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67d3aa0
1
Parent(s):
523a6d5
Update app.py
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app.py
CHANGED
@@ -1,6 +1,6 @@
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import streamlit as st
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import numpy as np
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from keras.preprocessing import image
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from keras.models import load_model
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@@ -32,14 +32,24 @@ def predict_breed(image_file):
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def main():
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st.title('Dog Breed Classification')
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uploaded_file = st.file_uploader('Choose an image of a dog', type=['jpg', 'jpeg', 'png'])
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if uploaded_file is not None:
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image_file = uploaded_file.name
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with open(image_file, 'wb') as f:
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f.write(uploaded_file.getbuffer())
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predicted_class = predict_breed(image_file)
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st.image(uploaded_file, caption=f'Predicted class: {predicted_class}', use_column_width=True)
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if __name__ == '__main__':
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main()
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import streamlit as st
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import numpy as np
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from tensorflow.keras.preprocessing import image
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from keras.models import load_model
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def main():
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st.title('Dog Breed Classification')
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uploaded_file = st.file_uploader('Choose an image of a dog', type=['jpg', 'jpeg', 'png'])
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if uploaded_file is not None:
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image_file = uploaded_file.name
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with open(image_file, 'wb') as f:
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f.write(uploaded_file.getbuffer())
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predicted_class = predict_breed(image_file)
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st.image(uploaded_file, caption=f'Predicted class: {predicted_class}', use_column_width=True)
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# Add a button to trigger image upload
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if st.button('Upload Image'):
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uploaded_file = st.file_uploader('Choose an image of a dog', type=['jpg', 'jpeg', 'png'])
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if uploaded_file is not None:
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image_file = uploaded_file.name
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with open(image_file, 'wb') as f:
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f.write(uploaded_file.getbuffer())
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predicted_class = predict_breed(image_file)
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st.image(uploaded_file, caption=f'Predicted class: {predicted_class}', use_column_width=True)
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if __name__ == '__main__':
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main()
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