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import streamlit as st
from streamlit_option_menu import option_menu
import streamlit.components.v1 as components
from tensorflow import keras
import tensorflow as tf
import numpy as np
import cv2
import io
import pandas as pd
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
if 'model' not in st.session_state:
st.session_state.model = 'Brain Tumor Detection'
def update_radio():
st.session_state.model =st.session_state.radio
if 'clas' not in st.session_state:
st.session_state.clas = '2 Classes'
def update_selbox():
st.session_state.clas =st.session_state.box
if 'check' not in st.session_state:
st.session_state.check1 = False
def update_check():
st.session_state.check1 =st.session_state.check
def update_photo():
st.session_state.photo =st.session_state.image
def pred(img,radio,selbox,check):
img = tf.keras.utils.load_img(
img,
grayscale=False,
color_mode='rgb',
target_size=(224,224),
interpolation='nearest',
keep_aspect_ratio=False
)
os.remove(st.session_state.image.name)
img = np.array(img).reshape(-1, 224, 224, 3)
if radio =='Alzheimer Detection':
model = keras.models.load_model('alzheimer_99.5.h5')
result=['Mild_Demented', 'Moderate_Demented', 'Non_Demented', 'Very_Mild_Demented']
else:
if selbox == '44 Classes':
model = keras.models.load_model('44class_96.5.h5')
result=['Astrocitoma T1','Astrocitoma T1C+','Astrocitoma T2','Carcinoma T1','Carcinoma T1C+','Carcinoma T2','Ependimoma T1','Ependimoma T1C+','Ependimoma T2','Ganglioglioma T1','Ganglioglioma T1C+',
'Ganglioglioma T2','Germinoma T1','Germinoma T1C+','Germinoma T2','Glioblastoma T1','Glioblastoma T1C+','Glioblastoma T2','Granuloma T1','Granuloma T1C+','Granuloma T2','Meduloblastoma T1',
'Meduloblastoma T1C+','Meduloblastoma T2','Meningioma T1','Meningioma T1C+','Meningioma T2','Neurocitoma T1','Neurocitoma T1C+','Neurocitoma T2','Oligodendroglioma T1','Oligodendroglioma T1C+',
'Oligodendroglioma T2','Papiloma T1','Papiloma T1C+','Papiloma T2','Schwannoma T1','Schwannoma T1C+','Schwannoma T2','Tuberculoma T1','Tuberculoma T1C+','Tuberculoma T2','_NORMAL T1','_NORMAL T2']
if selbox == '17 Classes':
model = keras.models.load_model('17class_98.1.h5')
result=['Glioma (Astrocitoma, Ganglioglioma, Glioblastoma, Oligodendroglioma, Ependimoma) T1','Glioma (Astrocitoma, Ganglioglioma, Glioblastoma, Oligodendroglioma, Ependimoma) T1C+','Glioma (Astrocitoma, Ganglioglioma, Glioblastoma, Oligodendroglioma, Ependimoma) T2',
'Meningioma (de Baixo Grau, Atípico, Anaplásico, Transicional) T1','Meningioma (de Baixo Grau, Atípico, Anaplásico, Transicional) T1C+','Meningioma (de Baixo Grau, Atípico, Anaplásico, Transicional) T2','NORMAL T1','NORMAL T2','Neurocitoma (Central - Intraventricular, Extraventricular) T1','Neurocitoma (Central - Intraventricular, Extraventricular) T1C+',
'Neurocitoma (Central - Intraventricular, Extraventricular) T2','Outros Tipos de Lesões (Abscessos, Cistos, Encefalopatias Diversas) T1','Outros Tipos de Lesões (Abscessos, Cistos, Encefalopatias Diversas) T1C+','Outros Tipos de Lesões (Abscessos, Cistos, Encefalopatias Diversas) T2','Schwannoma (Acustico, Vestibular - Trigeminal) T1',
'Schwannoma (Acustico, Vestibular - Trigeminal) T1C+','Schwannoma (Acustico, Vestibular - Trigeminal) T2']
if selbox == '15 Classes':
model = keras.models.load_model('15class_99.8.h5')
result=['Astrocitoma','Carcinoma','Ependimoma','Ganglioglioma','Germinoma','Glioblastoma','Granuloma','Meduloblastoma','Meningioma','Neurocitoma','Oligodendroglioma','Papiloma','Schwannoma','Tuberculoma','_NORMAL']
if selbox == '2 Classes':
model = keras.models.load_model('2calss_lagre_dataset_99.1.h5')
result=['no', 'yes']
pred= model.predict(img)
if check:
pred=pd.DataFrame({
'class_name' : result,
'pred_score' : pred.flatten()
})
return pred
pred = np.argmax(pred, axis=1)
return result[pred[0]]
def spr_sidebar():
menu=option_menu(
menu_title=None,
options=['Home','About'],
icons=['house','info-square'],
menu_icon='cast',
default_index=0,
orientation='horizontal'
)
if menu=='Home':
st.session_state.app_mode = 'Home'
elif menu=='About':
st.session_state.app_mode = 'About'
def home_page():
st.session_state.check=st.session_state.check1
st.session_state.radio=st.session_state.model
st.session_state.box=st.session_state.clas
if 'photo' in st.session_state:
st.session_state.image=st.session_state.photo
st.title('Brain Tumor Detection')
st.session_state.image=st.file_uploader('Upload MRI Image',accept_multiple_files=False,type=['png', 'jpg','jpeg'],key="upload",on_change=update_photo)
if st.session_state.image != None:
st.image(st.session_state.image,width=300)
radio=st.radio("Model",options=('Brain Tumor Detection','Alzheimer Detection'),key='radio',on_change=update_radio)
check=st.checkbox('Show Prediction Scores',key='check',on_change=update_check)
if radio =='Brain Tumor Detection':
selbox=st.selectbox("choose a number of Classes",options=('44 Classes','17 Classes' ,'15 Classes','2 Classes'),index=0,key='box',on_change=update_selbox)
else:
selbox=st.radio("choose a number of Classes",options=(['4 Classes']),index=0,key='box1',on_change=update_selbox)
state =st.button('Get Result')
if state:
f=open(st.session_state.image.name, 'wb')
f.write(st.session_state.image.getbuffer())
f.close()
st.write(pred(st.session_state.image.name,radio,selbox,check))
def About_page():
st.write("Soon")
def main():
spr_sidebar()
if st.session_state.app_mode == 'Home':
home_page()
if st.session_state.app_mode == 'About' :
About_page()
# Run main()
if __name__ == '__main__':
main() |