Update src/streamlit_app.py

src/streamlit_app.py

@@ -154,7 +154,7 @@ def create_bar_chart(df, view_type):
     
     if view_type == "Total Score":
         
-        # Format df
+        # Copy df
         df_fig = df.copy()
         df_fig = df_fig[df_fig["score"] != np.inf]
 
@@ -211,7 +211,7 @@ def create_bar_chart(df, view_type):
     
     elif view_type == "Per Embodiment":
 
-        # Format df
+        # Copy df
         df_fig = df.copy()
         df_fig = df_fig[df_fig["score"] != np.inf]
 
@@ -293,7 +293,7 @@ def create_bar_chart(df, view_type):
     
     else:  # Per Category
         
-        # Format df
+        # Copy df
         df_fig = df.copy()
         df_fig = df_fig[df_fig["score"] != np.inf]
 
@@ -377,6 +377,42 @@ def create_bar_chart(df, view_type):
     
     return fig
 
+def create_summary_table(df):
+
+    # Copy df
+    df_table = df.copy()
+    df_table = df_table[df_table["score"] != np.inf]
+    
+    # Calculate total score per model
+    df_total = df_table.groupby("model")[["score"]].mean().reset_index()
+    df_total.columns = ["model", "Total Score"]
+    
+    # Calculate scores per embodiment
+    df_embodiment = df_table.groupby(["model", "embodiment"])[["score"]].mean().reset_index()
+    df_embodiment_pivot = df_embodiment.pivot(index="model", columns="embodiment", values="score")
+    df_embodiment_pivot.columns = [f"{col}" for col in df_embodiment_pivot.columns]
+    
+    # Calculate scores per category
+    df_category = df_table.copy()
+    df_category["category"] = df_category["category"].apply(ast.literal_eval)
+    df_category = df_category.explode("category")
+    df_category = df_category.groupby(["model", "category"])[["score"]].mean().reset_index()
+    df_category_pivot = df_category.pivot(index="model", columns="category", values="score")
+    df_category_pivot.columns = [f"{col}" for col in df_category_pivot.columns]
+    
+    # Combine all tables
+    df_summary = df_total.set_index("model")
+    df_summary = df_summary.join(df_embodiment_pivot)
+    df_summary = df_summary.join(df_category_pivot)
+    
+    # Sort by total score
+    df_summary = df_summary.sort_values(by="Total Score", ascending=True)
+    
+    # Reset index to make model a column again
+    df_summary = df_summary.reset_index()
+    
+    return df_summary
+
 # Header
 st.markdown("""
 <div class="header-container">
@@ -429,7 +465,19 @@ st.caption("🔹 Note: Lower scores indicate better performance.")
 
 # Detailed table
 with st.expander("View Detailed Scores"):
-    st.dataframe(df.style.background_gradient(cmap='Blues_r', subset=df.columns), width="stretch")  #TODO
+    # Create the summary table
+    df_summary = create_summary_table(df)
+    
+    # Display table
+    st.dataframe(
+        df_summary.style.background_gradient(
+            cmap='Blues_r', 
+            subset=[col for col in df_summary.columns if col != 'model']
+        ).format("{:.2f}", subset=[col for col in df_summary.columns if col != 'model']),
+        use_container_width=True
+    )
+
+    #st.dataframe(df.style.background_gradient(cmap='Blues_r', subset=df.columns), width="stretch")  #TODO
 
 with st.expander("How to Test Your Model", expanded=True):
     # Step 1