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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+ecg_classification_model[[:space:]](1).keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,45 @@
+import tensorflow as tf
+import numpy as np
+import gradio as gr
+from tensorflow.keras.preprocessing import image
+
+# Load the trained model
+model = tf.keras.models.load_model("ecg_classification_model (1).keras", compile=False)
+
+# Class labels (modify based on your dataset)
+class_labels = [
+    "Left Bundle Branch Block",
+    "Normal",
+    "Premature Atrial Contraction",
+    "Premature Ventricular Contractions",
+    "Right Bundle Branch Block",
+    "Ventricular Fibrillation"
+]
+
+# Function to preprocess the image
+def preprocess_image(img):
+    img = img.resize((224, 224))  # Resize to match model input
+    img_array = np.array(img) / 255.0  # Normalize
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+    return img_array
+
+# Function to make a prediction
+def predict_ecg(img):
+    processed_img = preprocess_image(img)
+    prediction = model.predict(processed_img)
+    predicted_class = class_labels[np.argmax(prediction)]
+    return f"Predicted Class: {predicted_class}"
+
+# Create Gradio Interface
+iface = gr.Interface(
+    fn=predict_ecg,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="ECG Image Classifier",
+    description="Upload an ECG image to classify it."
+)
+
+# Run the app
+if __name__ == "__main__":
+    iface.launch(share=True)
+

ecg_classification_model (1).keras

@@ -0,0 +1,3 @@
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+oid sha256:5692d3354588302d19b6938bc03ed12533a783dd125c1545787b55f16bac45d5
+size 257015602