added forecaster

app.py

@@ -1,19 +1,40 @@
 import gradio as gr
+import pandas as pd
+from gluonts.dataset.pandas import PandasDataset
+from gluonts.dataset.split import split
+from gluonts.torch.model.deepar import DeepAREstimator
+import matplotlib.pyplot as plt
 
 
-def forecast(data):
-    return data
+def fn(upload_data):
+    df = pd.read_csv(upload_data.name, index_col=0, parse_dates=True)
+    dataset = PandasDataset(df, target=df.columns[0])
+    training_data, test_gen = split(dataset, offset=-36)
 
+    model = DeepAREstimator(
+        prediction_length=12,
+        freq=dataset.freq,
+        trainer_kwargs=dict(max_epochs=1),
+    ).train(
+        training_data=training_data,
+    )
 
-demo = gr.Interface(
-    forecast,
-    [
-        gr.Timeseries(),
-    ],
-    [
-        gr.Timeseries(),
-    ],
-)
+    test_data = test_gen.generate_instances(prediction_length=12, windows=3)
+    forecasts = list(model.predict(test_data.input))
+
+    fig = plt.figure()
+    df["#Passengers"].plot(color="black")
+    for forecast, color in zip(forecasts, ["green", "blue", "purple"]):
+        forecast.plot(color=f"tab:{color}")
+    plt.legend(["True values"], loc="upper left", fontsize="xx-large")
+    return fig
+
+
+with gr.Blocks() as demo:
+    plot = gr.Plot()
+    upload_btn = gr.UploadButton()
+
+    upload_btn.upload(fn, inputs=upload_btn, outputs=plot)
 
 if __name__ == "__main__":
     demo.launch()

requirements.txt

@@ -1,3 +1,3 @@
 gluonts[torch,pro]
 pandas
-
+matplotlib