Update app.py

app.py

@@ -10,14 +10,9 @@ from yall import create_yall
 import plotly.graph_objs as go
 from huggingface_hub import ModelCard
 
-
-
 def calculate_pages(df, items_per_page):
     return -(-len(df) // items_per_page)  # Equivalent to math.ceil(len(df) / items_per_page)
 
-    
-
-# Function to get model info from Hugging Face API using caching
 @st.cache_data
 def cached_model_info(api, model):
     try:
@@ -25,7 +20,6 @@ def cached_model_info(api, model):
     except (RepositoryNotFoundError, RevisionNotFoundError):
         return None
 
-# Function to get model info from DataFrame and update it with likes and tags
 @st.cache_data
 def get_model_info(df):
     api = HfApi()
@@ -40,143 +34,47 @@ def get_model_info(df):
             df.loc[index, 'Tags'] = ''
     return df
 
-# Function to convert markdown table to DataFrame and extract Hugging Face URLs
 def convert_markdown_table_to_dataframe(md_content):
-    """
-    Converts markdown table to Pandas DataFrame, handling special characters and links,
-    extracts Hugging Face URLs, and adds them to a new column.
-    """
-    # Remove leading and trailing | characters
     cleaned_content = re.sub(r'\|\s*$', '', re.sub(r'^\|\s*', '', md_content, flags=re.MULTILINE), flags=re.MULTILINE)
-
-    # Create DataFrame from cleaned content
     df = pd.read_csv(StringIO(cleaned_content), sep="\|", engine='python')
-
-    # Remove the first row after the header
     df = df.drop(0, axis=0)
-
-    # Strip whitespace from column names
     df.columns = df.columns.str.strip()
-
-    # Extract Hugging Face URLs and add them to a new column
     model_link_pattern = r'\[(.*?)\]\((.*?)\)\s*\[.*?\]\(.*?\)'
     df['URL'] = df['Model'].apply(lambda x: re.search(model_link_pattern, x).group(2) if re.search(model_link_pattern, x) else None)
-
-    # Clean Model column to have only the model link text
     df['Model'] = df['Model'].apply(lambda x: re.sub(model_link_pattern, r'\1', x))
-
     return df
 
-@st.cache_data
-def get_model_info(df):
-    api = HfApi()
-
-    # Initialize new columns for likes and tags
-    df['Likes'] = None
-    df['Tags'] = None
-
-    # Iterate through DataFrame rows
-    for index, row in df.iterrows():
-        model = row['Model'].strip()
-        try:
-            model_info = api.model_info(repo_id=str(model))
-            df.loc[index, 'Likes'] = model_info.likes
-            df.loc[index, 'Tags'] = ', '.join(model_info.tags)
-
-        except (RepositoryNotFoundError, RevisionNotFoundError):
-            df.loc[index, 'Likes'] = -1
-            df.loc[index, 'Tags'] = ''
-
-    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
-
-                    
-
-
-# Function to create bar chart for a given category
 def create_bar_chart(df, category):
-    """Create and display a bar chart for a given category."""
     st.write(f"### {category} Scores")
-
-    # Sort the DataFrame based on the category score
     sorted_df = df[['Model', category]].sort_values(by=category, ascending=True)
-
-    # Create the bar chart with a color gradient (using 'Viridis' color scale as an example)
     fig = go.Figure(go.Bar(
         x=sorted_df[category],
         y=sorted_df['Model'],
         orientation='h',
-        marker=dict(color=sorted_df[category], colorscale='Picnic')  # You can change 'Viridis' to another color scale
+        marker=dict(color=sorted_df[category], colorscale='Agsunset')
     ))
-
-    # Update layout for better readability
     fig.update_layout(
         margin=dict(l=20, r=20, t=20, b=20)
     )
-
-    # Adjust the height of the chart based on the number of rows in the DataFrame
     st.plotly_chart(fig, use_container_width=True, height=len(df) * 35)
 
 def fetch_merge_configs(df):
-    # 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('/tmp/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'Scores: {row["Average"]}\n')
             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('/tmp/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('/tmp/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')
@@ -185,45 +83,32 @@ def fetch_merge_configs(df):
             file.write(f'TruthfulQA: {row[4]}\n')
             file.write(f'Bigbench: {row[5]}\n')
             file.write('yaml' + match + '```\n')
- 
 
-# Main function to run the Streamlit app
 def main():
-    # Set page configuration and title
     st.set_page_config(page_title="YALL - Yet Another LLM Leaderboard", layout="wide")
-
     st.title("🏆 YALL - Yet Another LLM Leaderboard")
     st.markdown("Leaderboard made with 🧐 [LLM AutoEval](https://github.com/mlabonne/llm-autoeval) using [Nous](https://huggingface.co/NousResearch) benchmark suite.")
-
-    # Create tabs for leaderboard and about section
     content = create_yall()
     tab1, tab2 = st.tabs(["🏆 Leaderboard", "📝 About"])
 
