app.py

import gradio as gr
import tensorflow as tf
import numpy as np

# Define a custom layer for FixedDropout
class FixedDropout(tf.keras.layers.Dropout):
    def _get_noise_shape(self, inputs):
        if self.noise_shape is None:
            return self.noise_shape
        symbolic_shape = tf.shape(inputs)
        noise_shape = [symbolic_shape[axis] if shape is None else shape
                       for axis, shape in enumerate(self.noise_shape)]
        return tuple(noise_shape)

# Register the custom layer
tf.keras.utils.get_custom_objects()['FixedDropout'] = FixedDropout

# Load your trained TensorFlow model
model = tf.keras.models.load_model('modelo_treinado.h5')  # Load your saved model

# Define a function to make predictions
def classify_image(input_image):
    # Preprocess the input image (resize and normalize)
    input_image = tf.image.resize(input_image, (224, 224))  # Make sure to match your model's input size
    input_image = (input_image / 255.0)  # Normalize to [0, 1]
    input_image = np.expand_dims(input_image, axis=0)  # Add batch dimension

    # Make a prediction using your model
    prediction = model.predict(input_image)

    # Assuming your model outputs probabilities for two classes, you can return the class with the highest probability
    class_index = np.argmax(prediction)
    class_labels = ["Normal", "Cataract"]  # Replace with your actual class labels
    predicted_class = class_labels[class_index]

    return predicted_class

# Create a Gradio interface
input_interface = gr.Interface(
    fn=classify_image,
    inputs="image",  # Specify input type as "image"
    outputs="text"   # Specify output type as "text"
)

# Launch the Gradio app
input_interface.launch()