Delete app.py

app.py

@@ -1,333 +0,0 @@
-"""
-Visual Product Matcher - Hugging Face Spaces Backend
-AI-powered product similarity search using CLIP model
-"""
-
-import gradio as gr
-from flask import Flask, request, jsonify
-from sentence_transformers import SentenceTransformer
-from PIL import Image
-import requests
-import json
-import numpy as np
-import io
-import base64
-import os
-import threading
-
-# Initialize Flask app for API endpoints
-flask_app = Flask(__name__)
-
-# Load model
-print("🔄 Loading CLIP model...")
-model = SentenceTransformer("sentence-transformers/clip-ViT-B-32")
-print("✅ Model loaded successfully!")
-
-# Load products database
-print("📂 Loading products database...")
-with open("server/products.json", "r", encoding="utf-8") as f:
-    products = json.load(f)
-print(f"✅ Loaded {len(products)} products")
-
-# Configuration
-IMAGE_FOLDER = "server/assets/product"
-
-# ============================================================================
-# HELPER FUNCTIONS
-# ============================================================================
-
-def cosine_similarity(a, b):
-    """Calculate cosine similarity between two vectors"""
-    a = np.array(a)
-    b = np.array(b)
-    return float(np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b)))
-
-
-def load_image_from_base64(data_string):
-    """Load image from base64 string"""
-    try:
-        if "," in data_string:
-            image_bytes = base64.b64decode(data_string.split(",")[-1])
-        else:
-            image_bytes = base64.b64decode(data_string)
-        return Image.open(io.BytesIO(image_bytes)).convert("RGB")
-    except Exception as e:
-        raise ValueError(f"Failed to decode base64 image: {str(e)}")
-
-
-def load_image_from_url(url):
-    """Load image from URL"""
-    if url.startswith("data:image"):
-        return load_image_from_base64(url)
-    
-    headers = {'User-Agent': 'Mozilla/5.0'}
-    response = requests.get(url, timeout=15, headers=headers)
-    response.raise_for_status()
-    return Image.open(io.BytesIO(response.content)).convert("RGB")
-
-
-def search_similar_products(image, top_k=20):
-    """
-    Core search function - finds similar products
-    Returns list of products sorted by similarity
-    """
-    query_embedding = model.encode(image)
-    
-    results = []
-    for product in products:
-        if "embedding" not in product:
-            continue
-        
-        score = cosine_similarity(query_embedding, product["embedding"])
-        
-        # Use relative path for HF Spaces
-        image_url = f"/file={IMAGE_FOLDER}/{product['image']}"
-        
-        results.append({
-            "id": product.get("id", ""),
-            "name": product["name"],
-            "price": product["price"],
-            "category": product.get("category", ""),
-            "description": product.get("description", ""),
-            "image": image_url,
-            "similarity": score
-        })
-    
-    results.sort(key=lambda x: x["similarity"], reverse=True)
-    return results[:top_k]
-
-
-# ============================================================================
-# FLASK API ENDPOINTS (for Vercel integration)
-# ============================================================================
-
-@flask_app.route("/api/health", methods=["GET"])
-def health_check():
-    """Health check endpoint"""
-    return jsonify({
-        "status": "ok",
-        "service": "Visual Product Matcher",
-        "model": "sentence-transformers/clip-ViT-B-32",
-        "products_loaded": len(products)
-    })
-
-
-@flask_app.route("/api/match", methods=["POST", "OPTIONS"])
-def match_products():
-    """
-    Main API endpoint for product matching
-    Accepts: {"image": "base64_data"} or {"imageUrl": "http://..."}
-    Returns: List of matching products
-    """
-    # Handle CORS preflight
-    if request.method == "OPTIONS":
-        response = jsonify({"status": "ok"})
-        response.headers.add("Access-Control-Allow-Origin", "*")
-        response.headers.add("Access-Control-Allow-Headers", "Content-Type")
-        response.headers.add("Access-Control-Allow-Methods", "POST, OPTIONS")
-        return response
-    
-    try:
-        data = request.json
-        
-        if not data:
-            return jsonify({"error": "No JSON data provided"}), 400
-        
-        image_data = data.get("image")
-        image_url = data.get("imageUrl")
-        
-        if not image_data and not image_url:
-            return jsonify({"error": "No image provided"}), 400
-        
-        # Load image
-        if image_data:
-            image = load_image_from_base64(image_data)
-        else:
-            image = load_image_from_url(image_url)
-        
-        # Search for similar products
-        results = search_similar_products(image, top_k=20)
-        
-        # Add CORS headers
-        response = jsonify(results)
-        response.headers.add("Access-Control-Allow-Origin", "*")
-        response.headers.add("Access-Control-Allow-Headers", "Content-Type")
-        
-        return response
-        
-    except Exception as e:
