Update app.py

app.py

@@ -232,20 +232,75 @@ 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 button to export data to CSV
-                if st.button("Export to CSV"):
+
+                # 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",
+                # 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)')
+
+                    
+
+
                 # Full-width plot for the first category
                 create_bar_chart(df, score_columns[0])