app.py

import streamlit as st
from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np


model=load_model('my_cnn_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) #resmi ortaya almasini sagladik
    return img

st.title('Image Classification of Cancer Image :eye:')
st.write('Select an image, and the model predicts cancer or not')

file=st.file_uploader('Bir resim sec',type=['jpg','jpeg','png'])

if file is not None:
    img=Image.open(file)
    st.image(img,caption='yuklenen resim')
    image=process_image(img)
    predictions=model.predict(image)
    predicted_class=np.argmax(predictions)

    class_names =['Not Cancer','Cancer']
    st.write(class_names[predicted_class])