app.py

import openai
import gradio as gr
from full_chain import get_response
import os

api_key = os.getenv("OPENAI_API_KEY")
client = openai.OpenAI(api_key=api_key)

def create_hyperlink(url, title, domain):
    """Create HTML hyperlink with domain information."""
    return f"<a href='{url}' target='_blank'>{title}</a> ({domain})"

def predict(message, history):
    """Process user message and return response with hyperlinked sources."""
    # Get response and source information
    responder, links, titles, domains, published_dates = get_response(message, rerank_type="crossencoder")
    
    # The responder already contains the formatted response with numbered citations
    # We just need to add the hyperlinked references at the bottom
    hyperlinks = []
    for i, (link, title, domain, published_date) in enumerate(zip(links, titles, domains, published_dates), 1):
        hyperlink = f"[{i}] {create_hyperlink(link, title, domain)} {published_date}"
        hyperlinks.append(hyperlink)

    # Split the responder to separate the response from its references
    response_parts = responder.split("References:")
    main_response = response_parts[0].strip()
    
    # Combine the response with hyperlinked references
    final_response = (
        f"{main_response}\n\n"
        f"References:\n"
        f"{chr(10).join(hyperlinks)}"
    )
    
    return final_response

# Initialize and launch Gradio interface
gr.ChatInterface(
    predict,
    examples=[
        "How many Americans Smoke?",
        "What are some measures taken by the Indian Government to reduce the smoking population?",
        "Does smoking negatively affect my health?"
    ],
    title="Tobacco Information Assistant",
    description="Ask questions about tobacco-related topics and get answers with reliable sources."
).launch()