init

app.py

@@ -1,7 +1,176 @@
 import gradio as gr
+import pandas as pd
+import numpy as np
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Sample data structure - replace this with your actual CSV loading
+# df = pd.read_csv('your_leaderboard_data.csv')
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+# For demonstration, I'll create sample data matching your structure
+data = {
+    'Model': ['Llama-3-70B-UltraMedical', 'MMed-Llama-3-8B', 'Llama-3.1-8B-UltraMedical', 'meditron-70b', 'meditron-7b'],
+    'Domain': ['Medical', 'Medical', 'Medical', 'Medical', 'Medical'],
+    'License': ['Llama-3', 'Llama-3', 'Llama-3', 'Apache 2.0', 'Apache 2.0'],
+    'Size (B)': [70, 8, 8, 70, 7],
+    'Size_Category': ['40-80', '5-10', '5-10', '40-80', '5-10'],  # Added for filtering
+    'Accessibility': ['Open Source', 'Open Source', 'Open Source', 'Open Source', 'Open Source'],
+    'Average Performance': [33.4, 20.37, 20.16, 15.68, 9.52],
+    'ADE-Identification': [77.96, 83.15, 64.43, 51.8, 39.01],
+    'BrainMRI-AIS': [95.91, 91.82, 92.48, 87.27, 64.47],
+    'Brateca-Hospitalization': [59.23, 55.1, 35.42, 43.18, 40.1]
+}
+
+df = pd.DataFrame(data)
+
+def filter_and_search_models(search_query, domain_filter, size_ranges, accessibility_filter):
+    """Filter and search models based on user inputs"""
+    filtered_df = df.copy()
+    
+    # Apply search filter
+    if search_query:
+        mask = filtered_df['Model'].str.contains(search_query, case=False, na=False)
+        filtered_df = filtered_df[mask]
+    
+    # Apply domain filter
+    if domain_filter:
+        if domain_filter == "Medical":
+            filtered_df = filtered_df[filtered_df['Domain'] == 'Medical']
+        elif domain_filter == "General":
+            filtered_df = filtered_df[filtered_df['Domain'] == 'General']
+    
+    # Apply size range filter
+    if size_ranges and len(size_ranges) > 0:
+        filtered_df = filtered_df[filtered_df['Size_Category'].isin(size_ranges)]
+    
+    # Apply accessibility filter
+    if accessibility_filter:
+        if accessibility_filter == "Open Source":
+            filtered_df = filtered_df[filtered_df['Accessibility'] == 'Open Source']
+        elif accessibility_filter == "Proprietary":
+            filtered_df = filtered_df[filtered_df['Accessibility'] == 'Proprietary']
+    
+    # Sort by Average Performance (descending)
+    filtered_df = filtered_df.sort_values('Average Performance', ascending=False)
+    
+    # Format the dataframe for display
+    display_df = filtered_df[['Model', 'Domain', 'License', 'Size (B)', 
+                              'Average Performance', 'ADE-Identification', 
+                              'BrainMRI-AIS', 'Brateca-Hospitalization']]
+    
+    # Round numerical values for better display
+    for col in ['Average Performance', 'ADE-Identification', 'BrainMRI-AIS', 'Brateca-Hospitalization']:
+        display_df[col] = display_df[col].round(2)
+    
+    return display_df
+
+# Create the Gradio interface
+with gr.Blocks(title="FACT Leaderboard", theme=gr.themes.Base()) as app:
+    gr.Markdown("# 🏆 FACT Leaderboard")
+    gr.Markdown("### Filter and search medical AI models by performance metrics")
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            # Search box
+            search_box = gr.Textbox(
+                label="Model Search",
+                placeholder="Search for a model...",
+                value=""
+            )
+            
+            # Domain filter
+            gr.Markdown("**Filter Model: Domain**")
+            domain_radio = gr.Radio(
+                choices=["All", "General", "Medical"],
+                value="All",
+                label="",
+                elem_classes="domain-filter"
+            )
+            
+            # Size range filter
+            gr.Markdown("**Filter Model: Size Range**")
+            size_checkboxes = gr.CheckboxGroup(
+                choices=["0-5", "5-10", "10-40", "40-80", ">80"],
+                value=["0-5", "5-10", "10-40", "40-80", ">80"],
+                label="",
+                elem_classes="size-filter"
+            )
+            
+            # Accessibility filter
+            gr.Markdown("**Filter Model: Accessibility**")
+            accessibility_radio = gr.Radio(
+                choices=["All", "Open Source", "Proprietary"],
+                value="All",
+                label="",
+                elem_classes="accessibility-filter"
+            )
+        
+        with gr.Column(scale=3):
+            # Results table
+            results_table = gr.Dataframe(
+                value=filter_and_search_models("", "All", ["0-5", "5-10", "10-40", "40-80", ">80"], "All"),
+                headers=["Model", "Model: Domain", "Model: License", "Size (B)", 
+                        "Average Performance", "ADE-Identification", 
+                        "BrainMRI-AIS", "Brateca-Hospitalization"],
+                datatype=["str", "str", "str", "number", "number", "number", "number", "number"],
+                elem_id="leaderboard-table",
+                interactive=False,
+                wrap=True
+            )
+    
+    # Update table when filters change
+    def update_table(search, domain, sizes, accessibility):
+        domain_val = None if domain == "All" else domain
+        accessibility_val = None if accessibility == "All" else accessibility
+        return filter_and_search_models(search, domain_val, sizes, accessibility_val)
+    
+    # Connect all inputs to the update function
+    search_box.change(
+        fn=update_table,
+        inputs=[search_box, domain_radio, size_checkboxes, accessibility_radio],
+        outputs=results_table
+    )
+    
+    domain_radio.change(
+        fn=update_table,
+        inputs=[search_box, domain_radio, size_checkboxes, accessibility_radio],
+        outputs=results_table
+    )
+    
+    size_checkboxes.change(
+        fn=update_table,
+        inputs=[search_box, domain_radio, size_checkboxes, accessibility_radio],
+        outputs=results_table
+    )
+    
+    accessibility_radio.change(
+        fn=update_table,
+        inputs=[search_box, domain_radio, size_checkboxes, accessibility_radio],
+        outputs=results_table
+    )
+    
+    # Add custom CSS for better styling
+    app.css = """
+    #leaderboard-table {
+        font-size: 14px;
+    }
+    
+    .domain-filter label,
+    .size-filter label,
+    .accessibility-filter label {
+        display: flex;
+        align-items: center;
+        margin: 5px 0;
+    }
+    
+    .domain-filter input[type="radio"],
+    .accessibility-filter input[type="radio"] {
+        margin-right: 8px;
+    }
+    
+    .size-filter input[type="checkbox"] {
+        margin-right: 8px;
+    }
+    """
+
+# Launch the app
+if __name__ == "__main__":
+    app.launch(share=True)