Skin_Cancer / app.py
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import streamlit as st
from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np
model=load_model('cnn_model.h5')
def process_image(img):
img=img.resize((170,170))
img=np.array(img)
img=img/255.0 #normalize
img=np.expand_dims(img,axis=0)
return img
st.title("Cancer Image Classification :cancer:")
st.write("Select image and model predicts whether it is cancer")
file=st.file_uploader('Bir Resim Sec',type=['jpg','jpeg','png'])
if file is not None:
img=Image.open(file)
st.image(img,caption='uploaded image')
image= process_image(img)
prediction=model.predict(image)
predicted_class=np.argmax(prediction)
class_names=['Non Cancer','Cancer']
st.write(class_names[predicted_class])