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import streamlit as st
import os
from datasets import load_dataset
import pandas as pd
import matplotlib.pyplot as plt
import argilla as rg
from datetime import datetime
ARGILLA_API_URL = os.environ.get("ARGILLA_API_URL")
ARGILLA_API_KEY = os.environ.get("ARGILLA_API_KEY")
HF_TOKEN = os.environ.get("HF_TOKEN")
client = rg.Argilla(
api_url=ARGILLA_API_URL,
api_key=ARGILLA_API_KEY
)
workspace = client.workspaces('cohere')
users_map = {str(user.id):user.username for user in list(workspace.users)}
ds = load_dataset("CohereForAI/mmlu-translations-results", split="train", token=HF_TOKEN)
df = ds.to_pandas()
st.title("🌐 MMLU Translation Review Progress 📈")
st.markdown(f"**Total tasks completed:** {len(ds)}")
# Get the current local time
now = datetime.now()
top_of_the_hour = now.replace(minute=0, second=0, microsecond=0)
# Calculate the minutes past the top of the hour
minutes_past = (now - top_of_the_hour).seconds // 60
# Display the time as X minutes ago
st.markdown(f"**Last updated:** {minutes_past} minutes ago")
st.header("Progress by Language")
# Extract the language from the metadata column and create a new column
df['language'] = df['metadata'].apply(lambda x: x.get('language'))
# Count the occurrences of each language
language_counts = df['language'].value_counts()
# Plotting the bar chart using matplotlib
fig, ax = plt.subplots()
language_counts.plot(kind='bar', ax=ax)
ax.set_title('Number of Completed Tasks for Each Language')
ax.set_xlabel('Language')
ax.set_ylabel('Count')
# Convert the language counts to a DataFrame for display in the table
language_counts_df = language_counts.reset_index()
language_counts_df.columns = ['Language', 'Count']
# Display the table in the Streamlit app
st.table(language_counts_df)
# Display the plot in the Streamlit app
st.pyplot(fig)
st.header("Leaderboard")
# Extract user_id from the is_edit_required field in the response column and count occurrences
user_ids = df['responses'].apply(lambda x: x['is_edit_required']).explode().apply(lambda x: x['user_id'])
user_id_counts = user_ids.value_counts()
# Map user IDs to usernames
user_id_counts.index = user_id_counts.index.map(users_map)
# Convert the user ID counts to a DataFrame for display in the table
user_id_counts_df = user_id_counts.reset_index()
user_id_counts_df.columns = ['Username', 'Count']
# Display the table of username counts in the Streamlit app
st.table(user_id_counts_df)
st.header("Raw Dataset")
st.dataframe(df) |