[Fix] categorical axis names

app.py

@@ -5,41 +5,41 @@ 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  # ✅ Just return None to hide the plot
-    
+        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
-    
+
     # Create the heatmap figure
-    fig = go.Figure(
-        data=go.Heatmap(
-            z=similarities,  # ✅ Ensure it's a NumPy array
-            x=selected_models,
-            y=selected_models,
-            colorscale='Viridis',
-            zmin=0, zmax=1,  # Normalize scale
-            text=similarities,  # ✅ Show values in heatmap
-            hoverinfo="text"
-        )
-    )
-    
-    # Improve axis readability
+    fig = go.Figure(data=go.Heatmap(
+        z=similarities,
+        x=selected_models,
+        y=selected_models,
+        colorscale='Viridis',
+        zmin=0,
+        zmax=1,
+        text=similarities,
+        hoverinfo="text"
+    ))
+
+    # Update layout with axis titles and margins
     fig.update_layout(
         title=f"Similarity Matrix for {selected_dataset}",
         xaxis_title="Models",
         yaxis_title="Models",
-        xaxis=dict(tickangle=45, automargin=True),
-        yaxis=dict(automargin=True),
         width=800 + 20 * len(selected_models),
         height=800 + 20 * len(selected_models),
-        margin=dict(b=100, l=100),  # Add bottom/left margin for labels
+        margin=dict(b=100, l=100)
     )
 
-    return fig  # ✅ Return only the figure
+    # 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)
 
+    return fig  # Return only the figure
 
 def validate_inputs(selected_models, selected_dataset):
     if not selected_models:
@@ -69,9 +69,9 @@ 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)  # ✅ Ensure visible=True
+    heatmap = gr.Plot(label="Similarity Heatmap", visible=True)
     
-    # Event handling
+    # Event handling: Validate first then create the heatmap
     generate_btn.click(
         fn=validate_inputs,
         inputs=[model_dropdown, dataset_dropdown],
@@ -79,7 +79,7 @@ with gr.Blocks(title="LLM Similarity Analyzer") as demo:
     ).then(
         fn=create_heatmap,
         inputs=[model_dropdown, dataset_dropdown],
-        outputs=heatmap  # ✅ Only one output (gr.Plot)
+        outputs=heatmap  # Only one output is needed
     )
     
     # Clear button
@@ -89,6 +89,5 @@ with gr.Blocks(title="LLM Similarity Analyzer") as demo:
         outputs=[model_dropdown, dataset_dropdown, heatmap]
     )
 
-
 if __name__ == "__main__":
     demo.launch()