product_recommender.py

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