[Fix] force rendering of axes

app.py

@@ -5,27 +5,26 @@ 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
-    
+        return None  # Hide the plot if no selection
+
     # 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()
-    fig.add_trace(go.Heatmap(
+    fig = go.Figure(data=go.Heatmap(
         z=similarities,
         x=selected_models,
         y=selected_models,
         colorscale='Viridis',
         zmin=0, zmax=1,
-        text=similarities,    # Values to show on hover
+        text=similarities,
         hoverinfo="text"
     ))
     
-    # Update layout for overall figure settings
+    # Update layout for title, size, margins, etc.
     fig.update_layout(
         title=f"Similarity Matrix for {selected_dataset}",
         xaxis_title="Models",
@@ -35,28 +34,26 @@ def create_heatmap(selected_models, selected_dataset):
         margin=dict(b=100, l=100)
     )
     
-    # Force both axes to be categorical by explicitly specifying tick values and text
+    # Force axes to be categorical and explicitly set the order
     fig.update_xaxes(
         type="category",
-        tickmode="array",
-        tickvals=selected_models,
-        ticktext=selected_models,
         tickangle=45,
+        categoryorder="array",
+        categoryarray=selected_models,  # Explicitly force ordering to match your list
         automargin=True,
         showgrid=True,
         showticklabels=True
     )
     fig.update_yaxes(
         type="category",
-        tickmode="array",
-        tickvals=selected_models,
-        ticktext=selected_models,
+        categoryorder="array",
+        categoryarray=selected_models,
         automargin=True,
         showgrid=True,
         showticklabels=True
     )
     
-    return fig  # Return only the figure
+    return fig
 
 
 def validate_inputs(selected_models, selected_dataset):