundColor: '#FF3B3F',\n    color: '#fff',\n  },\n  modelType: {\n    marginTop: theme.spacing(1),\n  },\n  processButton: {\n    marginTop: theme.spacing(3),\n    textAlign: 'center',\n  }\n}));\n\nconst ModelSelector = ({ \n  onModelSelect, \n  onProcess, \n  isProcessing, \n  modelsStatus, \n  selectedModel,\n  imageSelected \n}) => {\n  const classes = useStyles();\n  \n  const models = [\n    {\n      id: 'yolo',\n      name: 'YOLOv8',\n      description: 'Fast and accurate object detection',\n      icon: <VisibilityIcon />,\n      available: modelsStatus.yolo\n    },\n    {\n      id: 'detr',\n      name: 'DETR',\n      description: 'DEtection TRansformer for object detection',\n      icon: <VisibilityIcon />,\n      available: modelsStatus.detr\n    },\n    {\n      id: 'vit',\n      name: 'ViT',\n      description: 'Vision Transformer for image classification',\n      icon: <CategoryIcon />,\n      available: modelsStatus.vit\n    }\n  ];\n\n  const handleModelClick = (modelId) => {\n    if (models.find(m => m.id === modelId).available) {\n      onModelSelect(modelId);\n    }\n  };\n\n  return (\n    <Box sx={{ p: 2, height: '100%' }}>\n      <Typography variant=\"h6\" gutterBottom>\n        Select Model\n      </Typography>\n      \n      <Grid container spacing={2}>\n        {models.map((model) => (\n          <Grid item xs={12} sm={4} key={model.id}>\n            <Card \n              className={`\n                ${classes.card} \n                ${selectedModel === model.id ? classes.selectedCard : ''} \n                ${!model.available ? classes.unavailableCard : ''}\n              `}\n              onClick={() => handleModelClick(model.id)}\n            >\n              <CardContent className={classes.cardContent}>\n                <Box sx={{ mb: 2, color: 'primary' }}>\n                  {model.icon}\n                </Box>\n                <Typography variant=\"h5\" component=\"div\" gutterBottom>\n                  {model.name}\n                </Typography>\n                <div className={classes.chipContainer}>\n                  {model.available ? (\n                    <Chip \n                      label=\"Available\" \n                      className={classes.successChip}\n                      size=\"small\" \n                    />\n                  ) : (\n                    <Chip \n                      label=\"Not Available\" \n                      className={classes.errorChip}\n                      size=\"small\" \n                    />\n                  )}\n                </div>\n                <Typography variant=\"body2\" color=\"textSecondary\">\n                  {model.description}\n                </Typography>\n              </CardContent>\n              <CardActions>\n                <Button \n                  size=\"small\" \n                  onClick={() => handleModelClick(model.id)}\n                  disabled={!model.available}\n                  color={selectedModel === model.id ? \"primary\" : \"default\"}\n                  variant={selectedModel === model.id ? \"contained\" : \"outlined\"}\n                  fullWidth\n                >\n                  {selectedModel === model.id ? 'Selected' : 'Select'}\n                </Button>\n              </CardActions>\n            </Card>\n          </Grid>\n        ))}\n      </Grid>\n\n      <div className={classes.processButton}>\n        <Button\n          variant=\"contained\"\n          color=\"primary\"\n          size=\"large\"\n          startIcon={<PlayArrowIcon />}\n          onClick={onProcess}\n          disabled={!selectedModel || !imageSelected || isProcessing}\n        >\n          {isProcessing ? 'Processing...' : 'Process Image'}\n        </Button>\n      </div>\n    </Box>\n  );\n};\n\nexport default ModelSelector;\n","import React, { useState } from 'react';\nimport { \n  Button, \n  Box, \n  Typography, \n  CircularProgress, \n  Snackbar,\n  Dialog,\n  DialogTitle,\n  DialogContent,\n  DialogActions,\n  TextField,\n  FormControl,\n  InputLabel,\n  Select,\n  MenuItem,\n  Grid,\n  Card,\n  CardMedia,\n  CardContent,\n  Chip\n} from '@material-ui/core';\nimport { Alert } from '@material-ui/lab';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  root: {\n    marginTop: theme.spacing(2),\n    marginBottom: theme.spacing(2),\n    padding: theme.spacing(2),\n    backgroundColor: '#f5f5f5',\n    borderRadius: theme.shape.borderRadius,\n  },\n  button: {\n    marginRight: theme.spacing(2),\n  },\n  searchDialog: {\n    minWidth: '500px',\n  },\n  formControl: {\n    marginBottom: theme.spacing(2),\n    minWidth: '100%',\n  },\n  searchResults: {\n    marginTop: theme.spacing(2),\n  },\n  resultCard: {\n    marginBottom: theme.spacing(2),\n  },\n  resultImage: {\n    height: 140,\n    objectFit: 'contain',\n  },\n  chip: {\n    margin: theme.spacing(0.5),\n  },\n  similarityChip: {\n    backgroundColor: theme.palette.primary.main,\n    color: 'white',\n  }\n}));\n\nconst VectorDBActions = ({ results }) => {\n  const classes = useStyles();\n  const [isSaving, setIsSaving] = useState(false);\n  const [saveSuccess, setSaveSuccess] = useState(false);\n  const [saveError, setSaveError] = useState(null);\n  const [openSearchDialog, setOpenSearchDialog] = useState(false);\n  const [searchType, setSearchType] = useState('image');\n  const [searchClass, setSearchClass] = useState('');\n  const [searchResults, setSearchResults] = useState([]);\n  const [isSearching, setIsSearching] = useState(false);\n  const [searchError, setSearchError] = useState(null);\n  \n  // Extract model and data from results\n  const { model, data } = results;\n  \n  // Handle saving to vector DB\n  const handleSaveToVectorDB = async () => {\n    setIsSaving(true);\n    setSaveError(null);\n    \n    try {\n      let response;\n      \n      if (model === 'vit') {\n        // For ViT, save the whole image with classifications\n        response = await fetch('/api/add-to-collection', {\n          method: 'POST',\n          headers: {\n            'Content-Type': 'application/json',\n          },\n          body: JSON.stringify({\n            image: data.image,\n            metadata: {\n              model: 'vit',\n              classifications: data.classifications\n            }\n          })\n        });\n      } else {\n        // For YOLO and DETR, save detected objects\n        response = await fetch('/api/add-detected-objects', {\n          method: 'POST',\n          headers: {\n            'Content-Type': 'application/json',\n          },\n          body: JSON.stringify({\n            image: data.image,\n            objects: data.detections,\n            imageId: generateUUID()\n          })\n        });\n      }\n      \n      if (!response.ok) {\n        throw new Error(`HTTP error! Status: ${response.status}`);\n      }\n      \n      const result = await response.json();\n      \n      if (result.error) {\n        throw new Error(result.error);\n      }\n      \n      setSaveSuccess(true);\n      setTimeout(() => setSaveSuccess(false), 5000);\n    } catch (err) {\n      console.error('Error saving to vector DB:', err);\n      setSaveError(`Error saving to vector DB: ${err.message}`);\n    } finally {\n      setIsSaving(false);\n    }\n  };\n  \n  // Handle opening search dialog\n  const handleOpenSearchDialog = () => {\n    setOpenSearchDialog(true);\n    setSearchResults([]);\n    setSearchError(null);\n  };\n  \n  // Handle closing search dialog\n  const handleCloseSearchDialog = () => {\n    setOpenSearchDialog(false);\n  };\n  \n  // Handle search type change\n  const handleSearchTypeChange = (event) => {\n    setSearchType(event.target.value);\n    setSearchResults([]);\n    setSearchError(null);\n  };\n  \n  // Handle search class change\n  const handleSearchClassChange = (event) => {\n    setSearchClass(event.target.value);\n  };\n  \n  // Handle search\n  const handleSearch = async () => {\n    setIsSearching(true);\n    setSearchError(null);\n    \n    try {\n      let requestBody = {};\n      \n      if (searchType === 'image') {\n        // Search by current image\n        requestBody = {\n          searchType: 'image',\n          image: data.image,\n          n_results: 5\n        };\n      } else {\n        // Search by class name\n        if (!searchClass.trim()) {\n          throw new Error('Please enter a class name');\n        }\n        \n        requestBody = {\n          searchType: 'class',\n          class_name: searchClass.trim(),\n          n_results: 5\n        };\n      }\n      \n      const response = await fetch('/api/search-similar-objects', {\n        method: 'POST',\n        headers: {\n          'Content-Type': 'application/json',\n        },\n        body: JSON.stringify(requestBody)\n      });\n      \n      if (!response.ok) {\n        throw new Error(`HTTP error! Status: ${response.status}`);\n      }\n      \n      const result = await response.json();\n      \n      if (result.error) {\n        throw new Error(result.error);\n      }\n      \n      console.log('Search API response:', result);\n      \n      // The backend responds with {success, searchType, results} structure, so extract only the results array\n      if (result.success && Array.isArray(result.results)) {\n        console.log('Setting search results array:', result.results);\n        console.log('Results array length:', result.results.length);\n        console.log('First result item:', result.results[0]);\n        setSearchResults(result.results);\n      } else {\n        console.error('Unexpected API response format:', result);\n        throw new Error('Unexpected API response format');\n      }\n    } catch (err) {\n      console.error('Error searching vector DB:', err);\n      setSearchError(`Error searching vector DB: ${err.message}`);\n    } finally {\n      setIsSearching(false);\n    }\n  };\n  \n  // Generate UUID for image ID\n  const generateUUID = () => {\n    return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, function(c) {\n      const r = Math.random() * 16 | 0;\n      const v = c === 'x' ? r : (r & 0x3 | 0x8);\n      return v.toString(16);\n    });\n  };\n  \n  // Render search results\n  const renderSearchResults = () => {\n    console.log('Rendering search results:', searchResults);\n    console.log('Search results length:', searchResults.length);\n    \n    if (searchResults.length === 0) {\n      console.log('No results to render');\n      return (\n        <Typography variant=\"body1\">No results found.</Typography>\n      );\n    }\n    \n    return (\n      <Grid container spacing={2}>\n        {searchResults.map((result, index) => {\n          const similarity = (1 - result.distance) * 100;\n          \n          return (\n            <Grid item xs={12} sm={6} key={index}>\n              <Card className={classes.resultCard}>\n                {result.metadata && result.metadata.image_data ? (\n                  <CardMedia\n                    className={classes.resultImage}\n                    component=\"img\"\n                    height=\"200\"\n                    image={`data:image/jpeg;base64,${result.metadata.image_data}`}\n                    alt={result.metadata && result.metadata.class ? result.metadata.class : 'Object'}\n                  />\n                ) : (\n                  <Box \n                    className={classes.resultImage}\n                    style={{ \n                      backgroundColor: '#f0f0f0', \n                      display: 'flex', \n                      alignItems: 