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.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ ecg_classification_model[[:space:]](1).keras filter=lfs diff=lfs merge=lfs -text
app.py ADDED
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+ import tensorflow as tf
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+ import numpy as np
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+ import gradio as gr
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+ from tensorflow.keras.preprocessing import image
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+
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+ # Load the trained model
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+ model = tf.keras.models.load_model("ecg_classification_model (1).keras", compile=False)
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+
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+ # Class labels (modify based on your dataset)
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+ class_labels = [
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+ "Left Bundle Branch Block",
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+ "Normal",
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+ "Premature Atrial Contraction",
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+ "Premature Ventricular Contractions",
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+ "Right Bundle Branch Block",
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+ "Ventricular Fibrillation"
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+ ]
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+
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+ # Function to preprocess the image
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+ def preprocess_image(img):
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+ img = img.resize((224, 224)) # Resize to match model input
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+ img_array = np.array(img) / 255.0 # Normalize
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+ img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
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+ return img_array
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+
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+ # Function to make a prediction
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+ def predict_ecg(img):
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+ processed_img = preprocess_image(img)
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+ prediction = model.predict(processed_img)
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+ predicted_class = class_labels[np.argmax(prediction)]
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+ return f"Predicted Class: {predicted_class}"
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+
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+ # Create Gradio Interface
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+ iface = gr.Interface(
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+ fn=predict_ecg,
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+ inputs=gr.Image(type="pil"),
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+ outputs="text",
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+ title="ECG Image Classifier",
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+ description="Upload an ECG image to classify it."
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+ )
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+
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+ # Run the app
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+ if __name__ == "__main__":
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+ iface.launch(share=True)
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+
ecg_classification_model (1).keras ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:5692d3354588302d19b6938bc03ed12533a783dd125c1545787b55f16bac45d5
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+ size 257015602
requirements.txt ADDED
Binary file (74 Bytes). View file