Update app.py

app.py

@@ -1,34 +1,39 @@
 import gradio as gr
 import tensorflow as tf
 from tensorflow.keras.models import load_model
-from PIL import Image
-import numpy as np
+from tensorflow.keras.layers import Layer
 
-# Load the TensorFlow model
-tf_model_path = 'modelo_treinado.h5'  # Update with the path to your TensorFlow model
-tf_model = load_model(tf_model_path)
+# Define the custom 'FixedDropout' layer
+class FixedDropout(Layer):
+    def __init__(self, rate, **kwargs):
+        super(FixedDropout, self).__init__(**kwargs)
+        self.rate = rate
+
+    def call(self, inputs, training=None):
+        if training is None:
+            training = K.learning_phase()
+
+        if training == 1:
+            return K.dropout(inputs, self.rate)
+        return inputs
+
+# Register the custom layer in a custom object scope
+custom_objects = {"FixedDropout": FixedDropout}
+
+# Load the TensorFlow model with the custom object scope
+tf_model_path = 'modelo_treinado.h5'  # Update with the path to your model
+tf_model = load_model(tf_model_path, custom_objects=custom_objects)
 
 # Class labels for the model
 class_labels = ["Normal", "Cataract"]
 
 # Define a function for prediction
 def predict(image):
-    # Preprocess the input image (resize and normalize)
-    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
+    # Your prediction code here...
 
 # Create the Gradio interface
 gr.Interface(
     fn=predict,
     inputs=gr.Image(type="pil"),
-    outputs=gr.Label(num_top_classes=2),
+    outputs=gr.Label(num_top_classes=2)
 ).launch()