docs/scratchpad.md

#!/usr/bin/env python3 """ VedaMD Enhanced: Sri Lankan Clinical Assistant Main Gradio Application for Hugging Face Spaces Deployment

Enhanced Medical-Grade RAG System with: ✅ 5x Enhanced Retrieval (15+ documents vs previous 5) ✅ Medical Entity Extraction & Clinical Terminology
✅ Clinical ModernBERT (768d medical embeddings) ✅ Medical Response Verification & Safety Protocols ✅ Advanced Re-ranking & Coverage Verification ✅ Source Traceability & Citation Support """

import os import logging import gradio as gr from typing import List, Dict, Optional import sys

Add src directory to path for imports

sys.path.append(os.path.join(os.path.dirname(file), 'src'))

from src.enhanced_groq_medical_rag import EnhancedGroqMedicalRAG, EnhancedMedicalResponse

Configure logging

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

Initialize Enhanced Medical RAG System

logger.info("🏥 Initializing VedaMD Enhanced for Hugging Face Spaces...") try: enhanced_rag_system = EnhancedGroqMedicalRAG() logger.info("✅ Enhanced Medical RAG system ready!") except Exception as e: logger.error(f"❌ Failed to initialize system: {e}") raise

def process_enhanced_medical_query(message: str, history: List[List[str]]) -> str: """ Process medical query with enhanced RAG system """ try: if not message.strip(): return "Please enter a medical question about Sri Lankan clinical guidelines."

    # Convert Gradio chat history to our format
    formatted_history = []
    if history:
        for chat_pair in history:
            if len(chat_pair) >= 2:
                user_msg, assistant_msg = chat_pair[0], chat_pair[1]
                if user_msg:
                    formatted_history.append({"role": "user", "content": user_msg})
                if assistant_msg:
                    formatted_history.append({"role": "assistant", "content": assistant_msg})
    
    # Get enhanced response
    response: EnhancedMedicalResponse = enhanced_rag_system.query(
        query=message, 
        history=formatted_history
    )
    
    # Format enhanced response for display
    formatted_response = format_enhanced_medical_response(response)
    return formatted_response
    
except Exception as e:
    logger.error(f"Error processing query: {e}")
    return f"⚠️ **System Error**: {str(e)}\n\nPlease try again or contact support if the issue persists."

def format_enhanced_medical_response(response: EnhancedMedicalResponse) -> str: """ Format the enhanced medical response for display, ensuring citations are always included. """ formatted_parts = []

# Main response from the LLM
final_response_text = response.answer.strip()
formatted_parts.append(final_response_text)

# ALWAYS add the clinical sources section with clear numbering
if response.sources:
    formatted_parts.append("\n\n---\n")
    formatted_parts.append("### 📋 **Clinical Sources & Citations**")
    formatted_parts.append("\nThis response is based on the following Sri Lankan clinical guidelines:")
    # Create a numbered list of all sources used for the response
    for i, source in enumerate(response.sources, 1):
        # Make the citation number bold and add a clear label
        formatted_parts.append(f"\n**[{i}]** Source: {source}")

# Enhanced information section with clear separation
formatted_parts.append("\n\n---\n")
formatted_parts.append("### 📊 **Response Analysis**")

# Safety and verification info with clearer formatting
if response.verification_result:
    safety_status = "✅ SAFE" if response.safety_status == "SAFE" else "⚠️ CAUTION"
    formatted_parts.append(f"\n**Medical Safety Status**: {safety_status}")
    formatted_parts.append(f"**Verification Score**: {response.verification_result.verification_score:.1%}")
    formatted_parts.append(f"**Verified Medical Claims**: {response.verification_result.verified_claims}/{response.verification_result.total_claims}")

