[Fix] populate plot

app.py

@@ -4,33 +4,32 @@ import numpy as np
 from src.dataloading import get_leaderboard_models_cached, get_leaderboard_datasets
 
 def create_heatmap(selected_models, selected_dataset):
-    print(f"Selected Models: {selected_models}")  # Debugging
-    print(f"Selected Dataset: {selected_dataset}")  # Debugging
     if not selected_models or not selected_dataset:
-        print("No models or dataset selected")  # Debugging
-        return None, gr.update(visible=False)   # Hide the plot if no models or dataset selected
+        return None, gr.update(visible=False)  # Hide if no selection
     
     # Generate random similarity matrix
     size = len(selected_models)
     similarities = np.random.rand(size, size)
     similarities = (similarities + similarities.T) / 2  # Make symmetric
     
-    # Create plot
-    fig = go.Figure(data=go.Heatmap(
+    # ✅ Explicitly create a Figure object before adding Heatmap
+    fig = go.Figure()
+    fig.add_trace(go.Heatmap(
         z=similarities,
         x=selected_models,
         y=selected_models,
         colorscale='Viridis'
     ))
-    
+
     fig.update_layout(
         title=f"Similarity Matrix for {selected_dataset}",
         width=800,
-        height=800
+        height=800,
+        xaxis_title="Models",
+        yaxis_title="Models"
     )
     
-    print("Heatmap generated successfully")  # Debugging
-    return fig, gr.update(visible=True)  # Show the plot
+    return fig, gr.update(visible=True)  # ✅ Ensure visibility
 
 def validate_inputs(selected_models, selected_dataset):
     if not selected_models:
@@ -70,7 +69,7 @@ with gr.Blocks(title="LLM Similarity Analyzer") as demo:
     ).then(
         fn=create_heatmap,
         inputs=[model_dropdown, dataset_dropdown],
-        outputs=[heatmap, heatmap]
+        outputs=[heatmap, heatmap.visible]
     )
     
     # Clear button