first demo version

app.py

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+import gradio as gr
+import numpy as np
+import datetime
+import pandas as pd
+
+import hopsworks
+
+def get_price():
+
+    project = hopsworks.login()
+    fs = project.get_feature_store()
+
+    price_pred_fg = fs.get_feature_group(
+        name="price_predictions",
+        version=1
+    ).read()
+    
+    dates = [
+        (datetime.datetime.now() + datetime.timedelta(days=i)).strftime("%Y-%m-%d")
+    for i in range(1, 8)]
+    days_ahead = list(range(1, 8))
+
+    prices = [price_pred_fg.loc[(price_pred_fg['date'] == dates[i]) & (price_pred_fg['days_ahead'] == days_ahead[i])]['predicted_price'].values[0] for i in range(0,7)]
+    price_predictions = pd.DataFrame()
+    price_predictions['date'] = dates
+    price_predictions['price'] = prices
+    # print(price_predictions)
+    return price_predictions
+
+demo = gr.Interface(
+    fn=get_price,
+    title="Energy Price Prediction",
+    description="Predicted daily average energy prices over the coming 7 days",
+    allow_flagging="never",
+    inputs=[],
+    outputs=gr.Timeseries(x="date", y=["price"]))
+
+demo.launch()
+

requirements.txt

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+hopsworks