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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('src/skin_cancer_model.h5')
def process_image(img):
img=img.resize((170,170))
img=np.array(img)
img=img/255.0
img=np.expand_dims(img,axis=0)
return img
st.title('Deri Kanser resmi sınıflandırma :cancer:')
st.write('Resim seç, model kanser olup olmadığını tahmin etsin!')
file=st.file_uploader('Bir resim yükle',type=['jpg','jpeg','png'])
if file is not None: # Resim yuklenmisse burasi calisacak
img=Image.open(file)
st.image(img, caption='Yuklenen Resim')
image=process_image(img)
prediction=model.predict(image)
predicted_class = 1 if prediction > 0.5 else 0
class_names=['Kanser değil','Kanser']
st.write(class_names[predicted_class]) |