#!/usr/bin/env python """Streamlit app for the Drug Interaction System.""" import os import sys import streamlit as st import matplotlib.pyplot as plt import io import base64 import networkx as nx import uuid # Add the current directory to the Python path sys.path.insert(0, os.path.abspath(os.path.dirname(__file__))) # Import the necessary components from your package from src.models.chatbot import DrugInteractionChatbot # Initialize the chatbot @st.cache_resource def get_chatbot(): """Get or create the chatbot instance with caching.""" return DrugInteractionChatbot() # Set page config st.set_page_config( page_title="Drug Interaction Assistant", page_icon="💊", layout="wide", initial_sidebar_state="expanded" ) # Title and description st.title("Drug Interaction Assistant") st.markdown(""" This application helps you analyze drug interactions, get information about medications, and visualize drug interaction networks. Powered by biomedical language models. """) # Initialize session state for chat history if "messages" not in st.session_state: st.session_state.messages = [] # Sidebar with information with st.sidebar: st.header("About") st.markdown(""" This Drug Interaction Assistant can: - Analyze potential interactions between medications - Provide detailed information about specific drugs - Analyze clinical notes for drug mentions and interactions - Generate visualizations of drug interaction networks """) st.header("Example Questions") st.markdown(""" - "Can I take aspirin and warfarin together?" - "Tell me about metformin" - "Analyze this clinical note: Patient is taking..." - "Show me a visualization for warfarin" """) # Main content area col1, col2 = st.columns([2, 1]) with col1: # Chat interface st.header("Chat with the Assistant") # Display chat history for message in st.session_state.messages: with st.chat_message(message["role"]): st.markdown(message["content"]) # Chat input if prompt := st.chat_input("Ask about drug interactions..."): # Add user message to chat history st.session_state.messages.append({"role": "user", "content": prompt}) # Display user message with st.chat_message("user"): st.markdown(prompt) # Get chatbot response chatbot = get_chatbot() response = chatbot.process_message(prompt) # Check if we need to generate a visualization visualization_needed = False drug_name = None if "interaction found between" in response: # Extract drug name from response import re match = re.search(r'interaction found between (.+?) and', response) if match: drug_name = match.group(1) visualization_needed = True # Add assistant response to chat history st.session_state.messages.append({"role": "assistant", "content": response}) # Display assistant response with st.chat_message("assistant"): st.markdown(response) # Generate and display visualization if needed if visualization_needed and drug_name: try: G, error = chatbot.processor.generate_network(drug_name) if not error: # Create visualization plt.figure(figsize=(10, 8)) # Get positions pos = nx.spring_layout(G, seed=42) # Draw nodes node_sizes = [G.nodes[node].get('size', 10) for node in G.nodes()] node_colors = [G.nodes[node].get('color', 'blue') for node in G.nodes()] nx.draw_networkx_nodes(G, pos, node_size=node_sizes, node_color=node_colors, alpha=0.8) # Draw edges with colors based on severity edge_colors = [] edge_widths = [] for u, v, data in G.edges(data=True): edge_colors.append(data.get('color', 'gray')) edge_widths.append(data.get('weight', 1)) nx.draw_networkx_edges(G, pos, edge_color=edge_colors, width=edge_widths, alpha=0.7) # Add labels nx.draw_networkx_labels(G, pos, font_size=10, font_family="sans-serif") # Save to BytesIO buf = io.BytesIO() plt.axis('off') plt.tight_layout() plt.savefig(buf, format='png', dpi=150) buf.seek(0) plt.close() # Convert to base64 for display img_str = base64.b64encode(buf.read()).decode('utf-8') st.image(f"data:image/png;base64,{img_str}", caption=f"Interaction Network for {drug_name}") except Exception as e: st.error(f"Error generating visualization: {str(e)}") with col2: # Drug information section st.header("Drug Information") # Drug search drug_search = st.text_input("Search for a drug", key="drug_search") if drug_search: chatbot = get_chatbot() drug_info = chatbot.processor.get_drug_information(drug_search) if drug_info: st.subheader(drug_info.get("drug_name", drug_search)) if drug_info.get("drug_class") and drug_info["drug_class"] != "Information not available": st.markdown(f"**Drug Class:** {drug_info['drug_class']}") if drug_info.get("mechanism") and drug_info["mechanism"] != "Information not available": st.markdown(f"**Mechanism of Action:** {drug_info['mechanism']}") if drug_info.get("indications") and drug_info["indications"][0] != "Information not available": st.markdown("**Common Indications:**") for indication in drug_info["indications"]: st.markdown(f"- {indication}") if drug_info.get("side_effects") and drug_info["side_effects"][0] != "Information not available": st.markdown("**Common Side Effects:**") for effect in drug_info["side_effects"]: st.markdown(f"- {effect}") if drug_info.get("common_interactions") and drug_info["common_interactions"][0] != "Information not available": st.markdown("**Common Interactions:**") for interaction in drug_info["common_interactions"]: st.markdown(f"- {interaction}") if drug_info.get("contraindications") and drug_info["contraindications"][0] != "Information not available": st.markdown("**Contraindications:**") for contraindication in drug_info["contraindications"]: st.markdown(f"- {contraindication}") else: st.warning(f"No information found for {drug_search}") # Clinical note analysis section st.header("Clinical Note Analysis") clinical_note = st.text_area("Enter clinical note to analyze", height=150) if clinical_note and st.button("Analyze Note"): chatbot = get_chatbot() results = chatbot.processor.extract_drugs_from_clinical_notes(clinical_note) # Display medications if results["medications"]: st.subheader("Medications Identified") for med in results["medications"]: name = med.get("name", "Unknown") dosage = med.get("dosage", "Not specified") frequency = med.get("frequency", "Not specified") if dosage != "Not specified" or frequency != "Not specified": st.markdown(f"- **{name}**: {dosage} {frequency}") else: st.markdown(f"- **{name}**") else: st.info("No medications were identified in the clinical notes.") # Display potential interactions if results.get("potential_interactions"): st.subheader("Potential Interactions") for interaction in results["potential_interactions"]: drug1 = interaction.get("drug1", "Unknown") drug2 = interaction.get("drug2", "Unknown") concern = interaction.get("concern", "Potential interaction") st.markdown(f"- **{drug1}** + **{drug2}**: {concern}") elif results.get("database_interactions"): st.subheader("Potential Interactions") for interaction in results["database_interactions"]: drug1 = interaction.get("drug1", "Unknown") drug2 = interaction.get("drug2", "Unknown") desc = interaction.get("description", "Potential interaction") severity = interaction.get("severity", "Unknown") st.markdown(f"- **{drug1}** + **{drug2}**: {desc} ({severity})") else: st.info("No potential interactions were identified.") # Footer st.markdown("---") st.markdown("""

Drug Interaction Assistant | Powered by Biomedical Language Models

This information is for educational purposes only. Always consult a healthcare professional for medical advice.

""", unsafe_allow_html=True)