main.py

import streamlit as st
from streamlit_option_menu import option_menu
from tensorflow import keras
import tensorflow as tf
import numpy as np
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 = '15 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()*100
        })
        pred.sort_values(['pred_score'],ascending = False,kind='stable',inplace=True)
        pred.reset_index(drop=True,inplace=True)
        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 MRI Tumor and Alzheimer Classification')
    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=150)
        col,col2=st.columns([2,3])
        radio=col.radio("Model",options=('Brain Tumor Detection','Alzheimer Detection'),key='radio',on_change=update_radio)
        check=col.checkbox('Show Prediction Scores',key='check',on_change=update_check)
        if radio =='Brain Tumor Detection':
            selbox=col2.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=col2.radio("choose a number of Classes",options=(['4 Classes']),index=0,key='box1',on_change=update_selbox)
        
        state =col.button('Get Result')
        if state:
            f=open(st.session_state.image.name, 'wb') 
            f.write(st.session_state.image.getbuffer())
            f.close()
            
            with st.spinner('Model Running....'):
                res=pred(st.session_state.image.name,radio,selbox,check)
            if check:
                col2.write(res)
            else :
                col2.success(str(res))
                
                    
                



def About_page():
    st.header('Development')
    """
    Check out the [repository](https://github.com/abdelrhmanelruby/Brain-MRI-Tumor-and-Alzheimer-Classification) for the source code and approaches
    """
    st.subheader('Data')
    """
    For the main model, we used [Brain Tumor MRI Images 44 Classes](https://www.kaggle.com/datasets/fernando2rad/brain-tumor-mri-images-44c) a collection of T1, contrast-enhanced T1, and T2 magnetic resonance images separated by brain tumor type. Contains a total of 4479 images and 44 classes.
    
    We used this dataset to train our main CNN model and then tested it on different datasets. We used the same model and weights as the main model, with the only difference being the output layer. 
    ### Testing datasets 
    - [Brain Tumor MRI Images 44 Classes](https://www.kaggle.com/datasets/fernando2rad/brain-tumor-mri-images-44c) using only tumor types 4479 images and 15 classes
    - [Brain Tumor MRI Images 17 Classes](https://www.kaggle.com/datasets/fernando2rad/brain-tumor-mri-images-17-classes) contains 4448 images and 17 classes
    - [Brain Tumor Classification (MRI)](https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri) contains 3264 images and 4 classes
    - [Brain MRI Images for Brain Tumor Detection](https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection)contains 253 images and 2 classes
    - [Brain_Tumor_Detection_MRI](https://www.kaggle.com/datasets/abhranta/brain-tumor-detection-mri) contains 3060 images and 2 classes
    - [Alzheimer MRI Preprocessed Dataset](https://www.kaggle.com/datasets/sachinkumar413/alzheimer-mri-dataset) contains 6400 images and 2 classes
    """

    """
    ## Contributors
    - AbdElRahman Elruby [Linkedin](https://www.linkedin.com/in/abdelrhmanelruby/) | [Github](https://github.com/abdelrhmanelruby)
    - Marwa Shaaban AbdElhakeem [Linkedin](https://www.linkedin.com/in/marwa-shaaban-abd-elhakim/) | [Github](https://github.com/Marwa-Shaaban)
    - Yara Yasser Farouk [Linkedin](https://www.linkedin.com/in/yara-yasser-64493b249/)
    - Salma Mahmoud Fahim [Linkedin](https://www.linkedin.com/in/salmafahim) | [Github](https://github.com/SalmaFahim)
    """

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()

hide_st_style = """
            <style>
            #MainMenu {visibility: hidden;}
            footer {visibility: hidden;}
            header {visibility: hidden;}
            </style>
            """
st.markdown(hide_st_style, unsafe_allow_html=True)