'center', \n                      justifyContent: 'center',\n                      height: 200\n                    }}\n                  >\n                    <Typography variant=\"body2\" color=\"textSecondary\">\n                      {result.metadata && result.metadata.class ? result.metadata.class : 'Object'} Image\n                    </Typography>\n                  </Box>\n                )}\n                <CardContent>\n                  <Box display=\"flex\" justifyContent=\"space-between\" alignItems=\"center\" mb={1}>\n                    <Typography variant=\"subtitle1\">Result #{index + 1}</Typography>\n                    <Chip \n                      label={`Similarity: ${similarity.toFixed(2)}%`}\n                      className={classes.similarityChip}\n                      size=\"small\"\n                    />\n                  </Box>\n                  <Typography variant=\"body2\" color=\"textSecondary\">\n                    <strong>Class:</strong> {result.metadata.class || 'N/A'}\n                  </Typography>\n                  {result.metadata.confidence && (\n                    <Typography variant=\"body2\" color=\"textSecondary\">\n                      <strong>Confidence:</strong> {(result.metadata.confidence * 100).toFixed(2)}%\n                    </Typography>\n                  )}\n                  <Typography variant=\"body2\" color=\"textSecondary\">\n                    <strong>Object ID:</strong> {result.id}\n                  </Typography>\n                </CardContent>\n              </Card>\n            </Grid>\n          );\n        })}\n      </Grid>\n    );\n  };\n  \n  return (\n    <Box className={classes.root}>\n      <Typography variant=\"h6\" gutterBottom>\n        Vector Database Actions\n      </Typography>\n      \n      <Box display=\"flex\" alignItems=\"center\" mb={2}>\n        <Button\n          variant=\"contained\"\n          color=\"primary\"\n          onClick={handleSaveToVectorDB}\n          disabled={isSaving}\n          className={classes.button}\n        >\n          {isSaving ? (\n            <>\n              <CircularProgress size={20} color=\"inherit\" style={{ marginRight: 8 }} />\n              Saving...\n            </>\n          ) : (\n            'Save to Vector DB'\n          )}\n        </Button>\n        \n        <Button\n          variant=\"outlined\"\n          color=\"primary\"\n          onClick={handleOpenSearchDialog}\n          className={classes.button}\n        >\n          Search Similar\n        </Button>\n      </Box>\n      \n      {saveError && (\n        <Alert severity=\"error\" style={{ marginTop: 8 }}>\n          {saveError}\n        </Alert>\n      )}\n      \n      <Snackbar open={saveSuccess} autoHideDuration={5000} onClose={() => setSaveSuccess(false)}>\n        <Alert severity=\"success\">\n          {model === 'vit' ? (\n            'Image and classifications successfully saved to vector DB!'\n          ) : (\n            'Detected objects successfully saved to vector DB!'\n          )}\n        </Alert>\n      </Snackbar>\n      \n      {/* Search Dialog */}\n      <Dialog\n        open={openSearchDialog}\n        onClose={handleCloseSearchDialog}\n        maxWidth=\"md\"\n        fullWidth\n      >\n        <DialogTitle>Search Vector Database</DialogTitle>\n        <DialogContent>\n          <FormControl className={classes.formControl}>\n            <InputLabel id=\"search-type-label\">Search Type</InputLabel>\n            <Select\n              labelId=\"search-type-label\"\n              id=\"search-type\"\n              value={searchType}\n              onChange={handleSearchTypeChange}\n            >\n              <MenuItem value=\"image\">Search by Current Image</MenuItem>\n              <MenuItem value=\"class\">Search by Class Name</MenuItem>\n            </Select>\n          </FormControl>\n          \n          {searchType === 'class' && (\n            <FormControl className={classes.formControl}>\n              <TextField\n                label=\"Class Name\"\n                value={searchClass}\n                onChange={handleSearchClassChange}\n                placeholder=\"e.g. person, car, dog...\"\n                fullWidth\n              />\n            </FormControl>\n          )}\n          \n          {searchError && (\n            <Alert severity=\"error\" style={{ marginBottom: 16 }}>\n              {searchError}\n            </Alert>\n          )}\n          \n          <Box className={classes.searchResults}>\n            {isSearching ? (\n              <Box display=\"flex\" justifyContent=\"center\" alignItems=\"center\" p={4}>\n                <CircularProgress />\n                <Typography variant=\"body1\" style={{ marginLeft: 16 }}>\n                  Searching...\n                </Typography>\n              </Box>\n            ) : (\n              <>\n                {console.log('Search dialog render - searchResults:', searchResults)}\n                {searchResults.length > 0 ? renderSearchResults() : \n                  <Typography variant=\"body1\">No results found. Please try another search.