Create product_recommender.py

product_recommender.py

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+import gradio as gr
+from typing import Dict, List
+import json
+import urllib.parse
+from product_recommender import ProductRecommender
+
+# Initialize the recommender
+recommender = ProductRecommender()
+
+# Sample product database - replace with your actual database
+product_database = [
+    {
+        "description": "FIFA 24 EA Sports Football Game",
+        "category": "games",
+        "features": ["sports", "multiplayer"],
+        "price": 4999
+    },
+    # Add more products
+]
+
+def get_recommendations(text: str) -> Dict:
+    try:
+        # Get recommendations using the multi-model system
+        recommendations = recommender.get_recommendations(text, product_database)
+        
+        # Format recommendations with shopping links
+        formatted_recommendations = []
+        for rec in recommendations:
+            query = urllib.parse.quote(rec['description'])
+            formatted_rec = {
+                "product": rec['description'],
+                "price": f"₹{rec['price']}",
+                "similarity_score": f"{rec.get('similarity', 0):.2f}",
+                "shopping_links": {
+                    "Amazon": f"https://www.amazon.in/s?k={query}",
+                    "Flipkart": f"https://www.flipkart.com/search?q={query}",
+                    "IGP": f"https://www.igp.com/search?q={query}"
+                }
+            }
+            formatted_recommendations.append(formatted_rec)
+        
+        return {
+            "recommendations": formatted_recommendations
+        }
+        
+    except Exception as e:
+        return {"error": str(e)}
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=get_recommendations,
+    inputs=gr.Textbox(lines=3),
+    outputs=gr.JSON(),
+    title="🎁 Smart Gift Recommender",
+    description="Get personalized gift suggestions with direct shopping links!"
+)
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)
+else:
+    app = demo.app