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| import streamlit as st | |
| import logging | |
| from services.research_fetcher import ResearchFetcher | |
| from services.model_handler import ModelHandler | |
| from utils.text_processor import TextProcessor | |
| from typing import List | |
| # Configure logging | |
| logging.basicConfig( | |
| level=logging.INFO, | |
| format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' | |
| ) | |
| class AutismResearchApp: | |
| def __init__(self): | |
| """Initialize the application components""" | |
| self.research_fetcher = ResearchFetcher() | |
| self.model_handler = ModelHandler() | |
| self.text_processor = TextProcessor() | |
| def _setup_streamlit(self): | |
| """Setup Streamlit UI components""" | |
| st.title("🧩 AMA Autism") | |
| st.subheader("Your one-stop shop for autism research!") | |
| st.markdown(""" | |
| Ask questions about autism research, and I'll analyze recent papers to provide evidence-based answers. | |
| """) | |
| def _fetch_research(self, query: str): | |
| """Fetch research papers for the given query""" | |
| papers = self.research_fetcher.fetch_all_papers(query) | |
| if not papers: | |
| st.warning("No relevant research papers found. Please try a different search term.") | |
| return None | |
| return papers | |
| def _display_sources(self, papers: List): | |
| """Display the source papers used to generate the answer""" | |
| st.markdown("### Sources") | |
| for i, paper in enumerate(papers, 1): | |
| st.markdown(f"**{i}. [{paper.title}]({paper.url})**") | |
| # Create three columns for metadata | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| if paper.authors: | |
| st.markdown(f"👥 Authors: {paper.authors}") | |
| with col2: | |
| st.markdown(f"📅 Published: {paper.publication_date}") | |
| with col3: | |
| st.markdown(f"📜 Source: {paper.source}") | |
| # Show abstract in expander | |
| with st.expander("📝 View Abstract"): | |
| st.markdown(paper.abstract) | |
| if i < len(papers): # Add separator between papers except for the last one | |
| st.divider() | |
| def run(self): | |
| """Run the main application loop""" | |
| self._setup_streamlit() | |
| # Initialize session state for papers | |
| if 'papers' not in st.session_state: | |
| st.session_state.papers = [] | |
| # Get user query | |
| query = st.text_input("What would you like to know about autism?") | |
| if query: | |
| # Show status while processing | |
| with st.status("Processing your question...") as status: | |
| # Fetch papers | |
| status.write("🔍 Searching for relevant research papers...") | |
| try: | |
| papers = self.research_fetcher.fetch_all_papers(query) | |
| except Exception as e: | |
| st.error(f"Error fetching research papers: {str(e)}") | |
| return | |
| if not papers: | |
| st.warning("No relevant papers found. Please try a different query.") | |
| return | |
| # Generate and validate answer | |
| status.write("📚 Analyzing research papers...") | |
| context = self.text_processor.create_context(papers) | |
| status.write("✍️ Generating answer...") | |
| answer = self.model_handler.generate_answer(query, context) | |
| status.write("✅ Validating answer...") | |
| is_valid, validation_message = self.model_handler.validate_answer(answer, context) | |
| status.write("✨ All done! Displaying results...") | |
| # Display results | |
| if is_valid: | |
| st.success("✅ Research analysis complete! The answer has been validated for accuracy.") | |
| else: | |
| st.warning("⚠️ The answer may contain information not fully supported by the research.") | |
| st.markdown("### Answer") | |
| st.markdown(answer) | |
| st.markdown("### Validation") | |
| st.info(f"🔍 {validation_message}") | |
| st.divider() | |
| self._display_sources(papers) | |
| def main(): | |
| app = AutismResearchApp() | |
| app.run() | |
| if __name__ == "__main__": | |
| main() |