app.py

import gradio as gr
import json
import zipfile
from pathlib import Path
import pandas as pd
from typing import Dict, List, Tuple
import random

class MedQADatabase:
    """Handler for MedQA and Med-Gemini databases"""
    
    def __init__(self, zip_path="medqa_databases.zip"):
        self.data = {
            'medgemini': [],
            'medqa_train': [],
            'medqa_dev': [],
            'medqa_test': []
        }
        self.load_databases(zip_path)
    
    def load_databases(self, zip_path):
        """Load all databases from the ZIP file"""
        print("📦 Loading databases from ZIP...")
        
        try:
            with zipfile.ZipFile(zip_path, 'r') as zip_ref:
                # Extract to temporary directory
                zip_ref.extractall('temp_data')
            
            # Load Med-Gemini
            medgemini_path = Path('temp_data/medqa_databases/med_gemini/medqa_relabelling.json')
            if medgemini_path.exists():
                with open(medgemini_path, 'r', encoding='utf-8') as f:
                    self.data['medgemini'] = json.load(f)
                print(f"✅ Loaded {len(self.data['medgemini'])} Med-Gemini questions")
            
            # Load MedQA splits
            medqa_base = Path('temp_data/medqa_databases/medqa_original')
            for split in ['train', 'dev', 'test']:
                split_path = medqa_base / f"{split}.json"
                if split_path.exists():
                    with open(split_path, 'r', encoding='utf-8') as f:
                        self.data[f'medqa_{split}'] = json.load(f)
                    print(f"✅ Loaded {len(self.data[f'medqa_{split}'])} MedQA {split} questions")
        
        except Exception as e:
            print(f"❌ Error loading databases: {e}")
            raise
    
    def get_stats(self) -> str:
        """Get database statistics"""
        stats = "## 📊 Database Statistics\n\n"
        stats += f"**Med-Gemini**: {len(self.data['medgemini']):,} questions\n\n"
        stats += f"**MedQA Original**:\n"
        stats += f"- Training: {len(self.data['medqa_train']):,} questions\n"
        stats += f"- Development: {len(self.data['medqa_dev']):,} questions\n"
        stats += f"- Test: {len(self.data['medqa_test']):,} questions\n"
        stats += f"- **Total**: {sum(len(self.data[f'medqa_{s}']) for s in ['train', 'dev', 'test']):,} questions\n\n"
        stats += f"**Grand Total**: {sum(len(v) for v in self.data.values()):,} questions"
        return stats
    
    def get_question(self, dataset: str, index: int) -> Dict:
        """Get a specific question from a dataset"""
        try:
            return self.data[dataset][index]
        except (KeyError, IndexError):
            return None
    
    def search_questions(self, query: str, dataset: str = 'all', max_results: int = 50) -> List[Tuple[str, int, str]]:
        """Search questions by keyword"""
        results = []
        query_lower = query.lower()
        
        datasets_to_search = list(self.data.keys()) if dataset == 'all' else [dataset]
        
        for ds in datasets_to_search:
            for idx, q in enumerate(self.data[ds]):
                # Search in question text
                question_text = q.get('question', q.get('Question', ''))
                if query_lower in question_text.lower():
                    preview = question_text[:100] + "..." if len(question_text) > 100 else question_text
                    results.append((ds, idx, preview))
                    
                    if len(results) >= max_results:
                        return results
        
        return results

# Initialize database
print("🚀 Initializing MedQA Explorer...")
db = MedQADatabase()

# ============================================================================
# GRADIO INTERFACE FUNCTIONS
# ============================================================================

def format_question_display(question_data: Dict, dataset: str) -> str:
    """Format question data for display"""
    
    if not question_data:
        return "❌ Question not found"
    
    # Handle different data formats
    if dataset == 'medgemini':
        return format_medgemini_question(question_data)
    else:
        return format_medqa_question(question_data)

def format_medgemini_question(q: Dict) -> str:
    """Format Med-Gemini question"""
    html = f"""
    <div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); padding: 20px; border-radius: 10px; margin-bottom: 20px;">
        <h2 style="color: white; margin: 0;">🔬 Med-Gemini Question</h2>
    </div>
    
    <div style="background: #f8f9fa; padding: 20px; border-radius: 8px; margin-bottom: 20px;">
        <h3>📋 Question</h3>
        <p style="font-size: 16px; line-height: 1.6;">{q.get('question', 'N/A')}</p>
    </div>
    
    <div style="background: #fff; padding: 20px; border-radius: 8px; margin-bottom: 20px; border: 2px solid #e0e0e0;">
        <h3>🔤 Answer Options</h3>
    """
    
    # Display options
    options = q.get('options', {})
    correct_answer = q.get('answer_idx', 'N/A')
    
    option_labels = ['A', 'B', 'C', 'D', 'E']
    for label in option_labels:
        option_key = f'opa' if label == 'A' else f'op{label.lower()}'
        if option_key in options:
            is_correct = (label == correct_answer)
            color = '#d4edda' if is_correct else '#fff'
            icon = '✅' if is_correct else '⭕'
            
            html += f"""
            <div style="background: {color}; padding: 12px; margin: 8px 0; border-radius: 5px; border: 1px solid #ccc;">
                {icon} <strong>{label}.</strong> {options[option_key]}
            </div>
            """
    
    html += "</div>"
    
