app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch
import requests

# Load DeepSeek-R1 model with trust_remote_code enabled
model_name = "deepseek-ai/DeepSeek-R1"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

# Ensure compatibility with `flash_attn` and force proper dtype
try:
    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        torch_dtype=torch.float16,  # Forces float16 to prevent fp8 issue
        device_map="auto",
        trust_remote_code=True
    )
except ImportError as e:
    raise RuntimeError("Missing required dependency: flash_attn. Install with `pip install flash_attn`") from e

# Use a text-generation pipeline for better inference
generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0 if torch.cuda.is_available() else -1)


# 🎯 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."

    # Generate model output
    response = generator(prompt, max_length=50, num_return_sequences=1)
    interests = response[0]["generated_text"].replace(prompt, "").strip()

    return interests.split(", ")  # Convert to a list of keywords


# 🎁 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)  # Extract interests using DeepSeek R1
    links = search_gifts(interests)  # Get shopping links

    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()