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import streamlit as st |
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from tensorflow.keras.models import load_model |
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from PIL import Image |
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import numpy as np |
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model=load_model('my_fv_model.h5') |
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def process_image(img): |
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img=img.resize((170,170)) |
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img=np.array(img) |
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img=img/255.0 |
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img=np.expand_dims(img,axis=0) |
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return img |
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st.title('Meyze Sebze Siniflandirmasi :tomato:') |
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st.write('Resim seç ve hangi meyve/sebze olduğunu tahmin etsin') |
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file=st.file_uploader('Bir Resim Sec', type=['jpg','jpeg','png']) |
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if file is not None: |
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img=Image.open(file) |
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st.image(img,caption='yuklenen resim') |
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image=process_image(img) |
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prediction=model.predict(image) |
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predicted_class=np.argmax(prediction) |
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class_names=['apple','banana','beetroot','bell pepper','cabbage','capsicum','carrot', |
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'cauliflower','chilli pepper','corn','cucumber','eggplant','garlic','ginger','grapes', |
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'jalepeno','kiwi','lemon','lettuce','mango','onion','orange','paprika','pear','peas', |
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'pineapple','pomegranate','potato','raddish','soy beans','spinach','sweetcorn','sweetpotato', |
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'tomato','turnip','watermelon'] |
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st.write(class_names[predicted_class]) |
