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 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": ["Annotated", "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 Annotators"], "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 Annotators", 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", "Annotated Records"] ) dataframe = dataframe.sort_values(by="Annotated Records", ascending=False) return dataframe.head(5) 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( donut_chart, inputs=[], outputs=[plot], ) gr.Markdown( """ ## 👾 Contributors Hall of Fame The number of all annotators and the top 5 users with the most responses are: """ ) with gr.Row(): plot2 = gr.Plot(label="Plot") demo.load( kpi_chart, inputs=[], outputs=[plot2], ) gr.Dataframe( value=top5_dataframe, headers=["Name", "Annotated Records"], datatype=[ "str", "number", ], row_count=5, col_count=(2, "fixed"), interactive=False, ), # Launch the Gradio interface demo.launch() if __name__ == "__main__": main()