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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 = (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 = 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() |