Update app.py

app.py

@@ -86,37 +86,37 @@ def get_model_info(df):
 
     return df
 
-def calculate_highest_combined_score(data, column):
-    score_columns = ['Average', 'AGIEval', 'GPT4All', 'TruthfulQA', 'Bigbench']
-    # Ensure the column exists and has numeric data
-    if column not in data.columns or not pd.api.types.is_numeric_dtype(data[column]):
-        return column, {}
-    scores = data[column].dropna().tolist()
-    models = data['Model'].tolist()
-    top_combinations = {r: [] for r in range(2, 5)}
-    for r in range(2, 5):
-        for combination in combinations(zip(scores, models), r):
-            combined_score = sum(score for score, _ in combination)
-            top_combinations[r].append((combined_score, tuple(model for _, model in combination)))
-        top_combinations[r].sort(key=lambda x: x[0], reverse=True)
-        top_combinations[r] = top_combinations[r][:5]
-    return column, top_combinations
-
-# Modified function to display the results of the highest combined scores using st.dataframe
-def display_highest_combined_scores(data):
-    score_columns = ['Average', 'AGIEval', 'GPT4All', 'TruthfulQA', 'Bigbench']
-    with st.spinner('Calculating highest combined scores...'):
-        results = [calculate_highest_combined_score(data, col) for col in score_columns]
-        for column, top_combinations in results:
-            st.subheader(f"Top Combinations for {column}")
-            for r, combinations in top_combinations.items():
-                # Prepare data for DataFrame
-                rows = [{'Score': score, 'Models': ', '.join(combination)} for score, combination in combinations]
-                df = pd.DataFrame(rows)
-                
-                # Display using st.dataframe
-                st.markdown(f"**Number of Models: {r}**")
-                st.dataframe(df, height=150)  # Adjust height as necessary
+#def calculate_highest_combined_score(data, column):
+#    score_columns = ['Average', 'AGIEval', 'GPT4All', 'TruthfulQA', 'Bigbench']
+#    # Ensure the column exists and has numeric data
+#    if column not in data.columns or not pd.api.types.is_numeric_dtype(data[column]):
+#        return column, {}
+#    scores = data[column].dropna().tolist()
+#    models = data['Model'].tolist()
+#    top_combinations = {r: [] for r in range(2, 5)}
+#    for r in range(2, 5):
+#        for combination in combinations(zip(scores, models), r):
+#            combined_score = sum(score for score, _ in combination)
+#            top_combinations[r].append((combined_score, tuple(model for _, model in combination)))
+#        top_combinations[r].sort(key=lambda x: x[0], reverse=True)
+#        top_combinations[r] = top_combinations[r][:5]
+#    return column, top_combinations
+
+## Modified function to display the results of the highest combined scores using st.dataframe
+#def display_highest_combined_scores(data):
+#    score_columns = ['Average', 'AGIEval', 'GPT4All', 'TruthfulQA', 'Bigbench']
+#    with st.spinner('Calculating highest combined scores...'):
+#        results = [calculate_highest_combined_score(data, col) for col in score_columns]
+#        for column, top_combinations in results:
+#            st.subheader(f"Top Combinations for {column}")
+#            for r, combinations in top_combinations.items():
+#                # Prepare data for DataFrame
+#                rows = [{'Score': score, 'Models': ', '.join(combination)} for score, combination in combinations]
+#                df = pd.DataFrame(rows)
+#                
+#                # Display using st.dataframe
+#                st.markdown(f"**Number of Models: {r}**")
+#                st.dataframe(df, height=150)  # Adjust height as necessary
 
                     
 
@@ -263,7 +263,7 @@ def main():
                 with col4:
                     create_bar_chart(df, score_columns[4])
 
-                display_highest_combined_scores(full_df)  # Call to display the calculated scores
+#                display_highest_combined_scores(full_df)  # Call to display the calculated scores
             except Exception as e:
                 st.error("An error occurred while processing the markdown table.")
                 st.error(str(e))