implement dashboard changes from data-is-better-together repo

app.py

@@ -8,6 +8,7 @@ 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
 
@@ -17,6 +18,7 @@ It's designed as a template to recreate the dashboard for the prompt translation
 
 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.
@@ -33,7 +35,21 @@ NUMBER_ANNOTATORS = "Anzahl der Annotierenden"
 NAME = 'Nutzername'
 NUMBER_ANNOTATIONS = 'Anzahl der Annotationen'
 
-CATEGORY = 'Kategorie'
+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[
@@ -108,16 +124,27 @@ def donut_chart_total() -> alt.Chart:
         {
             "values": [annotated_records, pending_records],
             "category": [ANNOTATED, PENDING],
-            "colors": ["#4CAF50", "#757575"],  # Green for Completed, Grey for Remaining
+            "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("category:N", legend=alt.Legend(title=CATEGORY)),
+        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")
@@ -186,9 +213,7 @@ def kpi_chart_total_annotators() -> alt.Chart:
     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]}
-    )
+    data = pd.DataFrame({"Category": [NUMBER_ANNOTATORS], "Value": [total_annotators]})
 
     # Create Altair chart
     chart = (
@@ -287,7 +312,7 @@ def main() -> None:
     }
     """
 
-    with gr.Blocks(css=css) as demo:
+    with gr.Blocks(css=css, delete_cache=(300, 300)) as demo:
         gr.Markdown(
             """
             # 🌍 Deutsch - Multilingual Prompt Evaluation Project
@@ -359,6 +384,11 @@ def main() -> None:
             )
             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()