Create app.py
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app.py
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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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model = load_model('braintumordetectmodel.h5')
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st.title('Classification of Brain Tumor using CNN')
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st.text("")
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upload_brain_photo = st.file_uploader('Please upload the photo of Brain MRI Image', type=['jpg', 'png'])
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if upload_brain_photo is not None:
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brain_photo_uploaded = image.load_img(upload_brain_photo, target_size=(180, 180, 3))
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st.text("")
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col1, col2, col3 = st.columns (3)
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with col1:
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st.write (' ')
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with col2:
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st.image (brain_photo_uploaded, caption='Preview of the uploaded Brain MRI Image', width=250)
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with col3:
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st.write (' ')
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brain_photo_uploaded_to_arr = image.img_to_array(brain_photo_uploaded)
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brain_photo_uploaded_to_arr = brain_photo_uploaded_to_arr / 255
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brain_photo_uploaded_to_arr_expand = np.expand_dims(brain_photo_uploaded_to_arr, axis=0)
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prediction = (model.predict(brain_photo_uploaded_to_arr_expand) > 0.5).astype('int32')
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st.text("")
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if prediction[0][0] == 0:
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st.markdown("<div style='background-color: green; padding: 8px; border-radius: 10px; text-align: center; color: white; font-size: large'>The Brain MRI imaging that you uploaded shows no signs of any tumor.</div>", unsafe_allow_html=True)
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else:
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st.markdown("<div style='background-color: red; padding: 8px; border-radius: 10px; text-align: center; color: white; font-size: large'> The brain imaging that you uploaded indeed has signs of a tumor.</div>", unsafe_allow_html=True)
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