app.py

import streamlit as st
from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np
import cv2

import tensorflow as tf

model_path = "my_cnn_model.h5"
model = tf.keras.models.load_model(model_path)


def process_image(img):
    img = cv2.resize(img, (170, 170))
    img = img / 255.0
    img = np.expand_dims(img, axis=0)
    return img

st.title('Kanser Resmi Siniflandirma :cancer:')
st.write('Resim seç ve model kanser olup olmadigini tahmin etsin')

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

if file is not None:
    img = Image.open(file)
    st.image(img, caption='Yuklenen resim')
    img = np.array(img)  
    if img.shape[2] == 4:  
        img = cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)  
    
    image = process_image(img)
    prediction = model.predict(image)
    prediction_class = np.argmax(prediction) 

    class_names = ['Kanser Değil', 'Kanser']
    st.write(class_names[prediction_class])