set share to true

app.py

@@ -1,45 +1,51 @@
-import gradio as gr
-import numpy as np
 import datetime
-import pandas as pd
-import matplotlib.pyplot as plt
 
 import hopsworks
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+import gradio as gr
+
 
 def get_price():
 
     project = hopsworks.login()
     fs = project.get_feature_store()
 
-    price_pred_fg = fs.get_feature_group(
-        name="price_predictions",
-        version=1
-    ).read()
-    
+    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)]
+        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)]
+    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
+    price_predictions["date"] = dates
+    price_predictions["price"] = prices
 
     fig = plt.figure()
-    price_predictions.plot(kind='line', x='date', y='price')
+    price_predictions.plot(kind="line", x="date", y="price")
 
     # print(price_predictions)
     return fig
 
+
 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=['plot']
+    outputs=["plot"],
 )
 
-demo.launch()
-
+demo.launch(share=True)