import os from typing import Dict, Tuple from uuid import UUID import altair as alt import argilla as rg from argilla.feedback import FeedbackDataset from argilla.client.feedback.dataset.remote.dataset import RemoteFeedbackDataset import gradio as gr import pandas as pd def obtain_source_target_datasets() -> ( Tuple[ FeedbackDataset | RemoteFeedbackDataset, FeedbackDataset | RemoteFeedbackDataset ] ): """ This function returns the source and target datasets to be used in the application. Returns: A tuple with the source and target datasets. The source dataset is filtered by the response status 'pending'. """ # Obtain the public dataset and see how many pending records are there source_dataset = rg.FeedbackDataset.from_argilla( os.getenv("SOURCE_DATASET"), workspace=os.getenv("SOURCE_WORKSPACE") ) filtered_source_dataset = source_dataset.filter_by(response_status=["pending"]) # Obtain a list of users from the private workspace target_dataset = rg.FeedbackDataset.from_argilla( os.getenv("RESULTS_DATASET"), workspace=os.getenv("RESULTS_WORKSPACE") ) return filtered_source_dataset, target_dataset def get_user_annotations_dictionary( dataset: FeedbackDataset | RemoteFeedbackDataset, ) -> Dict[str, int]: """ This function returns a dictionary with the username as the key and the number of annotations as the value. Args: dataset: The dataset to be analyzed. Returns: A dictionary with the username as the key and the number of annotations as the value. """ output = {} for record in dataset: for response in record.responses: if str(response.user_id) not in output.keys(): output[str(response.user_id)] = 1 else: output[str(response.user_id)] += 1 # Changing the name of the keys, from the id to the username for key in list(output.keys()): output[rg.User.from_id(UUID(key)).username] = output.pop(key) return output import altair as alt import pandas as pd import os def progress_bar_chart() -> alt.Chart: source_dataset, _ = obtain_source_target_datasets() total_records = int(os.getenv("TARGET_RECORDS")) # The total goal of records. annotated_records = len(source_dataset) # The number of records already annotated. pending_records = total_records - annotated_records # Calculate the pending records. percentage_complete = annotated_records / total_records * 100 # The percentage of completion. # Create a DataFrame for the progress bar data. progress_data = pd.DataFrame({ 'status': ['Completed', 'Pending'], 'percentage': [percentage_complete, 100 - percentage_complete], 'actual': [pending_records, annotated_records] }) # Create the progress bar chart. progress_bar = alt.Chart(progress_data).mark_bar(size=40).encode( x=alt.X('percentage:Q', axis=alt.Axis(title='Completion Percentage', format='%')), color=alt.Color('status:N', scale=alt.Scale(domain=['Completed', 'Pending'], range=['#28a745', '#dcdcdc'])), tooltip=['status', 'actual'] ) # Create the annotation text. text = progress_data.iloc[0]['actual'] # Get the 'Completed' actual number. label_text = alt.Chart(pd.DataFrame({'x_pos': [percentage_complete / 2], 'text': [f'{text} Completed']})).mark_text( align='center', dy=-10, fontSize=16, fontWeight='bold', color='white' ).encode( x='x_pos:Q', text='text:N' ) # Combine the bar and the label into a layered chart. chart = alt.layer(progress_bar, label_text).properties( title='Progress Towards Goal', width=800, height=150 ) # Configure the whole chart instead of individual elements. chart = chart.configure_axis(grid=False).configure_view(strokeWidth=0) return chart def donut_chart() -> alt.Chart: """ This function returns a donut chart with the number of annotated and pending records. Returns: An altair chart with the donut chart. """ source_dataset, _ = obtain_source_target_datasets() annotated_records = len(source_dataset) pending_records = int(os.getenv("TARGET_RECORDS")) - annotated_records source = pd.DataFrame( { "values": [annotated_records, pending_records], "category": ["Submitted", "Pending"], # Add a new column for categories } ) base = alt.Chart(source).encode( theta=alt.Theta("values:Q", stack=True), radius=alt.Radius( "values", scale=alt.Scale(type="sqrt", zero=True, rangeMin=20) ), color=alt.Color("category:N", legend=alt.Legend(title="Category")), ) c1 = base.mark_arc(innerRadius=20, stroke="#fff") c2 = base.mark_text(radiusOffset=10).encode(text="values:Q") chart = c1 + c2 return chart def kpi_chart() -> alt.Chart: """ This function returns a KPI chart with the total amount of annotators. Returns: An altair chart with the KPI chart. """ # Obtain the total amount of annotators _, target_dataset = obtain_source_target_datasets() user_ids_annotations = get_user_annotations_dictionary(target_dataset) total_annotators = len(user_ids_annotations) # Assuming you have a DataFrame with user data, create a sample DataFrame data = pd.DataFrame({"Category": ["Total Contributors"], "Value": [total_annotators]}) # Create Altair chart chart = ( alt.Chart(data) .mark_text(fontSize=100, align="center", baseline="middle", color="steelblue") .encode(text="Value:N") .properties(title="Number of Contributors", width=250, height=200) ) return chart def obtain_top_5_users(user_ids_annotations: Dict[str, int]) -> pd.DataFrame: """ This function returns the top 5 users with the most annotations. Args: user_ids_annotations: A dictionary with the user ids as the key and the number of annotations as the value. Returns: A pandas dataframe with the top 5 users with the most annotations. """ dataframe = pd.DataFrame( user_ids_annotations.items(), columns=["Name", "Submitted Responses"] ) dataframe = dataframe.sort_values(by="Submitted Responses", ascending=False) return dataframe.head(10) def main() -> None: # Connect to the space with rg.init() rg.init( api_url=os.getenv("ARGILLA_API_URL"), api_key=os.getenv("ARGILLA_API_KEY"), extra_headers={"Authorization": f"Bearer {os.getenv('HF_TOKEN')}"}, ) source_dataset, target_dataset = obtain_source_target_datasets() user_ids_annotations = get_user_annotations_dictionary(target_dataset) top5_dataframe = obtain_top_5_users(user_ids_annotations) with gr.Blocks() as demo: gr.Markdown( """ # 🗣️ The Prompt Collective Dashboad This Gradio dashboard shows the progress of the first "Data is Better Together" initiative to understand and collect good quality and diverse prompt for the OSS AI community. If you want to contribute to OSS AI, join [the Prompt Collective HF Space](https://huggingface.co/spaces/DIBT/prompt-collective). """ ) gr.Markdown( """ ## 🚀 Contributors Progress How many records have been submitted, how many are still pending? """ ) plot = gr.Plot(label="Plot") demo.load( progress_bar_chart, inputs=[], outputs=[plot], ) gr.Markdown( """ ## 👾 Contributors Hall of Fame The number of all contributors and the top 10 contributors: """ ) with gr.Row(): plot2 = gr.Plot(label="Plot") demo.load( kpi_chart, inputs=[], outputs=[plot2], ) gr.Dataframe( value=top5_dataframe, headers=["Name", "Submitted Responses"], datatype=[ "str", "number", ], row_count=10, col_count=(2, "fixed"), interactive=False, ), # Launch the Gradio interface demo.launch() if __name__ == "__main__": main()