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Browse files- app.py +31 -0
- cnn_model.h5 +3 -0
- requirements.txt +2 -0
app.py
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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('cnn_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 #normalize
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img=np.expand_dims(img,axis=0)
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return img
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st.title("Cancer Image Classification :cancer:")
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st.write("Select image and model predicts whether it is cancer")
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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='uploaded image')
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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=['Non Cancer','Cancer']
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st.write(class_names[predicted_class])
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cnn_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:7014d594d672d43c71288b5ff4f3b798796d1e027d09c2c321fe750765512c4b
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size 165525616
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requirements.txt
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streamlit
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tensorflow
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