# Enhanced retrieval metrics
formatted_parts.append(f"\n**Medical Information Coverage**:")
formatted_parts.append(f"- 🧠 Medical Entities: {response.medical_entities_count}")
formatted_parts.append(f"- 🎯 Context Adherence: {response.context_adherence_score:.1%}")
formatted_parts.append(f"- 📚 Guidelines Referenced: {len(response.sources)}")

# Always include processing time if available
if hasattr(response, 'query_time'):
    formatted_parts.append(f"- ⚡ Processing Time: {response.query_time:.2f}s")

# Medical disclaimer with clear separation
formatted_parts.append("\n\n---\n")
formatted_parts.append("*⚕️ This information is derived from Sri Lankan clinical guidelines and is for reference only. Always consult with qualified healthcare professionals for patient care decisions.*")

return "\n".join(formatted_parts)

def create_enhanced_medical_interface(): """ Create the enhanced Gradio interface for Hugging Face Spaces """ # Custom CSS for medical theme custom_css = """ .gradio-container { max-width: 900px !important; margin: auto !important; } .medical-header { background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 20px; border-radius: 10px; margin-bottom: 20px; text-align: center; } """

with gr.Blocks(
    title="🏥 VedaMD Enhanced: Sri Lankan Clinical Assistant",
    theme=gr.themes.Soft(),
    css=custom_css
) as demo:
    
    # Header
    gr.HTML("""
    <div class="medical-header">
        <h1>🏥 VedaMD Enhanced: Sri Lankan Clinical Assistant</h1>
        <h3>Enhanced Medical-Grade AI with Advanced RAG & Safety Protocols</h3>
        <p>✅ 5x Enhanced Retrieval • ✅ Medical Verification • ✅ Clinical ModernBERT • ✅ Source Traceability</p>
    </div>
    """)
    
    # Description
    gr.Markdown("""
    **🩺 Advanced Medical AI Assistant** for Sri Lankan maternal health guidelines with **enhanced safety protocols**:

    🎯 **Enhanced Features:**
    - **5x Enhanced Retrieval**: 15+ documents analyzed vs previous 5
    - **Medical Entity Extraction**: Advanced clinical terminology recognition  
    - **Clinical ModernBERT**: Specialized 768d medical domain embeddings
    - **Medical Response Verification**: 100% source traceability validation
    - **Advanced Re-ranking**: Medical relevance scoring with coverage verification
    - **Safety Protocols**: Comprehensive medical claim verification before delivery

    **Ask me anything about Sri Lankan clinical guidelines with confidence!** 🇱🇰
    """)
    
    # Chat interface
    chatbot = gr.ChatInterface(
        fn=process_enhanced_medical_query,
        examples=[
            "What is the complete management protocol for severe preeclampsia in Sri Lankan guidelines?",
            "How should postpartum hemorrhage be managed according to our local clinical protocols?",
            "What medications are contraindicated during pregnancy based on Sri Lankan guidelines?",
            "What are the evidence-based recommendations for managing gestational diabetes?",
            "How should puerperal sepsis be diagnosed and treated according to our guidelines?",
            "What are the protocols for assisted vaginal delivery in complicated cases?",
            "How should intrapartum fever be managed based on Sri Lankan standards?"
        ],
        cache_examples=False
    )
    
    # Footer with technical info
    gr.Markdown("""
    ---
    **🔧 Technical Details**: Enhanced RAG with Clinical ModernBERT embeddings, medical entity extraction, 
    response verification, and multi-stage retrieval for comprehensive medical information coverage.
    
    **⚖️ Disclaimer**: This AI assistant is for clinical reference only and does not replace professional medical judgment.
    Always consult with qualified healthcare professionals for patient care decisions.
    """)

return demo

Create and launch the interface

if name == "main": logger.info("🚀 Launching VedaMD Enhanced for Hugging Face Spaces...")

# Create the interface
demo = create_enhanced_medical_interface()

# Launch with appropriate settings for HF Spaces
demo.launch(
    server_name="0.0.0.0",
    server_port=7860,
    share=False,
    show_error=True,
    show_api=False
)