Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+
+# Function to process the CSV file
+def process_csv():
+    df = pd.read_csv("mitbih_train.csv", header=None)
+    M = df.values
+    X = M[:, :-1]
+    y = M[:, -1].astype(int)
+    
+    C0 = np.argwhere(y == 0).flatten()
+    C1 = np.argwhere(y == 1).flatten()
+    C2 = np.argwhere(y == 2).flatten()
+    C3 = np.argwhere(y == 3).flatten()
+    C4 = np.argwhere(y == 4).flatten()
+    
+    # Select sample indices
+    sample_data = {
+        "Cat_N": X[C0[0], :].tolist(),
+        "Cat_S": X[C1[0], :].tolist(),
+        "Cat_V": X[C2[0], :].tolist(),
+        "Cat_F": X[C3[0], :].tolist(),
+        "Cat_Q": X[C4[0], :].tolist(),
+        "time": (np.arange(0, 187) * 8 / 1000).tolist()  # time axis
+    }
+    
+    return sample_data
+
+# Gradio Interface for visualizing ECG data
+def get_ecg_data():
+    return process_csv()
+
+# Set up Gradio Interface
+iface = gr.Interface(
+    fn=get_ecg_data,
+    inputs=[],
+    outputs="json",
+    live=False
+)
+
+if __name__ == "__main__":
+    iface.launch(share=True)