Create app.py

app.py

@@ -0,0 +1,73 @@
+import gradio as gr
+import numpy as np
+import tensorflow as tf
+from scipy.signal import find_peaks
+
+model = tf.keras.models.load_model('aritmi_distilled.h5')
+
+def extract_features(ekg_signal):
+    if len(ekg_signal) < 10:
+        return None
+
+    r_peaks, _ = find_peaks(ekg_signal, height=0.5, distance=60)
+
+    if len(r_peaks) < 2:
+        return None
+
+    features = []
+    rr_intervals = np.diff(r_peaks)
+
+    if len(rr_intervals) == 0:
+        return None
+
+    features.append(np.nanmean(rr_intervals))
+    features.append(np.nanstd(rr_intervals))
+    features.append(np.nanmean(ekg_signal))
+    features.append(np.nanmean(r_peaks))
+    features.append(np.nanmean(np.diff(r_peaks)))
+
+    pq_interval = np.nanmean(rr_intervals)
+    qt_interval = np.nanmean(rr_intervals)
+    st_interval = np.nanmean(rr_intervals)
+
+    features.extend([pq_interval, qt_interval, st_interval])
+    return features
+
+def predict(ekg_signal):
+    try:
+        ekg_signal_list = [float(value.strip()) for value in ekg_signal.split(',') if value.strip()]
+        ekg_signal_array = np.array(ekg_signal_list, dtype=np.float32)
+    except Exception as e:
+        return f"Invalid dtype. Error: {str(e)}"
+
+    if ekg_signal_array.size == 0:
+        return "Empty ECG signal."
+
+    features = extract_features(ekg_signal_array)
+    if features is None:
+        return "Couldn't extract feature."
+
+    features = np.array(features).reshape(1, -1)
+    prediction = model.predict(features)
+    risk_probabilities = prediction[0]
+    aritmi_riski_yuzde = risk_probabilities[1] * 100
+
+    return f"Arrhythmia risk: %{aritmi_riski_yuzde:.2f}"
+
+
+
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(
+        label="ECG Signal",
+        placeholder="0.034, 0.181, 0.264, ...",
+    ),
+    outputs="text",
+    description="Arrhythmia risk predicting model from ECG data",
+    live=False
+)
+
+
+
+if __name__ == "__main__":
+    interface.launch()