-    # Leaderboard tab
     with tab1:
         if content:
             try:
                 score_columns = ['Average', 'AGIEval', 'GPT4All', 'TruthfulQA', 'Bigbench']
-
-                # Display dataframe
                 full_df = convert_markdown_table_to_dataframe(content)
 
                 for col in score_columns:
-                    # Corrected use of pd.to_numeric
                     full_df[col] = pd.to_numeric(full_df[col].str.strip(), errors='coerce')
 
                 full_df = get_model_info(full_df)
                 full_df['Tags'] = full_df['Tags'].fillna('')
                 df = pd.DataFrame(columns=full_df.columns)
 
-                # Toggles for filtering by tags
                 show_phi = st.checkbox("Phi (2.8B)", value=True)
                 show_mistral = st.checkbox("Mistral (7B)", value=True)
                 show_other = st.checkbox("Other", value=True)
 
-                # Create a DataFrame based on selected filters
                 dfs_to_concat = []
-
                 if show_phi:
                     dfs_to_concat.append(full_df[full_df['Tags'].str.lower().str.contains('phi,|phi-msft,')])
                 if show_mistral:
@@ -232,31 +117,22 @@ def main():
                     other_df = full_df[~full_df['Tags'].str.lower().str.contains('phi,|phi-msft,|mistral,')]
                     dfs_to_concat.append(other_df)
 
-                # Concatenate the DataFrames
                 if dfs_to_concat:
                     df = pd.concat(dfs_to_concat, ignore_index=True)
 
-                # Add a search bar
                 search_query = st.text_input("Search models", "")
-
-                # Filter the DataFrame based on the search query
                 if search_query:
                     df = df[df['Model'].str.contains(search_query, case=False)]
 
-                # Add a selectbox for page selection
                 items_per_page = 50
                 pages = calculate_pages(df, items_per_page)
                 page = st.selectbox("Page", list(range(1, pages + 1)))
 
-                # Sort the DataFrame by 'Average' column in descending order
                 df = df.sort_values(by='Average', ascending=False)
-
-                # Slice the DataFrame based on the selected page
                 start = (page - 1) * items_per_page
                 end = start + items_per_page
                 df = df[start:end]
-                
-                # Display the filtered DataFrame or the entire leaderboard
+
                 st.dataframe(
                     df[['Model'] + score_columns + ['Likes', 'URL']],
                     use_container_width=True,
@@ -274,12 +150,29 @@ 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
+                
+                selected_benchmarks = st.multiselect('Select benchmarks to include in the average', score_columns, default=score_columns)
+
+                if selected_benchmarks:
+                    df['Filtered Average'] = df[selected_benchmarks].mean(axis=1)
+                    df = df.sort_values(by='Filtered Average', ascending=False)
+                    st.dataframe(
+                        df[['Model'] + selected_benchmarks + ['Filtered Average', 'Likes', 'URL']],
+                        use_container_width=True,
+                        column_config={
+                            "Likes": st.column_config.NumberColumn(
+                                "Likes",
+                                help="Number of likes on Hugging Face",
+                                format="%d ❤️",
+                            ),
+                            "URL": st.column_config.LinkColumn("URL"),
+                        },
+                        hide_index=True,
+                        height=len(df) * 37,
+                    )
+                
                 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,
@@ -288,19 +181,11 @@ def main():
                         help="Click to download the CSV file",
                     )
                 if st.button("Fetch Merge-Configs"):
-                    # Call the function with the current DataFrame
-                    configurations, matches, csv_data = fetch_merge_configs(full_df) # Assuming full_df is your DataFrame
-                    # You can then display the configurations or matches as needed, or write them to a file
-                    # For example, displaying the configurations:
+                    configurations, matches, csv_data = fetch_merge_configs(full_df)
                     for config in configurations:
                         st.text(f"Model Name: {config['Model Name']}\nScores: {config['Scores']}\nAGIEval: {config['AGIEval']}\nGPT4All: {config['GPT4All']}\nTruthfulQA: {config['TruthfulQA']}\nBigbench: {config['Bigbench']}\nModel Card: {config['Model Card']}\n\n")
-
-                    # Convert the list of dictionaries to a DataFrame
                     configurations_df = pd.DataFrame(configurations)
-
-                    # Convert the DataFrame to a CSV string
                     configurations_csv = configurations_df.to_csv(index=False)
-
                     st.download_button(
                         label="Download Configurations",
                         data=configurations_csv,
@@ -309,33 +194,28 @@ def main():
                         help="Click to download the CSV file",
                     )
 
