app.py

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
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:

- 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 = 'Kategorie'

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": ["#4CAF50", "#757575"],  # Green for Completed, Grey for Remaining
        }
    )

    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=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'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{hub_id}</a>'


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) 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])

    # Launch the Gradio interface
    demo.launch()


if __name__ == "__main__":
    main()