Spaces:
Runtime error
Runtime error
| 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 | |
| # Translation of legends and titels | |
| ANNOTATED = "Anotaciones" | |
| NUMBER_ANNOTATED = "Anotaciones totales" | |
| PENDING = "Pendiente" | |
| NUMBER_ANNOTATORS = "Número de anotadores" | |
| NAME = "Nombre de usuario" | |
| NUMBER_ANNOTATIONS = "Número de anotaciones" | |
| CATEGORY = "Categoría" | |
| 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_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( | |
| """ | |
| # 🌍 Español - Multilingual Prompt Evaluation Project | |
| Hugging Face y Argilla han lanzado el proyecto [Multilingual Prompt Evaluation Project](https://github.com/huggingface/data-is-better-together/tree/main/prompt_translation). Es un benchmak multilingüe abierto para la evaluación de modelos de lenguaje, y por supuesto, también para el español. | |
| ## El objetivo es traducir 500 prompts | |
| Y como siempre: ¡se necesitan datos de calidad! La comunidad ha seleccionado los mejores 500 prompts que formarán el benchmark. En inglés, por supuesto. | |
| **Por eso necesitamos tu ayuda**: si entre todos traducimos los 500 prompts, podremos añadir el Español al leaderboard. | |
| ## Cómo participar | |
| Participar es sencillo. Ve al [Space de anotación](https://somosnlp-dibt-prompt-translation-for-es.hf.space/), inicia sesión o crea una cuenta de Hugging Face, y podrás empezar a trabajar. | |
| ¡Gracias de antemano! Ah, y te daremos un empujoncito: GPT4 ya ha preparado una sugerencia de traducción para ti. | |
| """ | |
| ) | |
| gr.Markdown( | |
| f""" | |
| ## 🚀 Progreso Actual | |
| ¡Esto es lo que hemos logrado hasta ahora! | |
| """ | |
| ) | |
| 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 de la Fama | |
| Aquí puedes ver los usuarios con más contribuciones: | |
| """ | |
| ) | |
| 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() | |