app.py

import gradio as gr
from rag_engine import RAGEngine
import torch
import os
import logging
import traceback

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

def safe_search(query, max_results):
    """Wrapper function to handle errors gracefully"""
    try:
        rag = RAGEngine()
        results = rag.search_and_process(query, max_results)
        
        if 'error' in results:
            return f"# ❌ Error\nSorry, an error occurred while processing your search:\n```\n{results['error']}\n```"
            
        return format_results(results)
    except Exception as e:
        error_msg = f"An error occurred: {str(e)}\n\nTraceback:\n{traceback.format_exc()}"
        logger.error(error_msg)
        return f"# ❌ Error\nSorry, an error occurred while processing your search:\n```\n{str(e)}\n```"

def format_results(results):
    """Format search results for display"""
    if not results or not results.get('results'):
        return "# ⚠️ No Results\nNo search results were found. Please try a different query."
    
    insights = results.get('insights', {})
    output = []
    
    # Main Summary
    output.append("📝 Executive Summary")
    output.append("-" * 50)
    output.append(insights.get('main_summary', ''))
    output.append("\n")
    
    # Key Findings
    output.append("🔑 Key Findings")
    output.append("-" * 50)
    for i, point in enumerate(insights.get('key_findings', []), 1):
        output.append(f"{i}. {point}")
    output.append("\n")
    
    # Sources
    output.append("📚 Sources")
    output.append("-" * 50)
    for source in insights.get('sources', []):
        output.append(f"• {source.get('title', '')}")
        output.append(f"  {source.get('url', '')}")
    output.append("\n")
    
    # Follow-up Questions
    output.append("❓ Suggested Questions")
    output.append("-" * 50)
    for question in results.get('follow_up_questions', []):
        output.append(f"• {question}")
    
    # Add main results
    if 'results' in results:
        output.append("\n")
        output.append("📄 Detailed Results")
        output.append("-" * 50)
        output.append("\n")
        for i, result in enumerate(results['results'], 1):
            if not isinstance(result, dict):
                continue
                
            output.append(f"### {i}. ")
            if 'url' in result:
                title = result.get('title', 'Untitled')
                output.append(f"[{title}]({result['url']})\n")
            if 'summary' in result:
                output.append(f"\n{result['summary']}\n\n")
                
    # Add similar chunks if available
    if 'similar_chunks' in results:
        output.append("\n")
        output.append("🔍 Related Content")
        output.append("-" * 50)
        output.append("\n")
        for i, chunk in enumerate(results['similar_chunks'], 1):
            if not isinstance(chunk, dict):
                continue
                
            output.append(f"### Related {i}\n")
            if 'metadata' in chunk:
                meta = chunk['metadata']
                if 'title' in meta and 'url' in meta:
                    output.append(f"From [{meta['title']}]({meta['url']})\n")
            if 'content' in chunk:
                output.append(f"\n{chunk['content'][:200]}...\n\n")
    
    return "\n".join(output)

def create_demo():
    """Create the Gradio interface"""
    
    with gr.Blocks(title="Web Search + RAG") as demo:
        gr.Markdown("# 🔍 Intelligent Web Search")
        gr.Markdown("Search the web with AI-powered insights and analysis.")
        
        with gr.Row():
            with gr.Column():
                query = gr.Textbox(
                    label="Search Query",
                    placeholder="Enter your search query...",
                    lines=2
                )
                max_results = gr.Slider(
                    minimum=1,
                    maximum=10,
                    value=5,
                    step=1,
                    label="Number of Results"
                )
                search_button = gr.Button("🔍 Search")
            
        output = gr.Textbox(
            label="Search Results",
            lines=20
        )
        
        search_button.click(
            fn=safe_search,
            inputs=[query, max_results],
            outputs=output
        )
        
        gr.Examples(
            examples=[
                ["What is RAG in AI?", 5],
                ["Latest developments in quantum computing", 3],
                ["How does BERT work?", 5]
            ],
            inputs=[query, max_results]
        )
        
    return demo

# Create the demo
demo = create_demo()

# Launch for Spaces
demo.launch()