[Fix] grey plot

app.py

@@ -1,61 +1,36 @@
 import gradio as gr
-import plotly.graph_objects as go
+import plotly.express as px
 import numpy as np
 from src.dataloading import get_leaderboard_models_cached, get_leaderboard_datasets
 
 def create_heatmap(selected_models, selected_dataset):
     if not selected_models or not selected_dataset:
-        return None  # Hide the plot if no selection
+        return None  # Hide 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
+    similarities = (similarities + similarities.T) / 2  # make symmetric
+    similarities = np.round(similarities, 2)
 
-    # Create the heatmap figure
-    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 for title, size, margins, etc.
+    # Use Plotly Express imshow to create a heatmap
+    fig = px.imshow(similarities,
+                    x=selected_models,
+                    y=selected_models,
+                    color_continuous_scale='Viridis',
+                    zmin=0, zmax=1,
+                    text_auto=True)
+    # Move x-axis labels to top and adjust tick angle for readability
+    fig.update_xaxes(side="top", tickangle=45)
+    # Update overall layout: title, dimensions, margins
     fig.update_layout(
         title=f"Similarity Matrix for {selected_dataset}",
-        xaxis_title="Models",
-        yaxis_title="Models",
-        width=800 + 20 * len(selected_models),
-        height=800 + 20 * len(selected_models),
+        width=800 + 20 * size,
+        height=800 + 20 * size,
         margin=dict(b=100, l=100)
     )
-    
-    # Force axes to be categorical and explicitly set the order
-    fig.update_xaxes(
-        type="category",
-        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",
-        categoryorder="array",
-        categoryarray=selected_models,
-        automargin=True,
-        showgrid=True,
-        showticklabels=True
-    )
-    
     return fig
 
-
 def validate_inputs(selected_models, selected_dataset):
     if not selected_models:
         raise gr.Error("Please select at least one model!")