Create app.py

app.py

@@ -0,0 +1,54 @@
+import gradio as gr
+import numpy as np
+import cv2
+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 = (64, 64, 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(0.1))
+        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 = cv2.resize(img, (64,64))
+        img = img.reshape(1,64,64,3)
+        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()