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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)
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