Create app.py

app.py

@@ -0,0 +1,136 @@
+import gradio as gr
+import pandas as pd
+
+# Define the columns for the UGI Leaderboard
+UGI_COLS = [
+    '#P', 'Model', 'UGI 🏆', 'Willingness👍', 'QuActivities', 'Internet', 'CrimeStats', 'Stories/Jokes', 'Pol Contro'
+]
+
+# Load the leaderboard data from a CSV file
+def load_leaderboard_data(csv_file_path):
+    try:
+        df = pd.read_csv(csv_file_path)
+        # Create hyperlinks in the Model column using HTML <a> tags with inline CSS for styling
+        df['Model'] = df.apply(lambda row: f'<a href="{row["Link"]}" target="_blank" style="color: blue; text-decoration: none;">{row["Model"]}</a>' if pd.notna(row["Link"]) else row["Model"], axis=1)
+        # Drop the 'Link' column as it's no longer needed
+        df.drop(columns=['Link'], inplace=True)
+        return df
+    except Exception as e:
+        print(f"Error loading CSV file: {e}")
+        return pd.DataFrame(columns=UGI_COLS)  # Return an empty dataframe with the correct columns
+
+# Update the leaderboard table based on the search query and parameter range filters
+def update_table(df: pd.DataFrame, query: str, param_ranges: dict) -> pd.DataFrame:
+    filtered_df = df
+    if any(param_ranges.values()):
+        conditions = []
+        for param_range, checked in param_ranges.items():
+            if checked:
+                if param_range == '~1.5':
+                    conditions.append((filtered_df['Params'] < 2.5))
+                elif param_range == '~3':
+                    conditions.append(((filtered_df['Params'] >= 2.5) & (filtered_df['Params'] < 6)))
+                elif param_range == '~7':
+                    conditions.append(((filtered_df['Params'] >= 6) & (filtered_df['Params'] < 9.5)))
+                elif param_range == '~13':
+                    conditions.append(((filtered_df['Params'] >= 9.5) & (filtered_df['Params'] < 16)))
+                elif param_range == '~20':
+                    conditions.append(((filtered_df['Params'] >= 16) & (filtered_df['Params'] < 28)))
+                elif param_range == '~34':
+                    conditions.append(((filtered_df['Params'] >= 28) & (filtered_df['Params'] < 40)))
+                elif param_range == '~50':
+                    conditions.append(((filtered_df['Params'] >= 40) & (filtered_df['Params'] < 60)))
+                elif param_range == '~70+':
+                    conditions.append((filtered_df['Params'] >= 60))
+        
+        if all(param_ranges.values()):
+            conditions.append(filtered_df['Params'].isna())
+        
+        filtered_df = filtered_df[pd.concat(conditions, axis=1).any(axis=1)]
+    
+    if query:
+        filtered_df = filtered_df[filtered_df.apply(lambda row: query.lower() in row.to_string().lower(), axis=1)]
+    
+    return filtered_df[UGI_COLS]  # Return only the columns defined in UGI_COLS
+
+# Define the Gradio interface
+demo = gr.Blocks()
+
+with demo:
+    gr.Markdown("## UGI Leaderboard", elem_classes="text-lg")
+    with gr.Column():
+        with gr.Row():
+            search_bar = gr.Textbox(placeholder=" 🔍 Search for a model...", show_label=False)
+        with gr.Row():
+            gr.Markdown("Model sizes (in billions of parameters)", elem_classes="text-sm")
+            param_range_1 = gr.Checkbox(label="~1.5", value=False)
+            param_range_2 = gr.Checkbox(label="~3", value=False)
+            param_range_3 = gr.Checkbox(label="~7", value=False)
+            param_range_4 = gr.Checkbox(label="~13", value=False)
+            param_range_5 = gr.Checkbox(label="~20", value=False)
+            param_range_6 = gr.Checkbox(label="~34", value=False)
+            param_range_7 = gr.Checkbox(label="~50", value=False)
+            param_range_8 = gr.Checkbox(label="~70+", value=False)
+    
+    # Load the initial leaderboard data
+    leaderboard_df = load_leaderboard_data("ugi-leaderboard-data.csv")
+    
+    # Define the datatypes for each column, setting 'Model' column to 'html'
+    datatypes = ['html' if col == 'Model' else 'str' for col in UGI_COLS]
+    
+    leaderboard_table = gr.Dataframe(
+        value=leaderboard_df[UGI_COLS],
+        datatype=datatypes,  # Specify the datatype for each column
+        interactive=False,  # Set to False to make the leaderboard non-editable
+        visible=True,
+        elem_classes="text-sm"  # Increase the font size of the leaderboard data
+    )
+
+    # Define the search and filter functionality
+    inputs = [
+        search_bar,
+        param_range_1,
+        param_range_2,
+        param_range_3,
+        param_range_4,
+        param_range_5,
+        param_range_6,
+        param_range_7,
+        param_range_8
+    ]
+    
+    outputs = leaderboard_table
+    
+    search_bar.change(
+        fn=lambda query, r1, r2, r3, r4, r5, r6, r7, r8: update_table(leaderboard_df, query, {
+            '~1.5': r1,
+            '~3': r2,
+            '~7': r3,
+            '~13': r4,
+            '~20': r5,
+            '~34': r6,
+            '~50': r7,
+            '~70+': r8
+        }),
+        inputs=inputs,
+        outputs=outputs
+    )
+    
+    for param_range in inputs[1:]:
+        param_range.change(
+            fn=lambda query, r1, r2, r3, r4, r5, r6, r7, r8: update_table(leaderboard_df, query, {
+                '~1.5': r1,
+                '~3': r2,
+                '~7': r3,
+                '~13': r4,
+                '~20': r5,
+                '~34': r6,
+                '~50': r7,
+                '~70+': r8
+            }),
+            inputs=inputs,
+            outputs=outputs
+        )
+
+# Launch the Gradio app
+demo.launch()