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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()
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