Upload 2 files

app.py

@@ -1,44 +1,21 @@
 import gradio as gr
 import hopsworks
-from sklearn.metrics import mean_absolute_error
 
 project = hopsworks.login()
-fs = project.get_feature_store()
-
-monitor_fg = fs.get_feature_group(name="ny_elec_predictions", version=1)
-history_df = monitor_fg.read()
-latest_prediction = history_df.iloc[-1]
-
-y_pred = history_df['prediction']
-y_test = history_df['actual']
-mean_error = mean_absolute_error(y_test, y_pred)
 
 dataset_api = project.get_dataset_api()
 dataset_api.download("Resources/images/df_ny_elec_recent.png", overwrite=True)
+dataset_api.download("Resources/images/df_ny_elec_prediction.png", overwrite=True)
 
 with gr.Blocks() as demo:
-    gr.Label("Today's prediction")
-    with gr.Row():
-        with gr.Column():
-            gr.Textbox(value="{}".format(latest_prediction['prediction_date']),
-                       label="Prediction date")
-        with gr.Column():
-            gr.Textbox(value="{:.0f}MWh".format(latest_prediction['prediction']),
-                       label="Predicted NY electricity demand")
     with gr.Row():
         with gr.Column():
-            gr.Textbox(value="{}MWh".format(latest_prediction['actual']),
-                       label="Actual demand")
-        with gr.Column():
-            gr.Textbox(value="{}MWh".format(latest_prediction['forecast_eia']),
-                       label="EIA forecast")
-    gr.Label("Recent Prediction History")
-    with gr.Row():
+            gr.Label("Today's prediction")
+            input_img = gr.Image("df_ny_elec_prediction.png", elem_id="latest-prediction")
         with gr.Column():
+            gr.Label("Recent Prediction History")
             input_img = gr.Image("df_ny_elec_recent.png", elem_id="recent-predictions")
-        with gr.Column():
-            gr.Textbox(label="MAE for historical predictions",
-                       value="{:.0f}MWh".format(mean_error))
+            # TODO: MAE plot
 
 
 demo.launch()

requirements.txt

@@ -1,2 +1 @@
-hopsworks
-scikit-learn
+hopsworks