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| import re | |
| import streamlit as st | |
| import requests | |
| import pandas as pd | |
| from io import StringIO | |
| import plotly.graph_objs as go | |
| from huggingface_hub import HfApi | |
| from huggingface_hub.utils import RepositoryNotFoundError, RevisionNotFoundError | |
| #from yall import create_yall | |
| def place_holder_dataframe(): | |
| list_dict = [ | |
| {"gist_id":"mistralai/Mistral-7B-Instruct-v0.3", | |
| "filename":"https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3/blob/main/README.md", | |
| "url":"https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3", | |
| "model_name":"Mistral-7B-Instruct-v0.3", | |
| "model_id":"mistralai/Mistral-7B-Instruct-v0.3", | |
| "Model":"Mistral-7B-Instruct-v0.3", | |
| "Elo":1200, | |
| "Undetected rate":0.27 | |
| }, | |
| { | |
| "gist_id":"mistralai/Mixtral-8x22B-Instruct-v0.1", | |
| "filename":"https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1/blob/main/README.md", | |
| "url":"https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1", | |
| "model_name":"Mixtral-8x22B-Instruct-v0.1", | |
| "model_id":"mistralai/Mixtral-8x22B-Instruct-v0.1", | |
| "Model":"Mixtral-8x22B-Instruct-v0.1", | |
| "Elo":1950, | |
| "Undetected rate":0.63 | |
| }, | |
| { | |
| "gist_id":"mistralai/Mixtral-8x7B-Instruct-v0.1", | |
| "filename":"https://huggingface.co/mistralai/Mixtral-8x7B-v0.1/blob/main/README.md", | |
| "url":"https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1", | |
| "model_name":"Mixtral-8x7B-Instruct-v0.1", | |
| "model_id":"mistralai/Mixtral-8x7B-Instruct-v0.1", | |
| "Model":"Mixtral-8x7B-Instruct-v0.1", | |
| "Elo":1467, | |
| "Undetected rate":0.41 | |
| } | |
| ] | |
| df = pd.DataFrame(list_dict) | |
| return df | |
| 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 | |
| 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 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='Inferno') | |
| )) | |
| # 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=35) | |
| # Example usage: | |
| # create_bar_chart(your_dataframe, 'Your_Category') | |
| def main(): | |
| st.set_page_config(page_title="LLM Roleplay Leaderboard", layout="wide") | |
| st.title("🏆🎭 LLM Roleplay Leaderboard") | |
| st.markdown("LLM Roleplay Leaderboard that uses scores from the matou garou roleplay game 🏠🐈.") | |
| #content = create_yall() | |
| tab1, tab2 = st.tabs(["🏆🎭 Leaderboard", "📝 About"]) | |
| df = place_holder_dataframe() | |
| with tab1: | |
| if len(df)>0: | |
| try: | |
| df = df.sort_values(by='Elo', ascending=False) | |
| # Add a search bar | |
| search_query = st.text_input("Search models", "") | |
| # Display the filtered DataFrame or the entire leaderboard | |
| st.dataframe( | |
| df[['Model', 'Elo', 'url', 'Undetected rate']], | |
| use_container_width=True, | |
| column_config={ | |
| "url": st.column_config.LinkColumn("url"), | |
| }, | |
| hide_index=True, | |
| ) | |
| # Filter the DataFrame based on the search query | |
| if search_query: | |
| df = df[df['Model'].str.contains(search_query, case=False)] | |
| # Comparison between models | |
| selected_models = st.multiselect('Select models to compare', df['Model'].unique()) | |
| comparison_df = df[df['Model'].isin(selected_models)] | |
| st.dataframe( | |
| comparison_df, | |
| use_container_width=True, | |
| column_config={ | |
| "url": st.column_config.LinkColumn("url"), | |
| }, | |
| hide_index=True, | |
| ) | |
| # 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, "Elo") | |
| # Next two plots in two columns | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| create_bar_chart(df, "Undetected rate") | |
| 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(''' | |
| ### Roleplay Leaderboard | |
| This space is here to present the results from the Matou-Garou space, where human and AI play a game of werewolf. | |
| It is meant as a social experience to see if you would be able to detect if talking to an AI. | |
| We also hope that this leaderboard can be used by video game creator in the future to select what model to select for LLM based NPCs | |
| Popularized by [Teknium](https://huggingface.co/teknium) and [NousResearch](https://huggingface.co/NousResearch), this benchmark suite aggregates four benchmarks | |
| Leaderboard copied from [Maxime Labonne](https://huggingface.co/mlabonne) | |
| ''') | |
| if __name__ == "__main__": | |
| main() | |