Update static React build to render vector DB result images in search dialog

api.py

@@ -1,3 +1,4 @@
+# -*- coding: utf-8 -*-
 from flask import Flask, request, jsonify, send_from_directory
 import torch
 from PIL import Image
@@ -12,6 +13,12 @@ from matplotlib.patches import Rectangle
 import time
 from flask_cors import CORS
 import json
+
+# Fix for SQLite3 version compatibility with ChromaDB
+import sys
+import pysqlite3
+sys.modules['sqlite3'] = pysqlite3
+
 import chromadb
 from chromadb.utils import embedding_functions
 
@@ -591,6 +598,12 @@ def add_to_collection():
         embedding = generate_image_embedding(image)
         if embedding is None:
             return jsonify({"error": "Failed to generate image embedding"})
+            
+        # 이미지 데이터를 base64로 인코딩하여 메타데이터에 추가
+        buffered = BytesIO()
+        image.save(buffered, format="JPEG")
+        img_str = base64.b64encode(buffered.getvalue()).decode('utf-8')
+        metadata['image_data'] = img_str
 
         # 이미지를 DB에 추가
         image_collection.add(
@@ -711,11 +724,17 @@ def add_detected_objects():
                     "height": height
                 })
                 
+                # 객체 이미지를 base64로 인코딩
+                buffered = BytesIO()
+                object_image.save(buffered, format="JPEG")
+                img_str = base64.b64encode(buffered.getvalue()).decode('utf-8')
+                
                 metadata = {
                     "image_id": image_id,
                     "class": obj.get('class', ''),
                     "confidence": obj.get('confidence', 0),
-                    "bbox": bbox_json  # JSON 문자열로 저장
+                    "bbox": bbox_json,  # JSON 문자열로 저장
+                    "image_data": img_str  # 이미지 데이터 추가
                 }
                 
                 object_ids.append(object_id)
@@ -899,16 +918,18 @@ def format_object_results(results):
                 if 'distances' in results and len(results['distances']) > 0:
                     result_item["distance"] = float(results['distances'][0][i])
                 
-                # 이미지 가져오기 시도
-                try:
-                    # 이미지 ID를 통해 원본 이미지 가져오기
-                    image_id = metadata.get('image_id')
-                    if image_id:
-                        print(f"[DEBUG] Getting image for object {obj_id} with image_id {image_id}")
-                        # 이미지 가져오기 로직 추가 필요
-                
-                except Exception as e:
-                    print(f"[DEBUG] Error getting image for result {i}: {e}")
+                # 이미지 데이터가 이미 메타데이터에 있는지 확인
+                if 'image_data' not in metadata:
+                    try:
+                        # 이미지 ID를 통해 원본 이미지 가져오기 시도
+                        image_id = metadata.get('image_id')
+                        if image_id:
+                            print(f"[DEBUG] Image data not found in metadata for object {obj_id} with image_id {image_id}")
+                            # 이미지 데이터가 없는 경우 로그만 출력
+                    except Exception as e:
+                        print(f"[DEBUG] Error checking image data for result {i}: {e}")
+                else:
+                    print(f"[DEBUG] Image data found in metadata for object {obj_id}")
                 
                 formatted_results.append(result_item)
             except Exception as e:

sqlite_fix.py

@@ -0,0 +1,5 @@
+# -*- coding: utf-8 -*-
+# This script overrides the default sqlite3 with pysqlite3 to meet ChromaDB requirements
+import sys
+import pysqlite3
+sys.modules['sqlite3'] = pysqlite3

static/asset-manifest.json

@@ -1,24 +1,24 @@
 {
   "files": {
     "main.css": "/static/css/main.59c2a54e.chunk.css",
-    "main.js": "/static/js/main.d46e1c99.chunk.js",
-    "main.js.map": "/static/js/main.d46e1c99.chunk.js.map",
-    "runtime-main.js": "/static/js/runtime-main.464c1afd.js",
-    "runtime-main.js.map": "/static/js/runtime-main.464c1afd.js.map",
-    "static/js/2.8246dbc7.chunk.js": "/static/js/2.8246dbc7.chunk.js",
-    "static/js/2.8246dbc7.chunk.js.map": "/static/js/2.8246dbc7.chunk.js.map",
-    "static/js/3.b132cefa.chunk.js": "/static/js/3.b132cefa.chunk.js",
-    "static/js/3.b132cefa.chunk.js.map": "/static/js/3.b132cefa.chunk.js.map",
+    "main.js": "/static/js/main.ad7f086c.chunk.js",
+    "main.js.map": "/static/js/main.ad7f086c.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/2.252de3c4.chunk.js": "/static/js/2.252de3c4.chunk.js",
+    "static/js/2.252de3c4.chunk.js.map": "/static/js/2.252de3c4.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.db9a464ae73aec55f712809be7313c56.js": "/precache-manifest.db9a464ae73aec55f712809be7313c56.js",
+    "precache-manifest.053b14ee2ebd7996a78e6e055f2144fe.js": "/precache-manifest.053b14ee2ebd7996a78e6e055f2144fe.js",
     "service-worker.js": "/service-worker.js",
     "static/css/main.59c2a54e.chunk.css.map": "/static/css/main.59c2a54e.chunk.css.map",
-    "static/js/2.8246dbc7.chunk.js.LICENSE.txt": "/static/js/2.8246dbc7.chunk.js.LICENSE.txt"
+    "static/js/2.252de3c4.chunk.js.LICENSE.txt": "/static/js/2.252de3c4.chunk.js.LICENSE.txt"
   },
   "entrypoints": [
-    "static/js/runtime-main.464c1afd.js",
-    "static/js/2.8246dbc7.chunk.js",
+    "static/js/runtime-main.25710301.js",
+    "static/js/2.252de3c4.chunk.js",
     "static/css/main.59c2a54e.chunk.css",
-    "static/js/main.d46e1c99.chunk.js"
+    "static/js/main.ad7f086c.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.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:"b132cefa"}[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/2.8246dbc7.chunk.js"></script><script src="/static/js/main.d46e1c99.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/2.252de3c4.chunk.js"></script><script src="/static/js/main.ad7f086c.chunk.js"></script></body></html>

