Delete app.py

app.py

@@ -1,273 +0,0 @@
-# app.py
-import torch
-from transformers import InstructBlipProcessor, InstructBlipForConditionalGeneration
-import gradio as gr
-from PIL import Image
-import re
-from typing import List, Tuple
-import os
-
-
-class RiverPollutionAnalyzer:
-    def __init__(self):
-        # Check if CUDA is available
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
-
-        # Load model with appropriate settings for Spaces
-        self.processor = InstructBlipProcessor.from_pretrained(
-            "Salesforce/instructblip-vicuna-7b"
-        )
-
-        # Simplified model loading for Spaces compatibility
-        self.model = InstructBlipForConditionalGeneration.from_pretrained(
-            "Salesforce/instructblip-vicuna-7b",
-            device_map="auto",
-            torch_dtype=torch.float16 if self.device == "cuda" else torch.float32,
-        ).to(self.device)
-
-        self.pollutants = [
-            "plastic waste",
-            "chemical foam",
-            "industrial discharge",
-            "sewage water",
-            "oil spill",
-            "organic debris",
-            "construction waste",
-            "medical waste",
-            "floating trash",
-            "algal bloom",
-            "toxic sludge",
-            "agricultural runoff",
-        ]
-
-        self.severity_descriptions = {
-            1: "Minimal pollution - Slightly noticeable",
-            2: "Minor pollution - Small amounts visible",
-            3: "Moderate pollution - Clearly visible",
-            4: "Significant pollution - Affecting water quality",
-            5: "Heavy pollution - Obvious environmental impact",
-            6: "Severe pollution - Large accumulation",
-            7: "Very severe pollution - Major ecosystem impact",
-            8: "Extreme pollution - Dangerous levels",
-            9: "Critical pollution - Immediate action needed",
-            10: "Disaster level - Ecological catastrophe",
-        }
-
-    def analyze_image(self, image):
-        """Analyze river pollution with robust parsing"""
-        if not isinstance(image, Image.Image):
-            image = Image.fromarray(image)
-
-        prompt = """Analyze this river pollution scene and provide:
-1. List ALL visible pollutants ONLY from: [plastic waste, chemical foam, industrial discharge, sewage water, oil spill, organic debris, construction waste, medical waste, floating trash, algal bloom, toxic sludge, agricultural runoff]
-2. Estimate pollution severity from 1-10
-
-Respond EXACTLY in this format:
-Pollutants: [comma separated list]
-Severity: [number]"""
-
-        inputs = self.processor(images=image, text=prompt, return_tensors="pt").to(
-            self.device
-        )
-
-        with torch.no_grad():
-            outputs = self.model.generate(
-                **inputs,
-                max_new_tokens=200,
-                temperature=0.5,
-                top_p=0.85,
-                do_sample=True,
-            )
-
-        analysis = self.processor.batch_decode(outputs, skip_special_tokens=True)[0]
-        pollutants, severity = self._parse_response(analysis)
-        return self._format_analysis(pollutants, severity)
-
-    def _parse_response(self, analysis: str) -> Tuple[List[str], int]:
-        """Robust parsing of model response"""
-        pollutants = []
-        severity = 3
-
-        # Extract pollutants
-        pollutant_match = re.search(
-            r"(?i)(pollutants?|contaminants?)[:\s]*\[?(.*?)(?:\]|Severity|severity|$)",
-            analysis,
-        )
-
-        if pollutant_match:
-            pollutants_str = pollutant_match.group(2).strip()
-            pollutants = [
-                p.strip().lower()
-                for p in re.split(r"[,;]|\band\b", pollutants_str)
-                if p.strip().lower() in self.pollutants
-            ]
-
-        # Extract severity
-        severity_match = re.search(r"(?i)(severity|level)[:\s]*(\d{1,2})", analysis)
-
-        if severity_match:
-            try:
-                severity = min(max(int(severity_match.group(2)), 1), 10)
-            except:
-                severity = self._calculate_severity(pollutants)
-        else:
-            severity = self._calculate_severity(pollutants)
-
-        return pollutants, severity
-
-    def _calculate_severity(self, pollutants: List[str]) -> int:
-        """Weighted severity calculation"""
-        if not pollutants:
-            return 1
-
-        weights = {
-            "medical waste": 3,
-            "toxic sludge": 3,
-            "oil spill": 2.5,
-            "chemical foam": 2,
-            "industrial discharge": 2,
-            "sewage water": 2,
-            "plastic waste": 1.5,
-            "construction waste": 1.5,
-            "algal bloom": 1.5,
-            "agricultural runoff": 1.5,
-            "floating trash": 1,
-            "organic debris": 1,
-        }
-
-        avg_weight = sum(weights.get(p, 1) for p in pollutants) / len(pollutants)
-        return min(10, max(1, round(avg_weight * 3)))
-
-    def _format_analysis(self, pollutants: List[str], severity: int) -> str:
-        """Generate formatted report"""
-        severity_bar = f"""📊 Severity: {severity}/10
-{"█" * severity}{"░" * (10 - severity)}
-{self.severity_descriptions.get(severity, "")}"""
-
-        pollutants_list = (
-            "\n🔍 No pollutants detected"
-            if not pollutants
-            else "\n".join(
-                f"{i}. {p.capitalize()}" for i, p in enumerate(pollutants[:5], 1)
-            )
-        )
-
-        return f"""🌊 River Pollution Analysis 🌊
-{pollutants_list}
-{severity_bar}"""
-
-    def analyze_chat(self, message: str) -> str:
-        """Handle chat questions about pollution"""
-        # Simple implementation - you can expand this
-        if any(word in message.lower() for word in ["hello", "hi", "hey"]):
-            return "Hello! I'm a river pollution analyzer. Ask me about pollution types or upload an image for analysis."
