Initial leaderboard setup

.ipynb_checkpoints/app-checkpoint.py

@@ -198,31 +198,39 @@ with tab2:
     )
     st.plotly_chart(fig, use_container_width=True)
     
-    # Create a single legend figure
-    legend_fig = go.Figure()
-    for model in selected_models:
-        legend_fig.add_trace(go.Scatter(
-            x=[None],
-            y=[None],
-            mode='markers',
-            name=model,
-            showlegend=True
-        ))
-    legend_fig.update_layout(
-        showlegend=True,
-        legend=dict(
-            orientation="h",
-            yanchor="middle",
-            y=1,
-            xanchor="center",
-            x=0.5,
-            font=dict(size=12)
-        ),
-        margin=dict(l=0, r=0, t=0, b=0),
-        height=50
-    )
-    st.plotly_chart(legend_fig, use_container_width=True)
-
+    # Create a shared legend container
+    legend_data = []
+    for i, model in enumerate(selected_models):
+        legend_data.append({
+            'Model': model,
+            'Visible': True,
+            'Index': i
+        })
+    legend_df = pd.DataFrame(legend_data)
+    
+    # Create toggles for models using st.columns
+    st.markdown("### Select Models to Display:")
+    
+    # Calculate how many columns we need (aim for about 4-5 models per row)
+    models_per_row = 4
+    num_rows = (len(selected_models) + models_per_row - 1) // models_per_row
+    
+    for row in range(num_rows):
+        cols = st.columns(models_per_row)
+        for col_idx in range(models_per_row):
+            model_idx = row * models_per_row + col_idx
+            if model_idx < len(selected_models):
+                model = selected_models[model_idx]
+                # Store toggle state in session state with unique key
+                toggle_key = f"toggle_{model}"
+                if toggle_key not in st.session_state:
+                    st.session_state[toggle_key] = True
+                st.session_state[toggle_key] = cols[col_idx].checkbox(
+                    model,
+                    value=st.session_state[toggle_key],
+                    key=f"model_toggle_{model}"
+                )
+    
     # Individual Precision-Recall plots for each class
     unique_classes = class_data['Class'].unique()
     num_classes = len(unique_classes)
@@ -237,7 +245,15 @@ with tab2:
             class_idx = row * 3 + col_idx
             if class_idx < num_classes:
                 current_class = unique_classes[class_idx]
-                class_specific_data = class_data[class_data['Class'] == current_class]
+                
+                # Filter data based on visible models
+                visible_models = [model for model in selected_models 
+                                if st.session_state[f"toggle_{model}"]]
+                
+                class_specific_data = class_data[
+                    (class_data['Class'] == current_class) & 
+                    (class_data['Model'].isin(visible_models))
+                ]
                 
                 fig = px.scatter(
                     class_specific_data,
@@ -245,7 +261,7 @@ with tab2:
                     y='Recall',
                     color='Model',
                     title=f'Precision vs Recall: {current_class}',
-                    height=300  # Adjust height as needed
+                    height=300
                 )
                 
                 # Update layout for better visibility
@@ -253,7 +269,7 @@ with tab2:
                     xaxis_range=[0, 1],
                     yaxis_range=[0, 1],
                     margin=dict(l=40, r=40, t=40, b=40),
-                    showlegend=False,  # Hide individual legends
+                    showlegend=False  # Hide individual legends
                 )
                 
                 # Add diagonal reference line

app.py

@@ -198,31 +198,39 @@ with tab2:
     )
     st.plotly_chart(fig, use_container_width=True)
     
-    # Create a single legend figure
-    legend_fig = go.Figure()
-    for model in selected_models:
-        legend_fig.add_trace(go.Scatter(
-            x=[None],
-            y=[None],
-            mode='markers',
-            name=model,
-            showlegend=True
-        ))
-    legend_fig.update_layout(
-        showlegend=True,
-        legend=dict(
-            orientation="h",
-            yanchor="middle",
-            y=1,
-            xanchor="center",
-            x=0.5,
-            font=dict(size=12)
-        ),
-        margin=dict(l=0, r=0, t=0, b=0),
-        height=50
-    )
-    st.plotly_chart(legend_fig, use_container_width=True)
-
+    # Create a shared legend container
+    legend_data = []
+    for i, model in enumerate(selected_models):
+        legend_data.append({
+            'Model': model,
+            'Visible': True,
+            'Index': i
+        })
+    legend_df = pd.DataFrame(legend_data)
+    
+    # Create toggles for models using st.columns
+    st.markdown("### Select Models to Display:")
+    
+    # Calculate how many columns we need (aim for about 4-5 models per row)
+    models_per_row = 4
+    num_rows = (len(selected_models) + models_per_row - 1) // models_per_row
+    
+    for row in range(num_rows):
+        cols = st.columns(models_per_row)
+        for col_idx in range(models_per_row):
+            model_idx = row * models_per_row + col_idx
+            if model_idx < len(selected_models):
+                model = selected_models[model_idx]
+                # Store toggle state in session state with unique key
+                toggle_key = f"toggle_{model}"
+                if toggle_key not in st.session_state:
+                    st.session_state[toggle_key] = True
+                st.session_state[toggle_key] = cols[col_idx].checkbox(
+                    model,
+                    value=st.session_state[toggle_key],
+                    key=f"model_toggle_{model}"
+                )
+    
     # Individual Precision-Recall plots for each class
     unique_classes = class_data['Class'].unique()
     num_classes = len(unique_classes)
@@ -237,7 +245,15 @@ with tab2:
             class_idx = row * 3 + col_idx
             if class_idx < num_classes:
                 current_class = unique_classes[class_idx]
-                class_specific_data = class_data[class_data['Class'] == current_class]
+                
+                # Filter data based on visible models
+                visible_models = [model for model in selected_models 
+                                if st.session_state[f"toggle_{model}"]]
+                
+                class_specific_data = class_data[
+                    (class_data['Class'] == current_class) & 
+                    (class_data['Model'].isin(visible_models))
+                ]
                 
                 fig = px.scatter(
                     class_specific_data,
@@ -245,7 +261,7 @@ with tab2:
                     y='Recall',
                     color='Model',
                     title=f'Precision vs Recall: {current_class}',
-                    height=300  # Adjust height as needed
+                    height=300
                 )
                 
                 # Update layout for better visibility
@@ -253,7 +269,7 @@ with tab2:
                     xaxis_range=[0, 1],
                     yaxis_range=[0, 1],
                     margin=dict(l=40, r=40, t=40, b=40),
-                    showlegend=False,  # Hide individual legends
+                    showlegend=False  # Hide individual legends
                 )
                 
                 # Add diagonal reference line