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a48b844 c80ffc3 4d88b80 c80ffc3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | 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])
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