-                
-                # Full-width plot for the first category
-                create_bar_chart(df, score_columns[0])
+                create_bar_chart(df, 'Filtered Average')
 
-                # Next two plots in two columns
                 col1, col2 = st.columns(2)
                 with col1:
                     create_bar_chart(df, score_columns[1])
                 with col2:
                     create_bar_chart(df, score_columns[2])
 
-                # Last two plots in two columns
                 col3, col4 = st.columns(2)
                 with col3:
                     create_bar_chart(df, score_columns[3])
                 with col4:
                     create_bar_chart(df, score_columns[4])
 
-#                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))
         else:
             st.error("Failed to download the content from the URL provided.")
-     # About tab
+            
     with tab2:
-        st.markdown('''markdown
+        st.markdown('''
             ### Nous benchmark suite
             Popularized by [Teknium](https://huggingface.co/teknium) and [NousResearch](https://huggingface.co/NousResearch), this benchmark suite aggregates four benchmarks:
             * [**AGIEval**](https://arxiv.org/abs/2304.06364) (0-shot): `agieval_aqua_rat,agieval_logiqa_en,agieval_lsat_ar,agieval_lsat_lr,agieval_lsat_rc,agieval_sat_en,agieval_sat_en_without_passage,agieval_sat_math`
@@ -349,82 +229,7 @@ def main():
             * Change the `gist_id` in [yall.py](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard/blob/main/yall.py#L126).
             * Create "New Secret" in Settings > Variables and secrets (name: "github", value: [your GitHub token](https://github.com/settings/tokens))
             A special thanks to [gblazex](https://huggingface.co/gblazex) for providing many evaluations.
-            
-            
-            # Bonus: Workflow for Automating Model Evaluation and Selection
-            
-            ## Step 1. Export CSV Data from Another-LLM-LeaderBoards
-            Go to our [Another-LLM-LeaderBoards](https://leaderboards.example.com) and click the export csv data button. Save it to `/tmp/models.csv`.
-            
-            ## Step 2: Examine CSV Data
-            Run a script for extracting model names, benchmark scores, and model page link from the CSV data.
-
-            ```python
-            import re
-            from huggingface_hub import ModelCard
-            import pandas as pd
-
-            # 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('/tmp/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')
-            ```
-            
-            ## Step 3: Feed the Discovered Models, Scores and Configurations to LLM-client (shell-gpt)
-            Run your local LLM-client by feeding it all the discovered merged models, their benchmark scores and if found the configurations used to merge them. Provide it with an instruction similar to this:
-
-            ```bash
-            cat /tmp/configurations2.txt | sgpt --chat config "Based on the merged models that are provided here, along with their respective benchmark achievements and the configurations used in merging them, your task is to come up with a new configuration for a new merged model that will outperform all others. In your thought process, argue and reflect on your own choices to improve your thinking process and outcome"
-            ```
-            
-            ## Step 4: (Optional) Reflect on Initial Configuration Suggested by Chat-GPT
-            If you wanted to get particularly naughty, you could add a step like this where you make Chat-GPT rethink and reflect on the configuration it initially comes up with based on the information you gave it.
-
-            ```bash
-            for i in $(seq 1 3); do echo "$i" && sgpt --chat config "Repeat the process from before and again reflect and improve on your suggested configuration"; sleep 20; done
-            ```
-            
-            ## Step 5: Wait for Chat-GPT to give you a LeaderBoard-topping merge configuration
-            Wait for Chat-GPT to provide a new merge configuration.
-
-            ## Step 6: Enter the Configuration in Automergekit NoteBook
-            Fire up your automergekit NoteBook and enter in the configuration that was just so generously provided to you by Chat-GPT.
-
-            ## Step 7: Evaluate the New Merge using auto-llm-eval notebook
-            Fire up your auto-llm-eval notebook to see if the merge that Chat-GPT came up with is actually making any sense and performing well.
-
-            ## Step 8: Repeat the Process
-            Repeat this process for a few times every day, learning from each new model created.
-
-            ## Step 9: Rank the New Number One Model
-            Rank the new number one model and top your own LeaderBoard: (Model: CultriX/MergeCeption-7B-v3)
-            ![image.png](https://cdn-uploads.huggingface.co/production/uploads/6495d5a915d8ef6f01bc75eb/mFV3Ou469fk6ivj1XrD9d.png)
-            
-            ## Step 10: Automate the Process with Cronjob
-            Create a cronjob that automates this process 5 times every day, only to then learn from the models that it has created in order to create even better ones and I'm telling you that you better prepare yourself for some non-neglectable increases in benchmark scores for the near future.
-
-            Cheers,
-            CultriX
         ''')
-        
-
-
 
-# Run the main function if this script is run directly
 if __name__ == "__main__":
-    main()
+    main()