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import streamlit as st
from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np
model=load_model('my_fv_model.h5')
def process_image(img):
img=img.resize((170,170)) #boyutunu 170*170 pixel yaptık
img=np.array(img)
img=img/255.0 #Normalize ettik
img=np.expand_dims(img,axis=0)
return img
st.title('Meyze Sebze Siniflandirmasi :tomato:')
st.write('Resim seç ve hangi meyve/sebze olduğunu 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='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]) |