Update app.py

app.py

@@ -232,74 +232,19 @@ def main():
                 selected_models = st.multiselect('Select models to compare', df['Model'].unique())
                 comparison_df = df[df['Model'].isin(selected_models)]
                 st.dataframe(comparison_df)
-
-                # Add a row with two columns for the buttons
-                button_row = st.columns(2)
-                
-                # Add the "Export to CSV" button in the first column
-                with button_row[0]:
-                    if st.button("Export to CSV"):
-                      # Export the DataFrame to CSV
-                      csv_data = df.to_csv(index=False)
-
-                      # Create a link to download the CSV file
-                      st.download_button(
-                          label="Download CSV",
-                          data=csv_data,
-                          file_name="leaderboard.csv",
-                          key="download-csv",
-                          help="Click to download the CSV file",
-                          )
-
-                # Add the new button in the second column
-                with button_row[1]:
-                    if st.button("Fetch Top Mergeki-Configs"):
-                        # Export the DataFrame to CSV and save it to /tmp/models.csv
-                        df.to_csv('/tmp/models.csv', index=False)# Add a button to export data to CSV
-                        # Load the CSV data
-                        df = pd.read_csv('/tmp/models.csv')
-
-                        # Sort the data by the second column (assuming the column name is 'Average')
-                        df_sorted = df.sort_values(by='Average', ascending=False)
-
-                    # Open the file in append mode
-                    with open('configurations.txt', 'a') as file:
-                        # Get model cards for the top 20 entries
-                        for index, row in df_sorted.head(20).iterrows():
-                            model_name = row['Model'].rstrip()
-                            card = ModelCard.load(model_name)
-                            file.write(f'Model Name: {model_name}\n')
-                            file.write(f'Scores: {row["Average"]}\n')  # Assuming 'Average' is the benchmark score
-                            file.write(f'AGIEval: {row["AGIEval"]}\n')
-                            file.write(f'GPT4All: {row["GPT4All"]}\n')
-                            file.write(f'TruthfulQA: {row["TruthfulQA"]}\n')
-                            file.write(f'Bigbench: {row["Bigbench"]}\n')
-                            file.write(f'Model Card: {card}\n')
-
-                    # Open the second file in read mode
-                    with open('configurations.txt', 'r') as file:
-                        # Read the content
-                        content = file.read()
-
-                        # Find all text between 'yaml' and '```'
-                        matches = re.findall(r'yaml(.*?)```', content, re.DOTALL)
-
-                    # Open the file 'configurations2.txt' in write mode
-                    with open('configurations2.txt', 'w') as file:
-                        # Write the matches to the file
-                        for row, match in zip(df_sorted[['Model', 'Average', 'AGIEval', 'GPT4All', 'TruthfulQA', 'Bigbench']].head(20).values, matches):
-                            file.write(f'Model Name: {row[0]}\n')
-                            file.write(f'Scores: {row[1]}\n')
-                            file.write(f'AGIEval: {row[2]}\n')
-                            file.write(f'GPT4All: {row[3]}\n')
-                            file.write(f'TruthfulQA: {row[4]}\n')
-                            file.write(f'Bigbench: {row[5]}\n')
-                            file.write('yaml' + match + '```\n')
-                     # Provide a link to download the generated file
-                    st.markdown('[Download configurations](file:configurations2.txt)')
-
-                    
-
+                # Add a button to export data to CSV
+                if st.button("Export to CSV"):
+                    # Export the DataFrame to CSV
+                    csv_data = df.to_csv(index=False)
+
+                    # Create a link to download the CSV file
+                    st.download_button(
+                        label="Download CSV",
+                        data=csv_data,
+                        file_name="leaderboard.csv",
+                        key="download-csv",
+                        help="Click to download the CSV file",
+                    )
 
                 # Full-width plot for the first category
                 create_bar_chart(df, score_columns[0])
@@ -348,4 +293,3 @@ def main():
 # Run the main function if this script is run directly
 if __name__ == "__main__":
     main()
-