Update app.py

app.py

@@ -1,10 +1,13 @@
 import gradio as gr
-import numpy as np
 from PIL import Image
+import numpy as np
+import cv2
 from keras.models import Model
 from keras.layers import Input, Conv2D, MaxPooling2D, Conv2DTranspose, concatenate
 
-def preprocess_image(image, size):
+size = 128
+
+def preprocess_image(image, size=128):
     image = image.resize((size, size))
     image = image.convert("L")
     image = np.array(image) / 255.0
@@ -26,7 +29,7 @@ def decoder_block(input, skip_features, num_filters):
     conv = conv_block(con, num_filters)
     return conv
 
-def build_model(input_shape, num_output_channels):
+def build_model(input_shape):
     input_layer = Input(input_shape)
     
     s1, p1 = encoder_block(input_layer, 64)
@@ -41,13 +44,19 @@ def build_model(input_shape, num_output_channels):
     d3 = decoder_block(d2, s2, 128)
     d4 = decoder_block(d3, s1, 64)
     
-    output_layer = Conv2D(num_output_channels, 1, padding="same", activation="sigmoid")(d4)                                                               
+    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 image_segmentation(image, size=128, num_output_channels=1):
-    image = preprocess_image(image, size)
+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]
@@ -56,33 +65,59 @@ def image_segmentation(image, size=128, num_output_channels=1):
     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__":
-    size = 128
-    num_output_channels = 1
-    model = build_model(input_shape=(size, size, 1), num_output_channels=num_output_channels)
+    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")
+        ],
+        title = '<h1 style="text-align: center;"> Cancer ultrasonido de Cancer de Mama  </h1>',
+        
+        description = """
+            Presentamos la demostración de Segmentación de Imágenes por Ultrasonido de Cáncer de Mama.
+        """,
+        theme="default",
+        layout="vertical",
+        verbose=True
+    ).launch(debug=True) 
+
+if __name__ == "__main__":
+    model = build_model(input_shape=(size, size, 1))
     gr.Interface(
         fn=image_segmentation,
         inputs="image",
         outputs=[
-            gr.Image(type="pil", label="Cáncer de mama "),
+            gr.Image(type="pil", label="Máscara de Cáncer de Mama"),
             gr.Number(label="Benigno"),
             gr.Number(label="Maligno")
         ],
-        title='<h1 style="text-align: center;">Cáncer de mama   </h1>',
+        title='<h1 style="text-align: center;">Segmentación de Ultrasonidos de Cáncer de Mama</h1>',
         description="""
-           Demostración de segmentación de imágenes de ultrasonido de cáncer de mama.
+            Presentamos la demostración de Segmentación de Imágenes por Ultrasonido de Cáncer de Mama.
         """,
+        examples=[
+            ['benign(10).png'],
+            ['benign(109).png'],
+            ['malignant.png']
+        ],
         theme="default",
         layout="vertical",
         verbose=True
     ).launch(debug=True)
 
+