src/streamlit_app.py

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 # normaliseren
    img=np.expand_dims(img,axis=0)
    return img

st.title('Deri kanser resmi siniflandirma :cancer:')
st.write('Resim sec, model kanser olup olmadigini tahmin etsin!')

file=st.file_uploader('Bir resim yukle',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 degil','Kanser']
    st.write(class_names[predicted_class])