app.py

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

model=load_model('mevvesebze_cnn_model.h5')

def process_image(img):
    img=img.resize((70,70)) #boyutunu 70 x 70 pixel yaptik
    img=np.array(img)
    img=img/255.0 #normalize ettik
    img=np.expand_dims(img,axis=0)
    return img

st.title("Meyve ve Sebze Resmi Siniflandirma :potato::carrot::avocado::tomato:")
st.write("Meyve ve sebzeleri tahmin etmek için resim ekleyin")

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)
    prediction=model.predict(image)
    predicted_class=np.argmax(prediction)
    
    class_names=["apple","banana","beetroot","bell pepper","cabbage","capsicum","carrot","cauliflower","chilli pepper","corn","cucumber","eggplant","garlic","ginger","grapes","jalepeno","kiwi","lemon","lettuce","mango","onion","orange","paprika","pear","peas","pineapple","pomegranate","potato","raddish","soy beans","spinach","sweetcorn","sweetpotato","tomato","turnip","watermelon"]
    st.write(class_names[predicted_class])