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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
import gradio as gr
import pandas as pd
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": ["Completed", "Remaining"],
"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 donut_chart_target() -> alt.Chart:
"""
This function returns a donut chart with the progress of the total annotations, in terms of the v1 objective.
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_ANNOTATIONS_V1")) - annotated_records
# Prepare data for the donut chart
source = pd.DataFrame(
{
"values": [annotated_records, pending_records],
"category": ["Completed", "Remaining"],
"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": ["Total remaining"], "Value": [pending_records]})
# Create Altair chart
chart = (
alt.Chart(data)
.mark_text(fontSize=100, align="center", baseline="middle", color="steelblue")
.encode(text="Value:N")
.properties(title="Total remaining", 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": ["Total completed"], "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="Total completed", width=250, height=200)
)
return chart
def kpi_chart() -> 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": ["Total Contributors"], "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 of Contributors", width=250, height=200)
)
return chart
def render_hub_user_link(hub_id):
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_5_users(user_ids_annotations: Dict[str, int]) -> pd.DataFrame:
"""
This function returns the top 5 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 5 users with the most annotations.
"""
dataframe = pd.DataFrame(
user_ids_annotations.items(), columns=["Name", "Submitted Responses"]
)
dataframe["Name"] = dataframe["Name"].apply(render_hub_user_link)
dataframe = dataframe.sort_values(by="Submitted Responses", ascending=False)
return dataframe.head(50)
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, top5_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_top5() -> pd.DataFrame:
return obtain_top_5_users(user_ids_annotations)
def main() -> None:
# Set the update interval
update_interval = 300 # seconds
update_interval_charts = 30 # seconds
# Connect to the space with rg.init()
rg.init(
api_url=os.getenv("ARGILLA_API_URL"),
api_key=os.getenv("ARGILLA_API_KEY"),
extra_headers={"Authorization": f"Bearer {os.getenv('HF_TOKEN')}"},
)
fetch_data()
scheduler = BackgroundScheduler()
scheduler.add_job(
func=fetch_data, trigger="interval", seconds=update_interval, max_instances=1
)
scheduler.start()
# 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(
"""
# π£οΈ The Prompt Collective Dashboad
This Gradio dashboard shows the progress of the first "Data is Better Together" initiative to understand and collect good quality and diverse prompt for the OSS AI community.
If you want to contribute to OSS AI, join [the Prompt Collective HF Space](https://huggingface.co/spaces/DIBT/prompt-collective).
"""
)
gr.Markdown(
f"""
## π Target for Releasing Dataset v2
How close are we to the target for version 2.0?
"""
)
with gr.Row():
donut_target_plot = gr.Plot(label="Plot")
demo.load(
donut_chart_target,
inputs=[],
outputs=[donut_target_plot],
every=update_interval_charts,
)
gr.Markdown(
f"""
## π Target for Releasing Dataset v1
Done! Thanks to the awesome DIBT community we've surpassed 10K rated prompts. Open Dataset coming soon!
"""
)
gr.Markdown(
f"""
## π Global Progress
Here's what the community has achieved so far!
"""
)
with gr.Row():
kpi_submitted_plot = gr.Plot(label="Plot")
demo.load(
kpi_chart_submitted,
inputs=[],
outputs=[kpi_submitted_plot],
every=update_interval_charts,
)
kpi_remaining_plot = gr.Plot(label="Plot")
demo.load(
kpi_chart_remaining,
inputs=[],
outputs=[kpi_remaining_plot],
every=update_interval_charts,
)
donut_total_plot = gr.Plot(label="Plot")
demo.load(
donut_chart_total,
inputs=[],
outputs=[donut_total_plot],
every=update_interval_charts,
)
gr.Markdown(
"""
## πΎ Contributors Hall of Fame
The number of all contributors and the top contributors:
"""
)
with gr.Row():
kpi_hall_plot = gr.Plot(label="Plot")
demo.load(
kpi_chart, inputs=[], outputs=[kpi_hall_plot], every=update_interval_charts
)
top5_df_plot = gr.Dataframe(
headers=["Name", "Submitted Responses"],
datatype=[
"markdown",
"number",
],
row_count=50,
col_count=(2, "fixed"),
interactive=False,
every=update_interval,
)
demo.load(get_top5, None, [top5_df_plot], every=update_interval_charts)
# Launch the Gradio interface
demo.launch()
if __name__ == "__main__":
main()
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