</Typography>\n                }\n              </>\n            )}\n          </Box>\n        </DialogContent>\n        <DialogActions>\n          <Button onClick={handleCloseSearchDialog} color=\"default\">\n            Close\n          </Button>\n          <Button \n            onClick={handleSearch} \n            color=\"primary\" \n            variant=\"contained\"\n            disabled={isSearching || (searchType === 'class' && !searchClass.trim())}\n          >\n            Search\n          </Button>\n        </DialogActions>\n      </Dialog>\n    </Box>\n  );\n};\n\nexport default VectorDBActions;\n","import React from 'react';\nimport { \n  Paper, \n  Typography, \n  Box, \n  List, \n  ListItem, \n  ListItemText, \n  Divider,\n  Grid,\n  Chip\n} from '@material-ui/core';\nimport VectorDBActions from './VectorDBActions';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  paper: {\n    padding: theme.spacing(2)\n  },\n  marginBottom: {\n    marginBottom: theme.spacing(2)\n  },\n  resultImage: {\n    maxWidth: '100%',\n    maxHeight: '400px',\n    objectFit: 'contain'\n  },\n  dividerMargin: {\n    margin: `${theme.spacing(2)}px 0`\n  },\n  chipContainer: {\n    display: 'flex',\n    gap: theme.spacing(1),\n    flexWrap: 'wrap'\n  }\n}));\n\nconst ResultDisplay = ({ results }) => {\n  const classes = useStyles();\n  if (!results) return null;\n  \n  const { model, data } = results;\n  \n  // Helper to format times nicely\n  const formatTime = (ms) => {\n    if (ms === undefined || ms === null || isNaN(ms)) return '-';\n    const num = Number(ms);\n    if (num < 1000) return `${num.toFixed(2)} ms`;\n    return `${(num / 1000).toFixed(2)} s`;\n  };\n  \n  // Check if there's an error\n  if (data.error) {\n    return (\n      <Paper sx={{ p: 2, bgcolor: '#ffebee' }}>\n        <Typography color=\"error\">{data.error}</Typography>\n      </Paper>\n    );\n  }\n\n  // Display performance info\n  const renderPerformanceInfo = () => {\n    if (!data.performance) return null;\n    \n    return (\n      <Box className=\"performance-info\">\n        <Divider className={classes.dividerMargin} />\n        <Typography variant=\"body2\">\n          Inference time: {formatTime(data.performance.inference_time)} on {data.performance.device}\n        </Typography>\n      </Box>\n    );\n  };\n\n  // Render for YOLO and DETR (object detection)\n  if (model === 'yolo' || model === 'detr') {\n    return (\n      <Paper className={classes.paper}>\n        <Typography variant=\"h6\" gutterBottom>\n          {model === 'yolo' ? 'YOLOv8' : 'DETR'} Detection Results\n        </Typography>\n        \n        <Grid container spacing={3}>\n          <Grid item xs={12} md={6}>\n            {data.image && (\n              <Box className={classes.marginBottom}>\n                <Typography variant=\"subtitle1\" gutterBottom>\n                  Detection Result\n                </Typography>\n                <img \n                  src={`data:image/png;base64,${data.image}`} \n                  alt=\"Detection Result\" \n                  className={classes.resultImage}\n                />\n              </Box>\n            )}\n          </Grid>\n          \n          <Grid item xs={12} md={6}>\n            <Box className={classes.marginBottom}>\n              <Typography variant=\"subtitle1\" gutterBottom>\n                Detected Objects:\n              </Typography>\n              \n              {data.detections && data.detections.length > 0 ? (\n                <List>\n                  {data.detections.map((detection, index) => (\n                    <React.Fragment key={index}>\n                      <ListItem>\n                        <ListItemText \n                          primary={\n                            <Box style={{ display: 'flex', alignItems: 'center' }}>\n                              <Typography variant=\"body1\" component=\"span\">\n                                {detection.class}\n                              </Typography>\n                              <Chip \n                                label={`${(detection.confidence * 100).toFixed(0)}%`}\n                                size=\"small\"\n                                color=\"primary\"\n                                style={{ marginLeft: 8 }}\n                              />\n                            </Box>\n                          } \n                          secondary={`Bounding Box: [${detection.bbox.join(', ')}]`} \n                        />\n                      </ListItem>\n                      {index < data.detections.length - 1 && <Divider />}\n                    </React.Fragment>\n                  ))}\n                </List>\n              ) : (\n                <Typography variant=\"body1\">No objects detected</Typography>\n              )}\n            </Box>\n          </Grid>\n        </Grid>\n        \n        {renderPerformanceInfo()}\n        \n        {/* Vector DB Actions for Object Detection */}\n        <VectorDBActions results={results} />\n      </Paper>\n    );\n  }\n  \n  // Render for ViT (classification)\n  if (model === 'vit') {\n    return (\n      <Paper className={classes.paper}>\n        <Typography variant=\"h6\" gutterBottom>\n          ViT Classification Results\n        </Typography>\n        \n        <Typography variant=\"subtitle1\" gutterBottom>\n          Top Predictions:\n        </Typography>\n        \n        {data.top_predictions && data.top_predictions.length > 0 ? (\n          <List>\n            {data.top_predictions.map((prediction, index) => (\n              <React.Fragment key={index}>\n                <ListItem>\n                  <ListItemText \n                    primary={\n                      <Box style={{ display: 'flex', alignItems: 'center' }}>\n                        <Typography variant=\"body1\" component=\"span\">\n                          {prediction.rank}. {prediction.class}\n                        </Typography>\n                        <Chip \n                          label={`${(prediction.probability * 100).toFixed(1)}%`}\n                          size=\"small\"\n                          color={index === 0 ? \"primary\" : \"default\"}\n                          style={{ marginLeft: 8 }}\n                        />\n                      </Box>\n                    } \n                  />\n                </ListItem>\n                {index < data.top_predictions.length - 1 && <Divider />}\n              </React.Fragment>\n            ))}\n          </List>\n        ) : (\n          <Typography variant=\"body1\">No classifications available</Typography>\n        )}\n        \n        {renderPerformanceInfo()}\n        \n        {/* Vector DB Actions for ViT Classification */}\n        <VectorDBActions results={results} />\n      </Paper>\n    );\n  }\n  \n  return null;\n};\n\nexport default ResultDisplay;\n","import React, { useState } from 'react';\nimport { \n  Paper, \n  Typography, \n  Box, \n  TextField, \n  Button, \n  CircularProgress,\n  Divider\n} from '@material-ui/core';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n  paper: {\n    padding: theme.spacing(2),\n    marginTop: theme.spacing(2)\n  },\n  marginBottom: {\n    marginBottom: theme.spacing(2)\n  },\n  dividerMargin: {\n    margin: `${theme.spacing(2)}px 0`\n  },\n  responseBox: {\n    padding: theme.spacing(2),\n    backgroundColor: '#f5f5f5',\n    borderRadius: theme.shape.borderRadius,\n    marginTop: theme.spacing(2),\n    whiteSpace: 'pre-wrap'\n  },\n  buttonProgress: {\n    marginLeft: theme.spacing(1)\n  }\n}));\n\nconst LlmAnalysis = ({ visionResults, model }) => {\n  const classes = useStyles();\n  const [userQuery, setUserQuery] = useState('');\n  const [isAnalyzing, setIsAnalyzing] = useState(false);\n  const [analysisResult, setAnalysisResult] = useState(null);\n  const [error, setError] = useState(null);\n\n  // Format time for display\n  const formatTime = (ms) => {\n    if (ms === undefined || ms === null || isNaN(ms)) return '-';\n    const num = Number(ms);\n    if (num < 1000) return `${num.toFixed(2)} ms`;\n    return `${(num / 1000).toFixed(2)} s`;\n  };\n\n  const handleAnalyze = async () => {\n    if (!userQuery.trim()) return;\n    \n    setIsAnalyzing(true);\n    setError(null);\n    \n    try {\n      const response = await fetch('/api/analyze', {\n        method: 'POST',\n        headers: {\n          'Content-Type': 'application/json',\n        },\n        body: JSON.stringify({\n          visionResults: visionResults,\n          userQuery: userQuery\n        }),\n      });\n\n      if (!response.ok) {\n        throw new Error(`HTTP error! Status: ${response.status}`);\n      }\n\n      const data = await response.json();\n      \n      if (data.error) {\n        setError(data.error);\n      } else {\n        setAnalysisResult(data);\n      }\n    } catch (err) {\n      console.error('Error analyzing with LLM:', err);\n      setError(`Error analyzing with LLM: ${err.message}`);\n    } finally {\n      setIsAnalyzing(false);\n    }\n  };\n\n  if (!visionResults) return null;\n\n  return (\n    <Paper className={classes.paper}>\n      <Typography variant=\"h6\" gutterBottom>\n        Ask AI about the {model === 'vit' ? 'Classification' : 'Detection'} Results\n      </Typography>\n      \n      <Typography variant=\"body2\" className={classes.marginBottom}>\n        Ask a question about the detected objects or classifications to get an AI-powered analysis.\n      </Typography>\n      \n      <TextField\n        fullWidth\n        label=\"Your question about the image\"\n        variant=\"outlined\"\n        value={userQuery}\n        onChange={(e) => setUserQuery(e.target.value)}\n        disabled={isAnalyzing}\n        className={classes.marginBottom}\n        placeholder={model === 'vit' \n          ? \"E.g., What category does this image belong to?\" \n          : \"E.g., How many people are in this image?\"}\n      />\n      \n      <Button \n        variant=\"contained\" \n        color=\"primary\"\n        onClick={handleAnalyze}\n        disabled={isAnalyzing || !userQuery.trim()}\n      >\n        Analyze with AI\n        {isAnalyzing && <CircularProgress size={24} className={classes.buttonProgress} />}\n      </Button>\n      \n      {error && (\n        <Box mt={2}>\n          <Typography color=\"error\">{error}</Typography>\n        </Box>\n      )}\n      \n      {analysisResult && (\n        <>\n          <Divider className={classes.dividerMargin} />\n          \n          <Typography variant=\"subtitle1\" gutterBottom>\n            AI Analysis:\n          </Typography>\n          \n          <Box className={classes.responseBox}>\n            <Typography variant=\"body1\">\n              {analysisResult.response}\n            </Typography>\n          </Box>\n          \n          {analysisResult.performance && (\n            <Box mt={1}>\n              <Typography variant=\"body2\" color=\"textSecondary\">\n                Analysis time: {formatTime(analysisResult.performance.inference_time)} on {analysisResult.performance.device}\n              </Typography>\n            </Box>\n          )}\n        </>\n      )}\n    </Paper>\n  );\n};\n\nexport default LlmAnalysis;\n","import React, { useState } from 'react';\nimport { \n  Paper,\n  Typography,\n  Grid,\n  TextField,\n  Button,\n  Divider\n} from '@material-ui/core';\n\nfunction OpenAIChat() {\n  const [model, setModel] = useState('gpt-4');\n  const [apiKey, setApiKey] = useState('');\n  const [system, setSystem] = useState('You are a helpful assistant.');\n  const [prompt, setPrompt] = useState('');\n  const [response, setResponse] = useState('');\n  const [loading, setLoading] = useState(false);\n  const [error, setError] = useState('');\n\n  const onSend = async () => {\n    setError('');\n    setResponse('');\n    const p = (prompt || '').trim();\n    if (!p) { setError('Please enter a question.'); return; }\n\n    setLoading(true);\n    try {\n      const res = await fetch('/api/openai/chat', {\n        method: 'POST',\n        headers: { 'Content-Type': 'application/json' },\n        credentials: 'include',\n        body: JSON.stringify({\n          prompt: p,\n          model: (model || '').trim() || 'gpt-4',\n          api_key: (apiKey || undefined),\n          system: (system || undefined)\n        })\n      });\n\n      if (!res.ok) {\n        let txt = await res.text();\n        try { txt = JSON.stringify(JSON.parse(txt), null, 2); } catch {}\n        throw new Error(txt);\n      }\n      const data = await res.json();\n      const meta = `Model: ${data.model} | Latency: ${data.latency_sec}s` + (data.usage ? ` | Usage: ${JSON.stringify(data.usage)}` : '');\n      setResponse((data.response || '(Empty response)') + '\\n\\n---\\n' + meta);\n    } catch (e) {\n      setError('Error: ' + e.message);\n    } finally {\n      setLoading(false);\n    }\n  };\n\n  const onClear = () => {\n    setPrompt('');\n    setResponse('');\n    setError('');\n  };\n\n  return (\n    <Paper style={{ padding: 16 }}>\n      <Typography variant=\"h5\" gutterBottom>\n        OpenAI Chat (OpenAI API)\n      </Typography>\n      <Typography variant=\"body2\" color=\"textSecondary\" gutterBottom>\n        If the server env var OPENAI_API_KEY is set, the API Key field is optional.\n      </Typography>\n      <Grid container spacing={2}>\n        <Grid item xs={12} md={6}>\n          <TextField\n            label=\"Model\"\n            value={model}\n            onChange={(e) => setModel(e.target.value)}\n            fullWidth\n            variant=\"outlined\"\n            size=\"small\"\n          />\n        </Grid>\n        <Grid item xs={12} md={6}>\n          <TextField\n            label=\"OpenAI API Key (optional)\"\n            value={apiKey}\n            onChange={(e) => setApiKey(e.target.value)}\n            fullWidth\n            variant=\"outlined\"\n            size=\"small\"\n            type=\"password\"\n            placeholder=\"sk-...\"\n          />\n        </Grid>\n        <Grid item xs={12}>\n          <TextField\n            label=\"System Prompt (optional)\"\n            value={system}\n            onChange={(e) => setSystem(e.target.value)}\n            fullWidth\n            variant=\"outlined\"\n            size=\"small\"\n          />\n        </Grid>\n        <Grid item xs={12}>\n          <TextField\n            label=\"User Question\"\n            value={prompt}\n            onChange={(e) => setPrompt(e.target.value)}\n            fullWidth\n            multiline\n            rows={4}\n            variant=\"outlined\"\n          />\n        </Grid>\n        {error && (\n          <Grid item xs={12}>\n            <Typography color=\"error\">{error}</Typography>\n          </Grid>\n        )}\n        <Grid item xs={12}>\n          <div style={{ display: 'flex', gap: 8 }}>\n            <Button color=\"primary\" variant=\"contained\" onClick={onSend} disabled={loading}>\n              {loading ? 'Sending...' : 'Send Question'}\n            </Button>\n            <Button variant=\"outlined\" onClick={onClear}>Clear</Button>\n          </div>\n        </Grid>\n        <Grid item xs={12}>\n          <Divider style={{ margin: '12px 0' }} />\n          <Typography variant=\"subtitle2\" color=\"textSecondary\">Response</Typography>\n          <pre style={{ whiteSpace: 'pre-wrap', fontFamily: 'ui-monospace, monospace' }}>{response}</pre>\n        </Grid>\n      </Grid>\n    </Paper>\n  );\n}\n\nexport default OpenAIChat;\n","import React, { useState, useEffect } from 'react';\nimport { \n  Container, \n  Typography, \n  Box, \n  Paper, \n  Grid, \n  CircularProgress,\n  AppBar,\n  Toolbar,\n  ThemeProvider,\n  createMuiTheme\n} from '@material-ui/core';\nimport ImageUploader from './components/ImageUploader';\nimport ModelSelector from './components/ModelSelector';\nimport ResultDisplay from './components/ResultDisplay';\nimport LlmAnalysis from './components/LlmAnalysis';\nimport OpenAIChat from './components/OpenAIChat';\nimport './App.css';\n\n// Create a theme\nconst theme = createMuiTheme({\n  palette: {\n    primary: {\n      main: '#3f51b5',\n    },\n    secondary: {\n      main: '#f50057',\n    },\n  },\n  typography: {\n    fontFamily: 'Roboto, Arial, sans-serif',\n  },\n});\n\nfunction App() {\n  const [selectedImage, setSelectedImage] = useState(null);\n  const [selectedModel, setSelectedModel] = useState('');\n  const [isProcessing, setIsProcessing] = useState(false);\n  const [results, setResults] = useState(null);\n  const [error, setError] = useState(null);\n  const [modelsStatus, setModelsStatus] = useState({\n    yolo: false,\n    detr: false,\n    vit: false\n  });\n\n  // Check API status on component mount\n  useEffect(() => {\n    fetch('/api/status')\n      .then(response => response.json())\n      .then(data => {\n        setModelsStatus(data.models);\n      })\n      .catch(err => {\n        console.error('Error checking API status:', err);\n        setError('Error connecting to the backend API. Please make sure the server is running.');\n      });\n  }, []);\n\n  const handleImageUpload = (image) => {\n    setSelectedImage(image);\n    setResults(null);\n    setError(null);\n  };\n\n  const handleModelSelect = (model) => {\n    setSelectedModel(model);\n    setResults(null);\n    setError(null);\n  };\n\n  const processImage = async () => {\n    if (!selectedImage || !selectedModel) {\n      setError('Please select both an image and a model');\n      return;\n    }\n\n    setIsProcessing(true);\n    setError(null);\n\n    // Create form data for the image\n    const formData = new FormData();\n    formData.append('image', selectedImage);\n\n    let endpoint = '';\n    switch (selectedModel) {\n      case 'yolo':\n        endpoint = '/api/detect/yolo';\n        break;\n      case 'detr':\n        endpoint = '/api/detect/detr';\n        break;\n      case 'vit':\n        endpoint = '/api/classify/vit';\n        break;\n      