added evaluation metrics

app.py

@@ -19,7 +19,6 @@ def preprocess(
     input_data,
     prediction_length,
     rolling_windows,
-    item_id,
     progress=gr.Progress(track_tqdm=True),
 ):
     df = pd.read_csv(input_data.name, index_col=0, parse_dates=True)
@@ -73,12 +72,11 @@ def train_and_forecast(
     forecast_it, ts_it = make_evaluation_predictions(
         dataset=test_data.input, predictor=predictor
     )
+    agg_metrics, _ = evaluator(ts_it, forecast_it)
 
     forecasts = list(predictor.predict(test_data.input))
 
-    agg_metrics, _ = evaluator(ts_it, forecast_it)
-
-    return plot_forecast(df, forecasts)
+    return plot_forecast(df, forecasts, agg_metrics)
 
 
 with gr.Blocks() as demo:
@@ -112,20 +110,20 @@ with gr.Blocks() as demo:
             epochs = gr.Number(value=10, label="Number of Epochs", precision=0)
 
     with gr.Row(label="Dataset"):
-        item_id = gr.Textbox(label="Item ID")
         upload_btn = gr.UploadButton(label="Upload")
         train_btn = gr.Button(label="Train and Forecast")
     plot = gr.Plot()
+    json = gr.JSON(label="Evaluation Metrics")
 
     upload_btn.upload(
         fn=preprocess,
-        inputs=[upload_btn, prediction_length, windows, item_id],
+        inputs=[upload_btn, prediction_length, windows],
         outputs=plot,
     )
     train_btn.click(
         fn=train_and_forecast,
         inputs=[upload_btn, prediction_length, windows, epochs],
-        outputs=plot,
+        outputs=[plot, json],
     )
 
 if __name__ == "__main__":