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import gradio as gr | |
import numpy as np | |
from tensorflow.keras.preprocessing import image | |
from tensorflow.keras.models import load_model | |
from PIL import Image as PILImage | |
import io | |
# Carregar o modelo treinado | |
model = load_model('model_1.0000.h5') | |
def predict_and_invert(input_image): | |
input_image = input_image.resize((224, 224)) | |
img = image.img_to_array(input_image) / 255.0 | |
img = np.expand_dims(img, axis=0) | |
img = img[:, :224, :224, :] | |
prediction = model.predict(img) | |
if prediction[0][0] > 0.5: | |
result = "Anomalia cardíaca (Doente)" | |
else: | |
result = "Normal (Sem anomalia)" | |
img_inverted = 1 - img[0] # Inverter a imagem | |
img_inverted_pil = PILImage.fromarray(np.uint8(img_inverted * 255)) | |
img_inverted_bytes = io.BytesIO() | |
img_inverted_pil.save(img_inverted_bytes, format='PNG') | |
return result, img_inverted_pil | |
# Criar uma interface Gradio | |
iface = gr.Interface( | |
fn=predict_and_invert, | |
inputs=gr.inputs.Image(type="pil", label="Carregar uma imagem"), | |
outputs=["text", "image"] | |
) | |
# Executar a interface Gradio | |
iface.launch() | |