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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
from make_plot import plot_forecast, plot_train_test
def offset_calculation(prediction_length, rolling_windows, length):
row_offset = -1 * prediction_length * rolling_windows
if abs(row_offset) > 0.95 * length:
raise gr.Error("Reduce prediction_length * rolling_windows")
return row_offset
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)
df.sort_index(inplace=True)
row_offset = offset_calculation(prediction_length, rolling_windows, len(df))
return plot_train_test(df.iloc[:row_offset], df.iloc[row_offset:])
def train_and_forecast(
input_data,
prediction_length,
rolling_windows,
epochs,
progress=gr.Progress(track_tqdm=True),
):
if not input_data:
raise gr.Error("Upload a file with the Upload button")
try:
df = pd.read_csv(input_data.name, index_col=0, parse_dates=True)
df.sort_index(inplace=True)
except AttributeError:
raise gr.Error("Upload a file with the Upload button")
row_offset = offset_calculation(prediction_length, rolling_windows, len(df))
try:
gluon_df = PandasDataset(df, target=df.columns[0])
except TypeError:
freq = pd.infer_freq(df.index[:3])
gluon_df = PandasDataset(df, target=df.columns[0], freq=freq)
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,
)
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)
with gr.Blocks() as demo:
gr.Markdown("""
# How to use
Upload a univariate csv with the first column showing your dates and the second column having your data
# How it works
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.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():
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)
if __name__ == "__main__":
demo.queue().launch()
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