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 = 'Annotations'
NUMBER_ANNOTATED = 'Total Annotations'
PENDING = 'Pending'

NUMBER_ANNOTATORS = "Number of annotators"
NAME = 'Username'
NUMBER_ANNOTATIONS = 'Number of annotations'

CATEGORY = 'Category'

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'<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;
    }
    """

    COUNTRY_FLAGS = {
    "Tanzania": "🇹🇿",
    "Kenya": "🇰🇪",
    "Democratic Republic of the Congo": "🇨🇩",
    "Uganda": "🇺🇬",
    "Rwanda": "🇷🇼",
    "Burundi": "🇧🇮",
    "Mozambique": "🇲🇿",
    "Somalia": "🇸🇴",
    "Comoros": "🇰🇲",
    }

    # Create a string that contains just the flag emojis
    flags_string = " ".join(COUNTRY_FLAGS.values())

    # Add the world emoji at the beginning
    full_string = f"🌍 {flags_string}"

    with gr.Blocks(css=css) as demo:
        gr.Markdown(
            f"""
            # {full_string} Swahili - Multilingual Prompt Evaluation Project
            Hugging Face na @argilla wanatengeneza mradi wa Multilingual Prompt Evaluation Project (https://github.com/huggingface/data-is-better-together/tree/main/prompt_translation). Hii ni kipimo cha lugha nyingi kilichofunguliwa kwa ajili ya kutathmini mifano ya lugha, na hivyo kwa Kiswahili.


            ## Lengo ni kutafsiri Maombi (Prompts) 500
            Na kama ilivyo kawaida: data inahitajika kwa hilo! Jamii ilichagua maombi bora 500 ambayo yametengenza benchmark. Maombi hayo yapo kwa lugha ya Kiingereza.
            **Ndio maana tunahitaji msaada wako**: ikiwa sisi sote tutatafsiri maombi 500, tunaweza kuongeza **Kiswahili** kwenye orodha ya lugha zilizotafsiriwa kwa ufasaha.

            ## Jinsi ya kushiriki (Swahili)
            Kushiriki ni rahisi. Nenda kwenye (https://huggingface.co/spaces/DIBT-Swahili/prompt-translation-for-Swahili), ingia au unda akaunti ya Hugging Face, na unaweza kuanza kufanya kazi.
            Shukrani za mapema! Tumerahisisha ufanyaji kazi kwa kutasfiri kwa kutumia system mbalimbali ambazo hazina uhakika mkubwa wa kuwa na majibu sahihi.
            

            ## How to participate
            Participating is easy. Go to the (https://huggingface.co/spaces/DIBT-Swahili/prompt-translation-for-Swahili), log in or create a Hugging Face account, and you can start working.
            Thanks in advance! Oh, and here's a little nudge: everything has been translated using various models, however, the translations may not be accurate.
            """
        )

        gr.Markdown(
            f"""
            ## 🚀 Maendeleo ya Sasa
            Hapa ndiyo tumefikia mpaka sasa!
            """
        )
        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
        #     Here you can see the top contributors and the number of annotations they have made.
        #     """
        # )

        gr.Markdown(
            """
            ## 👾 Ukumbi wa Umaarufu
            Hapa unaweza kuona wachangiaji bora na idadi ya maoni waliyotoa.
            """
        )

        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()