    # Show correct answer
    html += f"""
    <div style="background: #d4edda; padding: 15px; border-radius: 8px; margin-bottom: 20px; border-left: 4px solid #28a745;">
        <h3 style="margin-top: 0;">✅ Correct Answer</h3>
        <p style="font-size: 18px; font-weight: bold; margin: 0;">{correct_answer}</p>
    </div>
    """
    
    # Show explanation if available
    explanation = q.get('explanation', q.get('Explanation', ''))
    if explanation:
        html += f"""
        <div style="background: #e7f3ff; padding: 20px; border-radius: 8px; border-left: 4px solid #2196F3;">
            <h3 style="margin-top: 0;">💡 Explanation</h3>
            <p style="line-height: 1.6;">{explanation}</p>
        </div>
        """
    
    return html

def format_medqa_question(q: Dict) -> str:
    """Format MedQA original question"""
    html = f"""
    <div style="background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%); padding: 20px; border-radius: 10px; margin-bottom: 20px;">
        <h2 style="color: white; margin: 0;">📚 MedQA USMLE Question</h2>
    </div>
    
    <div style="background: #f8f9fa; padding: 20px; border-radius: 8px; margin-bottom: 20px;">
        <h3>📋 Question</h3>
        <p style="font-size: 16px; line-height: 1.6;">{q.get('question', 'N/A')}</p>
    </div>
    
    <div style="background: #fff; padding: 20px; border-radius: 8px; margin-bottom: 20px; border: 2px solid #e0e0e0;">
        <h3>🔤 Answer Options</h3>
    """
    
    # Display options
    options = q.get('options', {})
    correct_answer = q.get('answer_idx', 'N/A')
    
    for key, value in options.items():
        label = key.replace('op', '').upper() if key.startswith('op') else key
        is_correct = (label == correct_answer)
        color = '#d4edda' if is_correct else '#fff'
        icon = '✅' if is_correct else '⭕'
        
        html += f"""
        <div style="background: {color}; padding: 12px; margin: 8px 0; border-radius: 5px; border: 1px solid #ccc;">
            {icon} <strong>{label}.</strong> {value}
        </div>
        """
    
    html += "</div>"
    
    # Show correct answer
    html += f"""
    <div style="background: #d4edda; padding: 15px; border-radius: 8px; margin-bottom: 20px; border-left: 4px solid #28a745;">
        <h3 style="margin-top: 0;">✅ Correct Answer</h3>
        <p style="font-size: 18px; font-weight: bold; margin: 0;">{correct_answer}</p>
    </div>
    """
    
    # Show metamap if available
    metamap = q.get('metamap_phrases')
    if metamap:
        html += f"""
        <div style="background: #fff3cd; padding: 15px; border-radius: 8px; border-left: 4px solid #ffc107;">
            <h3 style="margin-top: 0;">🏥 Medical Concepts (MetaMap)</h3>
            <p style="line-height: 1.6;">{', '.join(metamap)}</p>
        </div>
        """
    
    return html

def browse_questions(dataset: str, index: int) -> Tuple[str, str]:
    """Browse questions by index"""
    total = len(db.data.get(dataset, []))
    
    if total == 0:
        return "❌ No questions in this dataset", f"Dataset: {dataset} (empty)"
    
    # Clamp index to valid range
    index = max(0, min(index, total - 1))
    
    question = db.get_question(dataset, index)
    html = format_question_display(question, dataset)
    info = f"📊 Question {index + 1} of {total} | Dataset: {dataset}"
    
    return html, info

def random_question(dataset: str) -> Tuple[str, str, int]:
    """Get a random question"""
    total = len(db.data.get(dataset, []))
    
    if total == 0:
        return "❌ No questions in this dataset", f"Dataset: {dataset} (empty)", 0
    
    index = random.randint(0, total - 1)
    question = db.get_question(dataset, index)
    html = format_question_display(question, dataset)
    info = f"🎲 Random Question {index + 1} of {total} | Dataset: {dataset}"
    
    return html, info, index

def search_interface(query: str, dataset: str) -> str:
    """Search interface"""
    if not query.strip():
        return "💡 Enter a search query to find questions"
    
    results = db.search_questions(query, dataset)
    
    if not results:
        return f"❌ No results found for '{query}' in {dataset}"
    
    html = f"""
    <div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); padding: 20px; border-radius: 10px; margin-bottom: 20px;">
        <h2 style="color: white; margin: 0;">🔍 Search Results: "{query}"</h2>
        <p style="color: white; margin: 5px 0 0 0;">Found {len(results)} results in {dataset}</p>
    </div>
    """
    
    for ds, idx, preview in results[:20]:  # Show top 20
        dataset_name = ds.replace('_', ' ').title()
        html += f"""
        <div style="background: #fff; padding: 15px; margin: 10px 0; border-radius: 8px; border-left: 4px solid #667eea;">
            <p style="margin: 0; color: #666; font-size: 12px;"><strong>{dataset_name}</strong> - Question #{idx + 1}</p>
            <p style="margin: 5px 0 0 0;">{preview}</p>
        </div>
        """
    
    if len(results) > 20:
        html += f"<p>... and {len(results) - 20} more results</p>"
    
    return html

# ============================================================================
# GRADIO APP
# ============================================================================

with gr.Blocks(theme=gr.themes.Soft(), title="MedQA Database Explorer") as app:
    
    gr.Markdown("""
    # 🏥 MedQA Database Explorer
    
    Explore medical question-answering databases including **Med-Gemini** and **MedQA USMLE**.
    """)
    