default:\n        setError('Invalid model selection');\n        setIsProcessing(false);\n        return;\n    }\n\n    try {\n      const response = await fetch(endpoint, {\n        method: 'POST',\n        body: formData,\n      });\n\n      if (!response.ok) {\n        throw new Error(`HTTP error! Status: ${response.status}`);\n      }\n\n      const data = await response.json();\n      setResults({ model: selectedModel, data });\n    } catch (err) {\n      console.error('Error processing image:', err);\n      setError(`Error processing image: ${err.message}`);\n    } finally {\n      setIsProcessing(false);\n    }\n  };\n\n  return (\n    <ThemeProvider theme={theme}>\n      <Box style={{ flexGrow: 1 }}>\n        <AppBar position=\"static\">\n          <Toolbar>\n            <Typography variant=\"h6\" style={{ flexGrow: 1 }}>\n              Multi-Model Object Detection Demo\n            </Typography>\n          </Toolbar>\n        </AppBar>\n        <Container maxWidth=\"lg\" style={{ marginTop: theme.spacing(4), marginBottom: theme.spacing(4) }}>\n          <Grid container spacing={3}>\n            <Grid item xs={12}>\n              <Paper style={{ padding: theme.spacing(2) }}>\n                <Typography variant=\"h5\" gutterBottom>\n                  Upload an image to see how each model performs!\n                </Typography>\n                <Typography variant=\"body1\" paragraph>\n                  This demo showcases three different object detection and image classification models:\n                </Typography>\n                <Typography variant=\"body1\" component=\"div\">\n                  <ul>\n                    <li><strong>YOLOv8</strong>: Fast and accurate object detection</li>\n                    <li><strong>DETR</strong>: DEtection TRansformer for object detection</li>\n                    <li><strong>ViT</strong>: Vision Transformer for image classification</li>\n                  </ul>\n                </Typography>\n              </Paper>\n            </Grid>\n            \n            <Grid item xs={12} md={6}>\n              <ImageUploader onImageUpload={handleImageUpload} />\n            </Grid>\n            \n            <Grid item xs={12} md={6}>\n              <ModelSelector \n                onModelSelect={handleModelSelect} \n                onProcess={processImage}\n                isProcessing={isProcessing}\n                modelsStatus={modelsStatus}\n                selectedModel={selectedModel}\n                imageSelected={!!selectedImage}\n              />\n            </Grid>\n            \n            {error && (\n              <Grid item xs={12}>\n                <Paper style={{ padding: theme.spacing(2), backgroundColor: '#ffebee' }}>\n                  <Typography color=\"error\">{error}</Typography>\n                </Paper>\n              </Grid>\n            )}\n            \n            {isProcessing && (\n              <Grid item xs={12} style={{ textAlign: 'center', margin: `${theme.spacing(4)}px 0` }}>\n                <CircularProgress />\n                <Typography variant=\"h6\" style={{ marginTop: theme.spacing(2) }}>\n                  Processing image...\n                </Typography>\n              </Grid>\n            )}\n            \n            {results && (\n              <>\n                <Grid item xs={12}>\n                  <ResultDisplay results={results} />\n                </Grid>\n                <Grid item xs={12}>\n                  <LlmAnalysis visionResults={results.data} model={results.model} />\n                </Grid>\n              </>\n            )}\n\n            {/* OpenAI Chat section at the end */}\n            <Grid item xs={12}>\n              <OpenAIChat />\n            </Grid>\n          </Grid>\n        </Container>\n      </Box>\n    </ThemeProvider>\n  );\n}\n\nexport default App;\n","const reportWebVitals = (onPerfEntry) => {\n  if (onPerfEntry && onPerfEntry instanceof Function) {\n    import('web-vitals').then(({ getCLS, getFID, getFCP, getLCP, getTTFB }) => {\n      getCLS(onPerfEntry);\n      getFID(onPerfEntry);\n      getFCP(onPerfEntry);\n      getLCP(onPerfEntry);\n      getTTFB(onPerfEntry);\n    });\n  }\n};\n\nexport default reportWebVitals;\n","import React from 'react';\nimport ReactDOM from 'react-dom';\nimport './index.css';\nimport App from './App';\nimport reportWebVitals from './reportWebVitals';\n\nReactDOM.render(\n  <React.StrictMode>\n    <App />\n  </React.StrictMode>,\n  document.getElementById('root')\n);\n\n// If you want to start measuring performance in your app, pass a function\n// to log results (for example: reportWebVitals(console.log))\n// or send to an analytics endpoint. Learn more: https://bit.ly/CRA-vitals\nreportWebVitals();\n"],"sourceRoot":""}

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static_bak_20250820_085943/static/js/runtime-main.25710301.js.map

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test_images/README.txt

@@ -0,0 +1,22 @@
+# Product Comparison API Testing Instructions
+
+To test the product comparison API, place test images in this directory. The test images can be:
+
+1. Cars (e.g., car1.jpg, car2.jpg)
+2. Electronics like smartphones or laptops (e.g., phone1.jpg, phone2.jpg)
+3. Any other products you want to compare
+
+## Testing Steps
+
+1. Start the Flask app: `python app.py`
+2. In a separate terminal, run the test script:
+   `python test_product_comparison_api.py --images test_images/image1.jpg test_images/image2.jpg`
+
+## Expected Results
+
+The test script will:
+- Send images to the API endpoint
+- Connect to the SSE stream to receive real-time updates
+- Display the final analysis results
+
+If successful, you should see structured output with product details, comparisons, and recommendations.

