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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_fish_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('Large Scale Fish :fish:')
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st.write('Resim seç ve hangi balik türü 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=['Black Sea Sprat','Gilt-Head Bream','Hourse Mackerel','Red Mullet',
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'Red Sea Bream','Sea Bass','Shrimp','Striped Red Mullet','Trout']
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st.write(class_names[predicted_class]) |