    # Statistics
    with gr.Accordion("📊 Database Statistics", open=False):
        gr.Markdown(db.get_stats())
    
    # Main interface
    with gr.Tabs():
        
        # Browse Tab
        with gr.Tab("📖 Browse Questions"):
            with gr.Row():
                with gr.Column(scale=1):
                    dataset_dropdown = gr.Dropdown(
                        choices=['medgemini', 'medqa_train', 'medqa_dev', 'medqa_test'],
                        value='medgemini',
                        label="Select Database"
                    )
                    
                    question_slider = gr.Slider(
                        minimum=0,
                        maximum=len(db.data['medgemini']) - 1,
                        value=0,
                        step=1,
                        label="Question Number"
                    )
                    
                    with gr.Row():
                        prev_btn = gr.Button("⬅️ Previous", size="sm")
                        random_btn = gr.Button("🎲 Random", size="sm", variant="primary")
                        next_btn = gr.Button("Next ➡️", size="sm")
                    
                    info_text = gr.Textbox(label="Info", interactive=False)
                
                with gr.Column(scale=2):
                    question_display = gr.HTML()
            
            # Update slider max when dataset changes
            def update_slider(dataset):
                max_val = len(db.data.get(dataset, [])) - 1
                return gr.Slider(maximum=max_val, value=0)
            
            dataset_dropdown.change(
                fn=update_slider,
                inputs=[dataset_dropdown],
                outputs=[question_slider]
            )
            
            # Browse functions
            def show_question(dataset, index):
                return browse_questions(dataset, int(index))
            
            question_slider.change(
                fn=show_question,
                inputs=[dataset_dropdown, question_slider],
                outputs=[question_display, info_text]
            )
            
            dataset_dropdown.change(
                fn=show_question,
                inputs=[dataset_dropdown, question_slider],
                outputs=[question_display, info_text]
            )
            
            # Navigation buttons
            def prev_question(dataset, index):
                new_index = max(0, int(index) - 1)
                html, info = browse_questions(dataset, new_index)
                return html, info, new_index
            
            def next_question(dataset, index):
                max_idx = len(db.data.get(dataset, [])) - 1
                new_index = min(max_idx, int(index) + 1)
                html, info = browse_questions(dataset, new_index)
                return html, info, new_index
            
            prev_btn.click(
                fn=prev_question,
                inputs=[dataset_dropdown, question_slider],
                outputs=[question_display, info_text, question_slider]
            )
            
            next_btn.click(
                fn=next_question,
                inputs=[dataset_dropdown, question_slider],
                outputs=[question_display, info_text, question_slider]
            )
            
            random_btn.click(
                fn=random_question,
                inputs=[dataset_dropdown],
                outputs=[question_display, info_text, question_slider]
            )
            
            # Load first question on start
            app.load(
                fn=show_question,
                inputs=[dataset_dropdown, question_slider],
                outputs=[question_display, info_text]
            )
        
        # Search Tab
        with gr.Tab("🔍 Search"):
            with gr.Row():
                search_query = gr.Textbox(
                    label="Search Query",
                    placeholder="Enter keywords (e.g., 'diabetes', 'heart failure', 'treatment')...",
                    scale=3
                )
                search_dataset = gr.Dropdown(
                    choices=['all', 'medgemini', 'medqa_train', 'medqa_dev', 'medqa_test'],
                    value='all',
                    label="Search In",
                    scale=1
                )
            
            search_btn = gr.Button("🔍 Search", variant="primary")
            search_results = gr.HTML()
            
            search_btn.click(
                fn=search_interface,
                inputs=[search_query, search_dataset],
                outputs=[search_results]
            )
            
            # Also search on Enter key
            search_query.submit(
                fn=search_interface,
                inputs=[search_query, search_dataset],
                outputs=[search_results]
            )
    
    gr.Markdown("""
    ---
    ### 📚 About the Databases
    
    **Med-Gemini**: Expert-relabeled medical questions with detailed explanations from Google's Med-Gemini project.
    
    **MedQA**: Original USMLE-style medical questions from the MedQA dataset.
    
    ### 🔗 Sources
    - [Med-Gemini Paper](https://arxiv.org/abs/2404.18416)
    - [MedQA Dataset](https://github.com/jind11/MedQA)
    """)

if __name__ == "__main__":
    app.launch()