added app

app.py

@@ -0,0 +1,96 @@
+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.HTML(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.HTML(article)
+
+demo.launch(enable_queue=True)