static/js/2.8246dbc7.chunk.js.LICENSE.txt

@@ -1,58 +0,0 @@
-/*
-object-assign
-(c) Sindre Sorhus
-@license MIT
-*/
-
-/**
- * A better abstraction over CSS.
- *
- * @copyright Oleg Isonen (Slobodskoi) / Isonen 2014-present
- * @website https://github.com/cssinjs/jss
- * @license MIT
- */
-
-/** @license React v0.19.1
- * scheduler.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 v16.13.1
- * 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.
- */
-
-/** @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
- * LICENSE file in the root directory of this source tree.
- */
-
-/** @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.
- */

static/js/3.b132cefa.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-image', {\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            image_id: 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          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          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      // 백엔드는 {success, searchType, results} 구조로 응답하므로 results 배열만 추출\n      if (result.success && Array.isArray(result.results)) {\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: ${err.message}`);\n    } finally {\n      setIsSearching(false);\n    }\n  };\n  \n  // Generate UUID for image ID\n  const generateUUID = () => {\n    return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, function(c) {\n      const r = Math.random() * 16 | 0;\n      const v = c === 'x' ? r : (r & 0x3 | 0x8);\n      return v.toString(16);\n    });\n  };\n  \n  // Render search results\n  const renderSearchResults = () => {\n    if (searchResults.length === 0) {\n      return (\n        <Typography variant=\"body1\">No results found.</Typography>\n      );\n    }\n    \n    return (\n      <Grid container spacing={2}>\n        {searchResults.map((result, index) => {\n          const similarity = (1 - result.distance) * 100;\n          \n          return (\n            <Grid item xs={12} sm={6} key={index}>\n              <Card className={classes.resultCard}>\n                <CardMedia\n                  className={classes.resultImage}\n                  image={`data:image/jpeg;base64,${result.image}`}\n                  title={`Result ${index + 1}`}\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              searchResults.length > 0 && renderSearchResults()\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, 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 './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          </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/js/main.d46e1c99.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-image', {\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            image_id: 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          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          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      // 백엔드는 {success, searchType, results} 구조로 응답하므로 results 배열만 추출\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                {/* 백엔드에서 이미지 데이터가 없으므로 이미지 대신 플레이스홀더 표시 */}\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                <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, 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 './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! 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static/{precache-manifest.4d423e5fa875a8623ac314ac294ba8fb.js → precache-manifest.053b14ee2ebd7996a78e6e055f2144fe.js}

@@ -1,10 +1,10 @@
 self.__precacheManifest = (self.__precacheManifest || []).concat([
   {
-    "revision": "846149ffb9155ebf2346da23b1ce9fba",
+    "revision": "55f7de6a753a31dd65b7dc1b57b2f81a",
     "url": "/index.html"
   },
   {
-    "revision": "fb834cb258d21ecab74a",
+    "revision": "289219e1c2ed943c38e7",
     "url": "/static/css/main.59c2a54e.chunk.css"
   },
   {
@@ -20,8 +20,8 @@ self.__precacheManifest = (self.__precacheManifest || []).concat([
     "url": "/static/js/3.9013e23f.chunk.js"
   },
   {
-    "revision": "fb834cb258d21ecab74a",
-    "url": "/static/js/main.c119e899.chunk.js"
+    "revision": "289219e1c2ed943c38e7",
+    "url": "/static/js/main.ad7f086c.chunk.js"
   },
   {
     "revision": "d8c310b0ac7ffa6d8151",

static/precache-manifest.db9a464ae73aec55f712809be7313c56.js

@@ -1,30 +0,0 @@
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-  {
-    "revision": "9218709d4acf7c23e778a4e8f08c8b3d",
-    "url": "/index.html"
-  },
-  {
-    "revision": "d0f6ae6d190eb7184333",
-    "url": "/static/css/main.59c2a54e.chunk.css"
-  },
-  {
-    "revision": "a98df5c2c54b35a1cd82",
-    "url": "/static/js/2.8246dbc7.chunk.js"
-  },
-  {
-    "revision": "89a1b2dcd30c03705b2bceeb141b76b6",
-    "url": "/static/js/2.8246dbc7.chunk.js.LICENSE.txt"
-  },
-  {
-    "revision": "e70f83fa947a6ee6b240",
-    "url": "/static/js/3.b132cefa.chunk.js"
-  },
-  {
-    "revision": "d0f6ae6d190eb7184333",
-    "url": "/static/js/main.d46e1c99.chunk.js"
-  },
-  {
-    "revision": "36de33bb3584e2071963",
-    "url": "/static/js/runtime-main.464c1afd.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.db9a464ae73aec55f712809be7313c56.js"
+  "/precache-manifest.053b14ee2ebd7996a78e6e055f2144fe.js"
 );
 
 self.addEventListener('message', (event) => {

static/{js/main.c119e899.chunk.js → static/js/main.ad7f086c.chunk.js}

@@ -1,2 +1,2 @@
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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      // 백엔드는 {success, searchType, results} 구조로 응답하므로 results 배열만 추출\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, 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 './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          </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":""}