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import gradio as gr
from rag_engine import RAGEngine
import torch
import os
import logging
import traceback
import asyncio
# 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 = asyncio.run(rag.search_and_process(query, max_results))
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:
return "# ⚠️ No Results\nNo search results were found. Please try a different query."
formatted = f"# 🔍 Search Results\n\n"
# Add insights section
if 'insights' in results:
formatted += f"## 💡 Key Insights\n{results['insights']}\n\n"
# Add follow-up questions
if 'follow_up_questions' in results:
formatted += "## ❓ Follow-up Questions\n"
for q in results['follow_up_questions']:
if q and q.strip():
formatted += f"- {q.strip()}\n"
formatted += "\n"
# Add main results
if 'results' in results:
formatted += "## 📄 Detailed Results\n\n"
for i, result in enumerate(results['results'], 1):
formatted += f"### {i}. "
if 'url' in result:
formatted += f"[{result.get('title', 'Untitled')}]({result['url']})\n"
else:
formatted += f"{result.get('title', 'Untitled')}\n"
if result.get('processed_content'):
content = result['processed_content']
if 'summary' in content:
formatted += f"**Summary:** {content['summary']}\n\n"
if content.get('metadata', {}).get('description'):
formatted += f"**Description:** {content['metadata']['description']}\n\n"
if content.get('content_type') == 'code':
formatted += f"**Code Analysis:** {content.get('explanation', '')}\n\n"
else:
formatted += f"**Detailed Explanation:** {content.get('explanation', '')}\n\n"
if 'snippet' in result:
formatted += f"**Snippet:** {result['snippet']}\n\n"
formatted += "---\n\n"
# Add similar queries if available
if results.get('similar_queries'):
formatted += "## 🔄 Related Searches\n"
for query in results['similar_queries']:
if isinstance(query, dict) and 'query' in query:
formatted += f"- {query['query']}\n"
elif isinstance(query, str):
formatted += f"- {query}\n"
return formatted
def create_demo():
"""Create the Gradio interface"""
# Create cache directory
os.makedirs(".cache", exist_ok=True)
demo = gr.Blocks(
title="AI-Powered Search Engine",
css="""
.gradio-container {max-width: 1200px !important}
.markdown-text {font-size: 16px !important}
"""
)
with demo:
gr.Markdown("""
# 🔍 Intelligent Web Search Engine
This advanced search engine uses AI to provide deep understanding of search results:
- 🧠 Multi-model AI analysis
- 📊 Semantic search and caching
- 💡 Automatic insights generation
- ❓ Smart follow-up questions
- 🔄 Related searches
""")
with gr.Row():
with gr.Column():
query = gr.Textbox(
label="Search Query",
placeholder="Enter your search query...",
lines=2
)
max_results = gr.Slider(
minimum=3,
maximum=10,
value=5,
step=1,
label="Maximum Results"
)
search_btn = gr.Button("🔍 Search", variant="primary")
with gr.Column():
output = gr.Markdown(
label="Results",
show_label=False
)
search_btn.click(
fn=safe_search,
inputs=[query, max_results],
outputs=output
)
gr.Examples(
examples=[
["What are the latest developments in quantum computing?", 5],
["How does Python's asyncio work? Show code examples", 5],
["Explain the transformer architecture in deep learning", 5],
["What are the environmental impacts of renewable energy?", 5]
],
inputs=[query, max_results],
outputs=output,
fn=safe_search,
cache_examples=True
)
return demo
# Create the demo
demo = create_demo()
# Launch for Spaces
demo.launch()
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