app.py

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

# ترجمة الأساطير والعناوين
ANNOTATED = "التعليقات المُضافة"
NUMBER_ANNOTATED = "إجمالي التعليقات المُضافة"
PENDING = "قيد الانتظار"

NUMBER_ANNOTATORS = "عدد المُعلقين"
NAME = "اسم المستخدم"
NUMBER_ANNOTATIONS = "عدد التعليقات"

CATEGORY = "الفئة"


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(
        "DIBT Translation for Arabic", 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_data()

    # To avoid the orange border for the Gradio elements that are in constant loading
    css = """
    .generating {
        border: none;
    }
    img {
    width: auto; /* or you can set it to a specific width like 200px */
    height: auto; /* adjust the height as needed */
    }
    """

    with gr.Blocks(css=css) as demo:
        gr.Markdown(
            """
           ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64d5698102e58cc1fdd0b585/Fo9_191B1KBcHj3fswMey.png)

            # 🌍 العربية - مشروع تقييم المطالبات متعدد اللغات

            أطلقت Hugging Face و Argilla مشروع [مشروع تقييم المطالبات متعدد اللغات](https://github.com/huggingface/data-is-better-together/tree/main/prompt_translation). إنه معيار متعدد اللغات مفتوح لتقييم نماذج اللغة، وبالطبع، للغة العربية أيضًا.

            ## 💡الهدف هو ترجمة 500 مطالبة
            وكالعادة: البيانات عالية الجودة مطلوبة! لقداختار المجتمع أفضل 500 مطالبة التي ستشكل المعيار. باللغة الإنجليزية، بالطبع.
            **لذلك نحتاج إلى مساعدتك**: إذا قمنا جميعًا بترجمة الـ 500 مطالبة، يمكننا إضافة العربية إلى قائمة المتصدرين.


            ## 📌كيفية المشاركة
            
             المشاركة سهلة. اذهب إلى [فضاء التعليق]
             (https://huggingface.co/spaces/2A2I/prompt-translation-for-Arabic)، 
             ويمكنك البدء في العمل
             قم بتسجيل الدخول 
             Huggine Face أو إنشاء حساب على ، 
            
            🤗!شكرًا لك مقدمًا 
            
            

            """
        )
        gr.Image(value="mpep-ar.png", interactive=False)

        gr.Markdown(
            f"""
            ## 🚀 التقدم الحالي
            وهذا ما حققناه حتى الآن!
            """
        )
        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(
            """
            ## 👾 قاعة المشاهير
            : هنا يمكنك رؤية المستخدمين الذين لديهم أكبر عدد من المساهمات
            """
        )

        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()