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| from functools import partial | |
| from typing import Dict | |
| import gradio as gr | |
| import numpy as np | |
| import plotly.graph_objects as go | |
| from huggingface_hub import from_pretrained_keras | |
| ROOT_DATA_URL = "https://raw.githubusercontent.com/hfawaz/cd-diagram/master/FordA" | |
| TRAIN_DATA_URL = f"{ROOT_DATA_URL}/FordA_TRAIN.tsv" | |
| TEST_DATA_URL = f"{ROOT_DATA_URL}/FordA_TEST.tsv" | |
| TIMESERIES_LEN = 500 | |
| CLASSES = {"Symptom does NOT exist", "Symptom exists"} | |
| model = from_pretrained_keras("EdoAbati/timeseries-classification-from-scratch") | |
| # Read data | |
| def read_data(file_url: str): | |
| data = np.loadtxt(file_url, delimiter="\t") | |
| y = data[:, 0] | |
| x = data[:, 1:] | |
| return x, y.astype(int) | |
| x_train, y_train = read_data(file_url=TRAIN_DATA_URL) | |
| x_test, y_test = read_data(file_url=TEST_DATA_URL) | |
| # Helper functions | |
| def get_prediction(row_index: int, data: np.ndarray) -> Dict[str, float]: | |
| x = data[row_index].reshape((1, TIMESERIES_LEN, 1)) | |
| predictions = model.predict(x).flatten() | |
| return {k: float(v) for k, v in zip(CLASSES, predictions)} | |
| def create_plot(row_index: int, dataset_name: str) -> go.Figure: | |
| x = x_train | |
| row = x[row_index] | |
| scatter = go.Scatter( | |
| x=list(range(TIMESERIES_LEN)), | |
| y=row.flatten(), | |
| mode="lines+markers", | |
| ) | |
| fig = go.Figure(data=scatter) | |
| fig.update_layout(title=f"Timeseries in row {row_index} of {dataset_name} set ") | |
| return fig | |
| def show_tab_section(data: np.ndarray, dataset_name: str): | |
| num_indexes = data.shape[0] | |
| index = gr.Slider(maximum=num_indexes - 1) | |
| button = gr.Button("Predict") | |
| plot = gr.Plot() | |
| create_plot_data = partial(create_plot, dataset_name=dataset_name) | |
| button.click(create_plot_data, inputs=[index], outputs=[plot]) | |
| get_prediction_data = partial(get_prediction, data=data) | |
| label = gr.Label() | |
| button.click(get_prediction_data, inputs=[index], outputs=[label]) | |
| # Gradio Demo | |
| title = "# Timeseries classification from scratch" | |
| description = """ | |
| Select a time series in the Training or Test dataset and ask the model to classify it! | |
| <br /> | |
| <br /> | |
| The model was trained on the <a href="http://www.j-wichard.de/publications/FordPaper.pdf" target="_blank">FordA dataset</a>. Each row is a diagnostic session run on an automotive subsystem. In each session 500 samples were collected. Given a time series, the model was trained to identify if a specific symptom exists or it does not exist. | |
| <br /> | |
| <br /> | |
| <p> | |
| <b>Model:</b> <a href="https://huggingface.co/keras-io/timeseries-classification-from-scratch" target="_blank">https://huggingface.co/keras-io/timeseries-classification-from-scratch</a> | |
| <br /> | |
| <b>Keras Example:</b> <a href="https://keras.io/examples/timeseries/timeseries_classification_from_scratch/" target="_blank">https://keras.io/examples/timeseries/timeseries_classification_from_scratch/</a> | |
| </p> | |
| <br /> | |
| """ | |
| article = """ | |
| <div style="text-align: center;"> | |
| Space by <a href="https://github.com/EdAbati" target="_blank">Edoardo Abati</a> | |
| <br /> | |
| Keras example by <a href="https://github.com/hfawaz/" target="_blank">hfawaz</a> | |
| </div> | |
| """ | |
| demo = gr.Blocks() | |
| with demo: | |
| gr.Markdown(title) | |
| gr.Markdown(description) | |
| with gr.Tabs(): | |
| with gr.TabItem("Training set"): | |
| show_tab_section(data=x_train, dataset_name="Training") | |
| with gr.TabItem("Test set"): | |
| show_tab_section(data=x_test, dataset_name="Test") | |
| gr.Markdown(article) | |
| demo.launch(enable_queue=True) | |