Update app.py

app.py

@@ -2,6 +2,8 @@ import gradio as gr
 import tensorflow as tf
 from tensorflow.keras.models import load_model
 from tensorflow.keras.layers import Layer
+import numpy as np
+from PIL import Image
 
 # Define the custom 'FixedDropout' layer
 class FixedDropout(Layer):
@@ -29,7 +31,18 @@ class_labels = ["Normal", "Cataract"]
 
 # Define a function for prediction
 def predict(image):
-    # Your prediction code here...
+    # Preprocess the input image
+    image = image.resize((224, 224))  # Adjust the size as needed
+    image = np.array(image) / 255.0  # Normalize pixel values
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+
+    # Make a prediction using the loaded TensorFlow model
+    predictions = tf_model.predict(image)
+
+    # Get the predicted class label
+    predicted_label = class_labels[np.argmax(predictions)]
+
+    return predicted_label
 
 # Create the Gradio interface
 gr.Interface(