Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+import numpy as np
+import cv2
+from keras.models import Model
+from keras.layers import Input, Conv2D, MaxPooling2D, Conv2DTranspose, concatenate
+
+size = 128
+
+def preprocess_image(image, size=128):
+    image = image.resize((size, size))
+    image = image.convert("L")
+    image = np.array(image) / 255.0
+    return image
+
+def conv_block(input, num_filters):
+    conv = Conv2D(num_filters, (3, 3), activation="relu", padding="same", kernel_initializer='he_normal')(input)
+    conv = Conv2D(num_filters, (3, 3), activation="relu", padding="same", kernel_initializer='he_normal')(conv)
+    return conv
+
+def encoder_block(input, num_filters):
+    conv = conv_block(input, num_filters)
+    pool = MaxPooling2D((2, 2))(conv)
+    return conv, pool
+
+def decoder_block(input, skip_features, num_filters):
+    uconv = Conv2DTranspose(num_filters, (2, 2), strides=2, padding="same")(input)
+    con = concatenate([uconv, skip_features])
+    conv = conv_block(con, num_filters)
+    return conv
+
+def build_model(input_shape):
+    input_layer = Input(input_shape)
+    
+    s1, p1 = encoder_block(input_layer, 64)
+    s2, p2 = encoder_block(p1, 128)
+    s3, p3 = encoder_block(p2, 256)
+    s4, p4 = encoder_block(p3, 512)
+
+    b1 = conv_block(p4, 1024)
+
+    d1 = decoder_block(b1, s4, 512)
+    d2 = decoder_block(d1, s3, 256)
+    d3 = decoder_block(d2, s2, 128)
+    d4 = decoder_block(d3, s1, 64)
+    
+    output_layer = Conv2D(1, 1, padding="same", activation="sigmoid")(d4)                                                               
+    model = Model(input_layer, output_layer, name="U-Net")
+    model.load_weights('modelo.h5')
+    return model
+    
+def preprocess_image(image, size=128):
+    image = cv2.resize(image, (size, size))
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+    image = image / 255.
+    return image
+
+def segment(image):
+    image = preprocess_image(image, size=size)
+    image = np.expand_dims(image, 0)
+    output = model.predict(image, verbose=0)
+    mask_image = output[0]
+    mask_image = np.squeeze(mask_image, -1)
+    mask_image *= 255
+    mask_image = mask_image.astype(np.uint8)
+    mask_image = Image.fromarray(mask_image).convert("L")
+
+    #Porcentaje de 0
+    positive_pixels = np.count_nonzero(mask_image)
+    total_pixels = mask_image.size[0] * mask_image.size[1]
+    percentage = (positive_pixels / total_pixels) * 100
+
+     # Calcular los porcentajes de 0 y 1
+    class_0_percentage = 100 - percentage
+    class_1_percentage = percentage
+
+    return mask_image, class_0_percentage, class_1_percentage
+
+if __name__ == "__main__":
+    model = build_model(input_shape=(size, size, 1))
+    gr.Interface(
+        fn=segment,
+        inputs="image",
+        outputs=[
+            gr.Image(type="pil", label="Breast Cancer Mask"),
+            gr.Number(label="Benigno"),
+            gr.Number(label="Maligno")
+        ],
+        examples=[["benign.png"], ["malignant .png"]],
+        title = '<h1 style="text-align: center;">Breast Cancer  </h1>',
+        
+        description = """
+        Explore essa incrível novidade no diagnóstico e tratamento do câncer de mama! 
+        Apresentamos a demo de Segmentação de Imagem por Ultrassom de Câncer de Mama. 
+        Faça o upload de uma imagem ou experimente um dos exemplos abaixo! 🙌
+        """,
+        theme="default",
+        layout="vertical",
+        verbose=True
+    ).launch(debug=True)