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

model=load_model('src/fruit_vegetable.h5')

def process_image(img):
    img=img.resize((240,240))
    img=np.array(img)
    img=img/255.0
    img=np.expand_dims(img,axis=0)
    return img

st.title("Meyve/Sebze sınıflandırma")
st.write("Resmini yükle ve model meyve/sebze ismini tahmin etsin")

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

if file is not None:
    img=Image.open(file)
    st.image(img,caption='Meyve/Sebze')
    image= process_image(img)
    prediction=model.predict(image)
    predicted_class=np.argmax(prediction)
    
    class_names=['capsicum','sweetcorn','orange','tomato','turnip','ginger','raddish','pomegranate','pineapple',
        'jalepeno','apple','carrot','lettuce','bell pepper','eggplant','beetroot','kiwi','pear',
        'cabbage','cauliflower','paprika','lemon','sweetpotato','grapes','cucumber','corn','banana',
        'garlic','chilli pepper','watermelon','mango','peas','onion','potato','spinach','soy beans']
    st.write(class_names[predicted_class])