Update app.py

app.py

@@ -2,12 +2,12 @@ import gradio as gr
 import numpy as np
 import cv2
 from tensorflow.keras.models import load_model
+from tensorflow.keras.layers import InputLayer
 
 # Custom object scope to handle InputLayer configuration
-from tensorflow.keras.layers import InputLayer
 custom_objects = {'InputLayer': InputLayer}
 
-# Load the model
+# Load model
 def load_ocular_model():
     return load_model('odir_cnn_model.h5', custom_objects=custom_objects)
 
@@ -19,13 +19,13 @@ LABELS = ['Normal (N)', 'Diabetes (D)', 'Glaucoma (G)', 'Cataract (C)',
           'Pathological Myopia (M)', 'Other diseases/abnormalities (O)']
 
 # Preprocess the image
-def preprocess_image(image, img_size=128):  
+def preprocess_image(image, img_size=128):  # Change img_size to 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
+# Predict diseases based on left and right images
 def predict_diseases(left_image, right_image):
     left_img = preprocess_image(left_image)
     right_img = preprocess_image(right_image)
@@ -39,14 +39,24 @@ def predict_diseases(left_image, right_image):
     
     return pred_labels
 
+# Define the Gradio interface
+def predict_from_images(left_image, right_image):
+    left_image = cv2.cvtColor(np.array(left_image), cv2.COLOR_RGB2BGR)  # Convert Gradio's image to OpenCV format
+    right_image = cv2.cvtColor(np.array(right_image), cv2.COLOR_RGB2BGR)  # Convert Gradio's image to OpenCV format
+    predictions = predict_diseases(left_image, right_image)
+    
+    return predictions
+
 # 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()
+input_images = [gr.Image(type="pil", label="Left Eye Image"), gr.Image(type="pil", label="Right Eye Image")]
+output_labels = gr.Label(num_top_classes=len(LABELS), label="Ocular Disease Predictions")
+
+gr_interface = gr.Interface(fn=predict_from_images, 
+                            inputs=input_images, 
+                            outputs=output_labels,
+                            title="Ocular Disease Prediction",
+                            description="Upload left and right eye images to predict ocular diseases.",
+                            live=False)
+
+# Launch the Gradio app
+gr_interface.launch()