abd.py

app.py

@@ -1,51 +1,51 @@
-import gradio as gr
-import numpy as np
-from skimage.transform import resize
-from tensorflow.keras.models import Sequential, load_model
-from tensorflow.keras.layers import Conv2D, MaxPool2D, Dropout, Dense, Flatten, BatchNormalization
-
-class SkinCancer :
-    def __init__ (self):
-        self.model = self.load_model()
-
-    def build_model (self) :
-        model = Sequential()
-        model.add(Conv2D(filters = 128, kernel_size = (4,4), input_shape = (32, 32, 3), activation = 'relu'))
-        model.add(MaxPool2D(pool_size = (4,4)))
-        model.add(Conv2D(filters = 64, kernel_size = (2,2), activation = 'relu'))
-        model.add(MaxPool2D(pool_size = (2,2)))
-        model.add(BatchNormalization())
-        #model.add(GlobalAveragePooling2D())
-        model.add(Flatten())
-        model.add(Dense(128, activation = 'relu'))
-        model.add(Dropout(0.2))
-        model.add(Dense(2, activation = 'sigmoid')) # sigmoid is better for binary classification
-        #model.summary()
-        return model
-
-    def load_model(self):
-        model = self.build_model()
-        model = load_model("Normal_skin_cancer_model.h5")
-        return model
-
-    def preprocess_image(self,img):
-        img = resize(img, (32,32))
-        img = img.reshape(1,32,32,3)
-        return img
-
-    def predict(self,img):
-        real_labels = ["benign", "malignant"]
-        img = self.preprocess_image(img)
-        res = np.argmax(self.model.predict(img))
-        return real_labels[res]
-
-def Test(img):
-    model_new = SkinCancer()
-    res = model_new.predict(img)
-    return res
-#interface
-interface = gr.Interface(fn = Test,
-                        inputs = gr.inputs.Image(shape=(200,200)),
-                        outputs=["text"],
-                        title="Skin Cancer detection")
+import gradio as gr
+import numpy as np
+from skimage.transform import resize
+from tensorflow.keras.models import Sequential, load_model
+from tensorflow.keras.layers import Conv2D, MaxPool2D, Dropout, Dense, Flatten, BatchNormalization
+
+class SkinCancer :
+    def __init__ (self):
+        self.model = self.load_model()
+
+    def build_model (self) :
+        model = Sequential()
+        model.add(Conv2D(filters = 128, kernel_size = (4,4), input_shape = (32, 32, 3), activation = 'relu'))
+        model.add(MaxPool2D(pool_size = (4,4)))
+        model.add(Conv2D(filters = 64, kernel_size = (2,2), activation = 'relu'))
+        model.add(MaxPool2D(pool_size = (2,2)))
+        model.add(BatchNormalization())
+        #model.add(GlobalAveragePooling2D())
+        model.add(Flatten())
+        model.add(Dense(128, activation = 'relu'))
+        model.add(Dropout(0.2))
+        model.add(Dense(1, activation = 'sigmoid')) # sigmoid is better for binary classification
+        #model.summary()
+        return model
+
+    def load_model(self):
+        model = self.build_model()
+        model = load_model("Normal_skin_cancer_model.h5")
+        return model
+
+    def preprocess_image(self,img):
+        img = resize(img, (32,32))
+        img = img.reshape(1,32,32,3)
+        return img
+
+    def predict(self,img):
+        real_labels = ["benign", "malignant"]
+        img = self.preprocess_image(img)
+        res = np.argmax(self.model.predict(img))
+        return real_labels[res]
+
+def Test(img):
+    model_new = SkinCancer()
+    res = model_new.predict(img)
+    return res
+#interface
+interface = gr.Interface(fn = Test,
+                        inputs = gr.inputs.Image(shape=(200,200)),
+                        outputs=["text"],
+                        title="Skin Cancer detection")
 interface.launch()