| import streamlit as st | |
| import numpy as np | |
| from keras.preprocessing import image | |
| from keras.models import load_model | |
| model = load_model('braintumordetectmodel.h5') | |
| st.title('Classification of Brain Tumor using CNN') | |
| st.text("") | |
| upload_brain_photo = st.file_uploader('Please upload the photo of Brain MRI Image', type=['jpg', 'png']) | |
| if upload_brain_photo is not None: | |
| brain_photo_uploaded = image.load_img(upload_brain_photo, target_size=(180, 180, 3)) | |
| st.text("") | |
| col1, col2, col3 = st.columns (3) | |
| with col1: | |
| st.write (' ') | |
| with col2: | |
| st.image (brain_photo_uploaded, caption='Preview of the uploaded Brain MRI Image', width=250) | |
| with col3: | |
| st.write (' ') | |
| brain_photo_uploaded_to_arr = image.img_to_array(brain_photo_uploaded) | |
| brain_photo_uploaded_to_arr = brain_photo_uploaded_to_arr / 255 | |
| brain_photo_uploaded_to_arr_expand = np.expand_dims(brain_photo_uploaded_to_arr, axis=0) | |
| prediction = (model.predict(brain_photo_uploaded_to_arr_expand) > 0.5).astype('int32') | |
| st.text("") | |
| if prediction[0][0] == 0: | |
| 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) | |
| else: | |
| 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) |