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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]) |