[Fix] debugging

app.py

@@ -6,30 +6,26 @@ from src.dataloading import get_leaderboard_models_cached, get_leaderboard_datas
 def create_heatmap(selected_models, selected_dataset):
     if not selected_models or not selected_dataset:
         return None  # Return None to hide the plot if inputs are missing
-
+    
     # Generate random similarity matrix
     size = len(selected_models)
     similarities = np.random.rand(size, size)
     similarities = (similarities + similarities.T) / 2  # Make symmetric
     similarities = np.round(similarities, 2)  # Round for clarity
-
-    print(f"Generated heatmap with {len(selected_models)} models")
-    print("Sample coordinates:", selected_models[:2])
-    print("Sample similarity value:", similarities[0][0])
-
+    
     # Create the heatmap figure
-    fig = go.Figure(data=go.Heatmap(
+    fig = go.Figure()
+    fig.add_trace(go.Heatmap(
         z=similarities,
         x=selected_models,
         y=selected_models,
         colorscale='Viridis',
-        zmin=0,
-        zmax=1,
-        text=similarities,
+        zmin=0, zmax=1,
+        text=similarities,    # Values to show on hover
         hoverinfo="text"
     ))
-
-    # Update layout with axis titles and margins
+    
+    # Update layout for overall figure settings
     fig.update_layout(
         title=f"Similarity Matrix for {selected_dataset}",
         xaxis_title="Models",
@@ -38,19 +34,39 @@ def create_heatmap(selected_models, selected_dataset):
         height=800 + 20 * len(selected_models),
         margin=dict(b=100, l=100)
     )
-
-    # Force the axes to use category types so that model names appear
-    fig.update_xaxes(type="category", tickangle=45, automargin=True)
-    fig.update_yaxes(type="category", automargin=True)
-
+    
+    # Force both axes to be categorical by explicitly specifying tick values and text
+    fig.update_xaxes(
+        type="category",
+        tickmode="array",
+        tickvals=selected_models,
+        ticktext=selected_models,
+        tickangle=45,
+        automargin=True,
+        showgrid=True,
+        showticklabels=True
+    )
+    fig.update_yaxes(
+        type="category",
+        tickmode="array",
+        tickvals=selected_models,
+        ticktext=selected_models,
+        automargin=True,
+        showgrid=True,
+        showticklabels=True
+    )
+    
     return fig  # Return only the figure
 
+
 def validate_inputs(selected_models, selected_dataset):
     if not selected_models:
         raise gr.Error("Please select at least one model!")
     if not selected_dataset:
         raise gr.Error("Please select a dataset!")
+    
 
+# Gradio interface setup
 with gr.Blocks(title="LLM Similarity Analyzer") as demo:
     gr.Markdown("## Model Similarity Comparison Tool")
     
@@ -75,7 +91,7 @@ with gr.Blocks(title="LLM Similarity Analyzer") as demo:
     generate_btn = gr.Button("Generate Heatmap", variant="primary")
     heatmap = gr.Plot(label="Similarity Heatmap", visible=True)
     
-    # Event handling: Validate first then create the heatmap
+    # Use a single output (the figure)
     generate_btn.click(
         fn=validate_inputs,
         inputs=[model_dropdown, dataset_dropdown],
@@ -83,10 +99,10 @@ with gr.Blocks(title="LLM Similarity Analyzer") as demo:
     ).then(
         fn=create_heatmap,
         inputs=[model_dropdown, dataset_dropdown],
-        outputs=heatmap  # Only one output is needed
+        outputs=heatmap
     )
     
-    # Clear button
+    # Clear button: clear selections and the plot
     clear_btn = gr.Button("Clear Selection")
     clear_btn.click(
         lambda: [None, None, None],