test_product_comparison_api.py

@@ -0,0 +1,151 @@
+#!/usr/bin/env python3
+"""
+Product Comparison API Test Script
+
+This script tests the product comparison API endpoints with local image files.
+It supports both the /api/product/compare/start and /api/product/compare/stream endpoints.
+
+Requirements:
+- pip install requests pillow sseclient-py
+
+Usage:
+    python test_product_comparison_api.py --images path/to/image1.jpg path/to/image2.jpg
+
+Example:
+    python test_product_comparison_api.py --images test_images/car1.jpg test_images/car2.jpg
+"""
+
+import requests
+import base64
+import json
+import time
+import os
+import sys
+from io import BytesIO
+from PIL import Image
+import argparse
+
+try:
+    import sseclient
+except ImportError:
+    print("Missing required package: sseclient-py")
+    print("Please install with: pip install sseclient-py")
+    sys.exit(1)
+
+def load_and_encode_image(image_path):
+    """Load an image file and convert to base64 string"""
+    try:
+        with Image.open(image_path) as img:
+            buffered = BytesIO()
+            img.save(buffered, format=img.format or "JPEG")
+            img_str = base64.b64encode(buffered.getvalue()).decode('utf-8')
+            return img_str
+    except Exception as e:
+        print(f"Error loading image {image_path}: {e}")
+        return None
+
+def test_product_comparison_api(base_url, image_paths, timeout=300):
+    """Test the product comparison API with provided images"""
+    print(f"Testing product comparison API with {len(image_paths)} images")
+    
+    # 1. Encode images as base64
+    images = []
+    for path in image_paths:
+        if not os.path.exists(path):
+            print(f"Error: Image file {path} not found")
+            return
+        
+        encoded = load_and_encode_image(path)
+        if encoded:
+            images.append(encoded)
+            print(f"Successfully encoded image: {path}")
+    
+    if not images:
+        print("No valid images to process")
+        return
+    
+    # 2. Start a comparison session
+    start_url = f"{base_url}/api/product/compare/start"
+    print(f"Sending request to {start_url}")
+    
+    try:
+        response = requests.post(
+            start_url,
+            json={"images": images}
+        )
+        
+        if not response.ok:
+            print(f"Error starting comparison: HTTP {response.status_code}")
+            print(response.text)
+            return
+        
+        data = response.json()
+        session_id = data.get('session_id')
+        
+        if not session_id:
+            print("Error: No session ID received")
+            return
+        
+        print(f"Session started with ID: {session_id}")
+        print(f"Status: {data.get('status')}")
+        
+        # 3. Connect to the streaming endpoint
+        stream_url = f"{base_url}/api/product/compare/stream/{session_id}"
+        print(f"Connecting to stream at {stream_url}")
+        
+        headers = {'Accept': 'text/event-stream'}
+        response = requests.get(stream_url, headers=headers, stream=True)
+        
+        client = sseclient.SSEClient(response)
+        
+        start_time = time.time()
+        result = None
+        
+        print("\n--- Streaming Updates ---")
+        try:
+            for event in client.events():
+                current_time = time.time()
+                if current_time - start_time > timeout:
+                    print("Timeout reached. Ending stream.")
+                    break
+                
+                try:
+                    data = json.loads(event.data)
+                    
+                    if 'message' in data:
+                        print(f"Message: {data['message']}")
+                    
+                    if 'status' in data:
+                        print(f"Status update: {data['status']}")
+                    
+                    if 'result' in data:
+                        print("\n--- Final Results ---")
+                        result = data['result']
+                        print(json.dumps(result, indent=2))
+                        break
+                    
+                    if 'error' in data:
+                        print(f"Error: {data['error']}")
+                        break
+                        
+                except json.JSONDecodeError:
+                    print(f"Error decoding JSON from event data: {event.data}")
+                    continue
+        except KeyboardInterrupt:
+            print("Stream monitoring interrupted by user")
+        
+        return result
+        
+    except Exception as e:
+        print(f"Error in API test: {e}")
+        return None
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Test Product Comparison API')
+    parser.add_argument('--url', default='http://localhost:5000', help='Base URL for the API')
+    parser.add_argument('--images', nargs='+', required=True, help='Paths to images for testing')
+    parser.add_argument('--timeout', type=int, default=300, help='Timeout in seconds')
+    
+    args = parser.parse_args()
+    
+    test_product_comparison_api(args.url, args.images, args.timeout)