new database added- med-gemini

app.py

@@ -1,436 +1,436 @@
-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__":
+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()

medqa_db.zip

@@ -0,0 +1,3 @@
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+oid sha256:f1c9706f423f219a1bde8929bdcb7d05135e6f998c42e8af2c5b19d830bdf8cc
+size 19150705