app.py

import gradio as gr
import tensorflow as tf
import requests
import cv2
import numpy as np

# Define a custom layer 'FixedDropout'
def fixed_dropout(x):
    return tf.keras.layers.Dropout(0.5)(x)

# Function to register custom layers within a custom_object_scope
def register_custom_layers():
    return tf.keras.utils.custom_object_scope({'FixedDropout': fixed_dropout})

# Load the TensorFlow model within the custom_object_scope
with register_custom_layers():
    tf_model = tf.keras.models.load_model('modelo_treinado.h5')

class_labels = ["Normal", "Cataract"]

def predict(inp):
    # Use the TensorFlow model to predict Normal or Cataract
    img_array = cv2.cvtColor(np.array(inp), cv2.COLOR_RGB2BGR)
    img_array = cv2.resize(img_array, (224, 224))
    img_array = img_array / 255.0
    img_array = np.expand_dims(img_array, axis=0)

    prediction_tf = tf_model.predict(img_array)
    label_index = np.argmax(prediction_tf)
    confidence_tf = float(prediction_tf[0, label_index])

    return class_labels[label_index], confidence_tf

demo = gr.Interface(
    fn=predict,
    inputs=gr.inputs.Image(type="pil"),
    outputs=["label", "number"],
)

demo.launch()