from apscheduler.schedulers.background import BackgroundScheduler import datetime 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 from huggingface_hub import restart_space import gradio as gr import pandas as pd """ This is the main file for the dashboard application. It contains the main function and the functions to obtain the data and create the charts. It's designed as a template to recreate the dashboard for the prompt translation project of any language. To create a new dashboard, you need several environment variables, that you can easily set in the HuggingFace Space that you are using to host the dashboard: - HF_TOKEN: Token with write access from your Hugging Face account: https://huggingface.co/settings/tokens - SOURCE_DATASET: The dataset id of the source dataset - SOURCE_WORKSPACE: The workspace id of the source dataset - TARGET_RECORDS: The number of records that you have as a target to annotate. We usually set this to 500. - ARGILLA_API_URL: Link to the Huggingface Space where the annotation effort is being hosted. For example, the Spanish one is https://somosnlp-dibt-prompt-translation-for-es.hf.space/ - ARGILLA_API_KEY: The API key to access the Huggingface Space. Please, write this as a secret in the Huggingface Space configuration. """ # Translation of legends and titles ANNOTATED = 'Annotationen' NUMBER_ANNOTATED = 'Annotationen Gesamt' PENDING = 'Ausstehend' NUMBER_ANNOTATORS = "Anzahl der Annotierenden" NAME = 'Nutzername' NUMBER_ANNOTATIONS = 'Anzahl der Annotationen' CATEGORY = "Category" def restart() -> None: """ This function restarts the space where the dashboard is hosted. """ # Update Space name with your Space information gr.Info("Restarting space at " + str(datetime.datetime.now())) restart_space( "ignacioct/TryingRestartDashboard", token=os.getenv("HF_TOKEN"), # factory_reboot=True, ) 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") # ) target_dataset = source_dataset.filter_by(response_status=["submitted"]) 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_total() -> alt.Chart: """ This function returns a donut chart with the progress of the total annotations. Counts each record that has been annotated at least once. Returns: An altair chart with the donut chart. """ # Load your data annotated_records = len(target_dataset) pending_records = int(os.getenv("TARGET_RECORDS")) - annotated_records # Prepare data for the donut chart source = pd.DataFrame( { "values": [annotated_records, pending_records], "category": [ANNOTATED, PENDING], "colors": [ "#4682b4", "#e68c39", ], # Blue for Completed, Orange for Remaining } ) domain = source["category"].tolist() range_ = source["colors"].tolist() 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( field="category", type="nominal", scale=alt.Scale(domain=domain, range=range_), legend=alt.Legend(title=CATEGORY), ), ) c1 = base.mark_arc(innerRadius=20, stroke="#fff") c2 = base.mark_text(radiusOffset=20).encode(text="values:Q") chart = c1 + c2 return chart def kpi_chart_remaining() -> alt.Chart: """ This function returns a KPI chart with the remaining amount of records to be annotated. Returns: An altair chart with the KPI chart. """ pending_records = int(os.getenv("TARGET_RECORDS")) - len(target_dataset) # Assuming you have a DataFrame with user data, create a sample DataFrame data = pd.DataFrame({"Category": [PENDING], "Value": [pending_records]}) # Create Altair chart chart = ( alt.Chart(data) .mark_text(fontSize=100, align="center", baseline="middle", color="#e68b39") .encode(text="Value:N") .properties(title=PENDING, width=250, height=200) ) return chart def kpi_chart_submitted() -> alt.Chart: """ This function returns a KPI chart with the total amount of records that have been annotated. Returns: An altair chart with the KPI chart. """ total = len(target_dataset) # Assuming you have a DataFrame with user data, create a sample DataFrame data = pd.DataFrame({"Category": [NUMBER_ANNOTATED], "Value": [total]}) # Create Altair chart chart = ( alt.Chart(data) .mark_text(fontSize=100, align="center", baseline="middle", color="steelblue") .encode(text="Value:N") .properties(title=NUMBER_ANNOTATED, width=250, height=200) ) return chart def kpi_chart_total_annotators() -> 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 total_annotators = len(user_ids_annotations) # Assuming you have a DataFrame with user data, create a sample DataFrame data = pd.DataFrame({"Category": [NUMBER_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_ANNOTATORS, width=250, height=200) ) return chart def render_hub_user_link(hub_id:str) -> str: """ This function returns a link to the user's profile on Hugging Face. Args: hub_id: The user's id on Hugging Face. Returns: A string with the link to the user's profile on Hugging Face. """ link = f"https://huggingface.co/{hub_id}" return f'{hub_id}' def obtain_top_users(user_ids_annotations: Dict[str, int], N: int = 50) -> pd.DataFrame: """ This function returns the top N 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 N users with the most annotations. """ dataframe = pd.DataFrame( user_ids_annotations.items(), columns=[NAME, NUMBER_ANNOTATIONS] ) dataframe[NAME] = dataframe[NAME].apply(render_hub_user_link) dataframe = dataframe.sort_values(by=NUMBER_ANNOTATIONS, ascending=False) return dataframe.head(N) def fetch_data() -> None: """ This function fetches the data from the source and target datasets and updates the global variables. """ print(f"Starting to fetch data: {datetime.datetime.now()}") global source_dataset, target_dataset, user_ids_annotations, annotated, remaining, percentage_completed, top_dataframe source_dataset, target_dataset = obtain_source_target_datasets() user_ids_annotations = get_user_annotations_dictionary(target_dataset) annotated = len(target_dataset) remaining = int(os.getenv("TARGET_RECORDS")) - annotated percentage_completed = round( (annotated / int(os.getenv("TARGET_RECORDS"))) * 100, 1 ) # Print the current date and time print(f"Data fetched: {datetime.datetime.now()}") def get_top(N = 50) -> pd.DataFrame: """ This function returns the top N users with the most annotations. Args: N: The number of users to be returned. 50 by default Returns: A pandas dataframe with the top N users with the most annotations. """ return obtain_top_users(user_ids_annotations, N=N) 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"), ) # Fetch the data initially fetch_data() # To avoid the orange border for the Gradio elements that are in constant loading css = """ .generating { border: none; } """ with gr.Blocks(css=css, delete_cache=(300, 300)) as demo: gr.Markdown( """ # 🌍 Deutsch - Multilingual Prompt Evaluation Project Hugging Face und @argilla haben das [Multilingual Prompt Evaluation Project](https://github.com/huggingface/data-is-better-together/tree/main/prompt_translation) ins Leben gerufen. Es handelt sich dabei um ein offenes, mehrsprachiges Benchmark zur Evaluation von Sprachmodellen, natürlich auch für Deutsch. ## Ziel ist es, 500 Prompts zu übersetzen Und wie immer gilt: dafür werden Daten benötigt! Die Community hat die besten 500 Prompts ausgewählt, die den Benchmark bilden werden. Auf Englisch, natürlich. **Deshalb brauchen wir Deine Unterstützung**: wenn wir alle 500 Prompts übersetzen, können wir Deutsch in das Leaderboard aufnehmen. ## Wie Du mitmachen kannst Mitmachen ist ganz einfach. Gehe zum [Annotationsspace](https://dibt-german-prompt-translation-for-german.hf.space), logge Dich ein oder erstelle einen Hugging Face Account, und lege los. Vielen Dank im Voraus! Oh, und wir geben Dir ein bisschen Starthilfe: GPT4 hat bereits Übersetzungsvorschläge für Dich vorbereitet, die Du nur noch validieren oder anpassen musst. """ ) gr.Markdown( f""" ## 🚀 Aktueller Fortschritt Das haben wir bisher erreicht! """ ) with gr.Row(): kpi_submitted_plot = gr.Plot(label="Plot") demo.load( kpi_chart_submitted, inputs=[], outputs=[kpi_submitted_plot], ) kpi_remaining_plot = gr.Plot(label="Plot") demo.load( kpi_chart_remaining, inputs=[], outputs=[kpi_remaining_plot], ) donut_total_plot = gr.Plot(label="Plot") demo.load( donut_chart_total, inputs=[], outputs=[donut_total_plot], ) gr.Markdown( """ ## 👾 Hall of Fame Hier kannst Du sehen, wer bisher die meisten Annotationen beigetragen hat. """ ) with gr.Row(): kpi_hall_plot = gr.Plot(label="Plot") demo.load( kpi_chart_total_annotators, inputs=[], outputs=[kpi_hall_plot] ) top_df_plot = gr.Dataframe( headers=[NAME, NUMBER_ANNOTATIONS], datatype=[ "markdown", "number", ], row_count=50, col_count=(2, "fixed"), interactive=False, ) demo.load(get_top, None, [top_df_plot]) # Manage background refresh scheduler = BackgroundScheduler() _ = scheduler.add_job(restart, "interval", minutes=30) scheduler.start() # Launch the Gradio interface demo.launch() if __name__ == "__main__": main()