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 @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 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()