added initial evaluation

app.py

@@ -3,6 +3,7 @@ import pandas as pd
 from gluonts.dataset.pandas import PandasDataset
 from gluonts.dataset.split import split
 from gluonts.torch.model.deepar import DeepAREstimator
+from gluonts.evaluation import Evaluator, make_evaluation_predictions
 
 from make_plot import plot_forecast, plot_train_test
 
@@ -54,16 +55,29 @@ def train_and_forecast(
 
     training_data, test_gen = split(gluon_df, offset=row_offset)
 
-    model = DeepAREstimator(
-            prediction_length=prediction_length,
-            freq=gluon_df.freq,
-            trainer_kwargs=dict(max_epochs=epochs),
-            ).train(
-            training_data=training_data,
-            )
+    estimator = DeepAREstimator(
+        prediction_length=prediction_length,
+        freq=gluon_df.freq,
+        trainer_kwargs=dict(max_epochs=epochs),
+    )
+
+    predictor = estimator.train(
+        training_data=training_data,
+    )
+
+    test_data = test_gen.generate_instances(
+        prediction_length=prediction_length, windows=rolling_windows
+    )
+
+    evaluator = Evaluator(num_workers=0)
+    forecast_it, ts_it = make_evaluation_predictions(
+        dataset=test_data.input, predictor=predictor
+    )
+
+    forecasts = list(predictor.predict(test_data.input))
+
+    agg_metrics, _ = evaluator(ts_it, forecast_it)
 
-    test_data = test_gen.generate_instances(prediction_length=prediction_length, windows=rolling_windows)
-    forecasts = list(model.predict(test_data.input))
     return plot_forecast(df, forecasts)
 
 
@@ -87,19 +101,32 @@ with gr.Blocks() as demo:
     1. Click **Upload** to upload your data
     2. Click **Run**
         - This app will visualize your data and then train an estimator and show its predictions 
-    """)
-    with gr.Accordion(label='Hyperparameters'):
+    """
+    )
+    with gr.Accordion(label="Hyperparameters"):
         with gr.Row():
-            prediction_length = gr.Number(value=12, label='Prediction Length', precision=0)
-            windows = gr.Number(value=3, label='Number of Windows', precision=0)
-            epochs = gr.Number(value=10, label='Number of Epochs', precision=0)
-    with gr.Row():
+            prediction_length = gr.Number(
+                value=12, label="Prediction Length", precision=0
+            )
+            windows = gr.Number(value=3, label="Number of Windows", precision=0)
+            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()
 
-    upload_btn.upload(fn=preprocess, inputs=[upload_btn, prediction_length, windows], outputs=plot)
-    train_btn.click(fn=train_and_forecast, inputs=[upload_btn, prediction_length, windows, epochs], outputs=plot)
+    upload_btn.upload(
+        fn=preprocess,
+        inputs=[upload_btn, prediction_length, windows, item_id],
+        outputs=plot,
+    )
+    train_btn.click(
+        fn=train_and_forecast,
+        inputs=[upload_btn, prediction_length, windows, epochs],
+        outputs=plot,
+    )
 
 if __name__ == "__main__":
     demo.queue().launch()