Update app.py

app.py

@@ -1,13 +1,10 @@
 import gradio as gr
-from PIL import Image
 import numpy as np
-import cv2
+from PIL import Image
 from keras.models import Model
 from keras.layers import Input, Conv2D, MaxPooling2D, Conv2DTranspose, concatenate
 
-size = 128
-
-def preprocess_image(image, size=128):
+def preprocess_image(image, size):
     image = image.resize((size, size))
     image = image.convert("L")
     image = np.array(image) / 255.0
@@ -29,7 +26,7 @@ def decoder_block(input, skip_features, num_filters):
     conv = conv_block(con, num_filters)
     return conv
 
-def build_model(input_shape):
+def build_model(input_shape, num_output_channels):
     input_layer = Input(input_shape)
     
     s1, p1 = encoder_block(input_layer, 64)
@@ -44,19 +41,13 @@ def build_model(input_shape):
     d3 = decoder_block(d2, s2, 128)
     d4 = decoder_block(d3, s1, 64)
     
-    output_layer = Conv2D(1, 1, padding="same", activation="sigmoid")(d4)                                                               
+    output_layer = Conv2D(num_output_channels, 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)
+def image_segmentation(image, size=128, num_output_channels=1):
+    image = preprocess_image(image, size)
     image = np.expand_dims(image, 0)
     output = model.predict(image, verbose=0)
     mask_image = output[0]
@@ -65,36 +56,33 @@ def segment(image):
     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))
+    size = 128
+    num_output_channels = 1
+    model = build_model(input_shape=(size, size, 1), num_output_channels=num_output_channels)
     gr.Interface(
-        fn=segment,
+        fn=image_segmentation,
         inputs="image",
         outputs=[
-            gr.Image(type="pil", label="Breast Cancer Mask"),
+            gr.Image(type="pil", label="Cáncer de mama "),
             gr.Number(label="Benigno"),
             gr.Number(label="Maligno")
         ],
-        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! 🙌
+        title='<h1 style="text-align: center;">Cáncer de mama   </h1>',
+        description="""
+           Demostración de segmentación de imágenes de ultrasonido de cáncer de mama.
         """,
         theme="default",
         layout="vertical",
         verbose=True
-    ).launch(debug=True) 
+    ).launch(debug=True)