app.py

import gradio as gr
from transformers import pipeline

# 🔹 Load Qwen2.5-14B-Instruct-1M with a pipeline
pipe = pipeline("text-generation", model="Qwen/Qwen2.5-14B-Instruct-1M")

# 🎯 Function to extract interests from user input
def extract_interests(text):
    prompt = f"Extract 3-5 relevant interests from this request: '{text}'. Focus on hobbies and product preferences."
    
    response = pipe(prompt, max_length=50, num_return_sequences=1)
    interests = response[0]["generated_text"].replace(prompt, "").strip()

    return interests.split(", ")

# 🎁 Web search for gift suggestions
def search_gifts(interests):
    query = "+".join(interests)
    amazon_url = f"https://www.amazon.in/s?k={query}"
    flipkart_url = f"https://www.flipkart.com/search?q={query}"
    igp_url = f"https://www.igp.com/search?q={query}"
    indiamart_url = f"https://dir.indiamart.com/search.mp?ss={query}"

    return {
        "Amazon": amazon_url,
        "Flipkart": flipkart_url,
        "IGP": igp_url,
        "IndiaMart": indiamart_url
    }

# 🎯 Main function for gift recommendation
def recommend_gifts(text):
    if not text:
        return "Please enter a description."

    interests = extract_interests(text)
    links = search_gifts(interests)

    return {
        "Predicted Interests": interests,
        "Gift Suggestions": links
    }

# 🎨 Gradio UI for easy interaction
demo = gr.Interface(
    fn=recommend_gifts, 
    inputs="text", 
    outputs="json",
    title="🎁 AI Gift Recommender",
    description="Enter details about the person you are buying a gift for, and get personalized suggestions with shopping links!",
)

# 🚀 Launch Gradio App
if __name__ == "__main__":
    demo.launch()