Update app.py

app.py

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+import gradio as gr
+import numpy as np
+import cv2
+from tensorflow.keras.models import load_model
+
+# Custom object scope to handle InputLayer configuration
+from tensorflow.keras.layers import InputLayer
+custom_objects = {'InputLayer': InputLayer}
+
+# Load the model
+def load_ocular_model():
+    return load_model('odir_cnn_model.h5', custom_objects=custom_objects)
+
+model = load_ocular_model()
+
+# Define the labels
+LABELS = ['Normal (N)', 'Diabetes (D)', 'Glaucoma (G)', 'Cataract (C)', 
+          'Age related Macular Degeneration (A)', 'Hypertension (H)', 
+          'Pathological Myopia (M)', 'Other diseases/abnormalities (O)']
+
+# Preprocess the image
+def preprocess_image(image, img_size=128):  
+    img = cv2.resize(image, (img_size, img_size))
+    img = img / 255.0  # Normalize
+    img = np.expand_dims(img, axis=0)  # Add batch dimension
+    return img
+
+# Prediction function
+def predict_diseases(left_image, right_image):
+    left_img = preprocess_image(left_image)
+    right_img = preprocess_image(right_image)
+    
+    left_predictions = model.predict(left_img)
+    right_predictions = model.predict(right_img)
+    
+    combined_predictions = (left_predictions + right_predictions) / 2
+    
+    pred_labels = {label: float(pred) for label, pred in zip(LABELS, combined_predictions[0])}
+    
+    return pred_labels
+
+# Gradio interface
+iface = gr.Interface(
+    fn=predict_diseases, 
+    inputs=[gr.inputs.Image(type="numpy"), gr.inputs.Image(type="numpy")], 
+    outputs="json",
+    title="Ocular Disease Prediction",
+    description="Upload left and right eye images to predict ocular diseases."
+)
+
+# Launch the interface
+iface.launch()