app.py

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

model=load_model('dog_cat_model.h5')

def process_image(img):
    img=img.resize((100,100))  #boyutunu 100*1000 pixel yaptık
    img=np.array(img)
    img=img/255.0  #Normalize ettik
    img=np.expand_dims(img,axis=0)
    return img

st.title('DOGS vs CATS REDUX')
st.write("Choose a picture and guess whether it's a cat or a dog")

file=st.file_uploader('Choose a picture', 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=['cat','dog']
    st.write(class_names[predicted_class])