Upload 3 files

app.py

@@ -17,20 +17,28 @@ dataset_api = project.get_dataset_api()
 dataset_api.download("Resources/images/df_ny_elec_recent.png", overwrite=True)
 
 with gr.Blocks() as demo:
+    gr.Label("Today's prediction")
     with gr.Row():
         with gr.Column():
-            gr.Label("Today's predicted NY electricity demand")
-            gr.Label("{:.0f}MWh".format(latest_prediction['prediction']))
+            gr.Textbox(value="{}".format(latest_prediction['prediction_date']),
+                       label="Prediction date")
         with gr.Column():
-            gr.Label("Today's actual demand")
-            gr.Label("{}MWh".format(latest_prediction['actual']))
+            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():
         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.Label("MAE for historical predictions")
-            gr.Label("{:.0f}MWh".format(mean_error))
+            gr.Textbox(label="MAE for historical predictions",
+                       value="{:.0f}MWh".format(mean_error))
 
 
 demo.launch()