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 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, ) 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()