-        elif "pollution" in message.lower():
-            return "Common river pollutants include: plastic waste, chemical foam, industrial discharge, sewage water, and oil spills."
-        else:
-            return "I can answer questions about river pollution. Try asking about pollution types or upload an image for analysis."
-
-
-# Initialize analyzer
-analyzer = RiverPollutionAnalyzer()
-
-css = """
-.header {
-    text-align: center;
-    padding: 20px;
-    background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
-    border-radius: 10px;
-    margin-bottom: 20px;
-}
-
-.side-by-side {
-    display: flex;
-    gap: 20px;
-}
-
-.left-panel, .right-panel {
-    flex: 1;
-}
-
-.analysis-box {
-    padding: 20px;
-    background: #f8f9fa;
-    border-radius: 10px;
-    margin-top: 20px;
-    border: 1px solid #dee2e6;
-}
-
-.chat-container {
-    background: #f8f9fa;
-    padding: 20px;
-    border-radius: 10px;
-    height: 100%;
-}
-"""
-
-with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
-    with gr.Column(elem_classes="header"):
-        gr.Markdown("# 🌍 River Pollution Analyzer")
-        gr.Markdown("### AI-powered water pollution detection")
-
-    with gr.Row(elem_classes="side-by-side"):
-        # Left Panel
-        with gr.Column(elem_classes="left-panel"):
-            with gr.Group():
-                image_input = gr.Image(
-                    type="pil", label="Upload River Image", height=300
-                )
-                analyze_btn = gr.Button("🔍 Analyze Pollution", variant="primary")
-
-            with gr.Group(elem_classes="analysis-box"):
-                gr.Markdown("### 📊 Analysis report")
-                analysis_output = gr.Markdown()
-
-        # Right Panel
-        with gr.Column(elem_classes="right-panel"):
-            with gr.Group(elem_classes="chat-container"):
-                chatbot = gr.Chatbot(label="Pollution Analysis Q&A", height=400)
-                with gr.Row():
-                    chat_input = gr.Textbox(
-                        placeholder="Ask about pollution sources...",
-                        label="Your Question",
-                        container=False,
-                        scale=5,
-                    )
-                    chat_btn = gr.Button("💬 Ask", variant="secondary", scale=1)
-                clear_btn = gr.Button("🧹 Clear Chat History", size="sm")
-
-    analyze_btn.click(
-        analyzer.analyze_image, inputs=image_input, outputs=analysis_output
-    )
-
-    chat_input.submit(
-        lambda msg, chat: ("", chat + [(msg, analyzer.analyze_chat(msg))]),
-        inputs=[chat_input, chatbot],
-        outputs=[chat_input, chatbot],
-    )
-
-    chat_btn.click(
-        lambda msg, chat: ("", chat + [(msg, analyzer.analyze_chat(msg))]),
-        inputs=[chat_input, chatbot],
-        outputs=[chat_input, chatbot],
-    )
-
-    clear_btn.click(lambda: None, outputs=[chatbot])
-
-    gr.Examples(
-        examples=[
-            [
-                "https://huggingface.co/spaces/username/your-space-name/resolve/main/examples/polluted_river_1.jpg"
-            ],
-            [
-                "https://huggingface.co/spaces/username/your-space-name/resolve/main/examples/polluted_river_2.jpg"
-            ],
-        ],
-        inputs=image_input,
-        outputs=analysis_output,
-        fn=analyzer.analyze_image,
-        cache_examples=True,
-        label="Try example images:",
-    )
-
-demo.launch()