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| 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 = "Annotations" | |
| NUMBER_ANNOTATED = "Total Annotations" | |
| PENDING = "Pending" | |
| NUMBER_ANNOTATORS = "Number of annotators" | |
| NAME = "Username" | |
| NUMBER_ANNOTATIONS = "Number of annotations" | |
| 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": ["#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, delete_cache=(300, 300)) as demo: | |
| gr.Markdown( | |
| """ | |
| # 🌍 [YOUR LANGUAGE] - Multilingual Prompt Evaluation Project | |
| Hugging Face and @argilla are developing [Multilingual Prompt Evaluation Project](https://github.com/huggingface/data-is-better-together/tree/main/prompt_translation) project. It is an open multilingual benchmark for evaluating language models, and of course, also for [YOUR LANGUAGE]. | |
| ## The goal is to translate 500 Prompts | |
| And as always: data is needed for that! The community selected the best 500 prompts that will form the benchmark. In English, of course. | |
| **That's why we need your help**: if we all translate the 500 prompts, we can add [YOUR LANGUAGE] to the leaderboard. | |
| ## How to participate | |
| Participating is easy. Go to the [annotation space][add a link to your annotation dataset], log in or create a Hugging Face account, and you can start working. | |
| Thanks in advance! Oh, and we'll give you a little push: GPT4 has already prepared a translation suggestion for you. | |
| """ | |
| ) | |
| gr.Markdown( | |
| f""" | |
| ## 🚀 Current Progress | |
| This is what we've achieved so far! | |
| """ | |
| ) | |
| 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. | |
| """ | |
| ) | |
| 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() | |