-        error_response = jsonify({"error": str(e)})
-        error_response.headers.add("Access-Control-Allow-Origin", "*")
-        return error_response, 400
-
-
-# ============================================================================
-# GRADIO UI (for web interface)
-# ============================================================================
-
-def gradio_search(image):
-    """Gradio interface function"""
-    if image is None:
-        return "⚠️ Please upload an image", []
-    
-    try:
-        results = search_similar_products(image, top_k=6)
-        
-        # Format text output
-        output_lines = ["# 🔍 Search Results\n"]
-        
-        for i, product in enumerate(results, 1):
-            output_lines.append(f"### {i}. {product['name']}")
-            output_lines.append(f"**Price:** ${product['price']}")
-            output_lines.append(f"**Similarity:** {product['similarity']:.1%}")
-            output_lines.append(f"**Category:** {product['category']}")
-            if product['description']:
-                output_lines.append(f"*{product['description']}*")
-            output_lines.append("---")
-        
-        text_output = "\n".join(output_lines)
-        
-        # Get images for gallery
-        image_paths = []
-        for product in results:
-            img_path = f"{IMAGE_FOLDER}/{product['image'].split('/')[-1]}"
-            if os.path.exists(img_path):
-                image_paths.append(img_path)
-        
-        return text_output, image_paths
-        
-    except Exception as e:
-        return f"❌ Error: {str(e)}", []
-
-
-# Create Gradio interface
-with gr.Blocks(
-    title="🔍 Visual Product Matcher",
-    theme=gr.themes.Soft(),
-    css=".gradio-container {max-width: 1200px; margin: auto;}"
-) as gradio_interface:
-    
-    gr.Markdown(
-        """
-        # 🔍 Visual Product Matcher
-        
-        Upload a product image to find similar items!
-        
-        **Features:**
-        - 🤖 AI-powered similarity search using CLIP
-        - 📸 Upload images or use camera
-        - ⚡ Instant results
-        
-        ---
-        """
-    )
-    
-    with gr.Row():
-        with gr.Column(scale=1):
-            image_input = gr.Image(
-                type="pil",
-                label="📸 Upload Product Image",
-                sources=["upload", "webcam", "clipboard"]
-            )
-            
-            search_btn = gr.Button(
-                "🔍 Search Similar Products",
-                variant="primary",
-                size="lg"
-            )
-            
-            gr.Markdown(
-                """
-                ### 💡 Tips:
-                - Upload clear product images
-                - Works best with centered products
-                - Supported: JPG, PNG, WebP
-                """
-            )
-        
-        with gr.Column(scale=1):
-            text_output = gr.Markdown(label="Results")
-    
-    with gr.Row():
-        gallery = gr.Gallery(
-            label="🎨 Similar Products",
-            columns=3,
-            rows=2,
-            height="auto",
-            object_fit="contain"
-        )
-    
-    search_btn.click(
-        fn=gradio_search,
-        inputs=[image_input],
-        outputs=[text_output, gallery]
-    )
-    
-    # Example images
-    example_images = []
-    for i in [1, 2, 10]:
-        img_path = f"{IMAGE_FOLDER}/product_{i}.jpg"
-        if os.path.exists(img_path):
-            example_images.append([img_path])
-    
-    if example_images:
-        gr.Markdown("---\n### 📌 Try These Examples:")
-        gr.Examples(examples=example_images, inputs=image_input)
-    
-    gr.Markdown(
-        """
-        ---
-        
-        ### 🔌 API Access
-        
-        **Endpoint:** `POST /api/match`
-        
-        **Request:**
-        ```json
-        {
-          "image": "base64_image_data",
-          "imageUrl": "https://example.com/image.jpg"
-        }
-        ```
-        
-        **Response:** Array of similar products with similarity scores
-        
-        ---
-        
-        Built with ❤️ using Sentence Transformers CLIP
-        """
-    )
-
-
-# ============================================================================
-# LAUNCH APP
-# ============================================================================
-
-def run_flask():
-    """Run Flask API in background thread"""
-    flask_app.run(host="0.0.0.0", port=7860)
-
-
-if __name__ == "__main__":
-    # Start Flask API in background
-    flask_thread = threading.Thread(target=run_flask, daemon=True)
-    flask_thread.start()
-    
-    print("\n" + "="*60)
-    print("🚀 Visual Product Matcher Started!")
-    print("="*60)
-    print(f"📊 Products loaded: {len(products)}")
-    print(f"🤖 Model: sentence-transformers/clip-ViT-B-32")
-    print("="*60)
-    print("\n🌐 Gradio UI: Available at Space URL")
-    print("🔌 API Endpoints:")
-    print("   - GET  /api/health")
-    print("   - POST /api/match")
-    print("="*60 + "\n")
-    
-    # Launch Gradio
-    gradio_interface.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=False
-    )