app.py

import gradio as gr
import tensorflow as tf
from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np

# 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)

# 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

# Create the Gradio interface
gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs=gr.Label(num_top_classes=2),
).launch()