app.py

import os
import utils
import pickle
import numpy as np
import gradio as gr
import tensorflow as tf
import matplotlib.pyplot as plt
from ttictoc import tic,toc
from keras.models import load_model
from urllib.request import urlretrieve

'''--------------------------- Descarga de modelos ----------------------------'''

# 3D U-Net
if not os.path.exists("unet.h5"):  
    urlretrieve("https://dl.dropboxusercontent.com/s/ay5q8caqzlad7h5/unet.h5?dl=0", "unet.h5")

# Med3D
if not os.path.exists("resnet_50_23dataset.pth"):     
    urlretrieve("https://dl.dropboxusercontent.com/s/otxsgx3e31d5h9i/resnet_50_23dataset.pth?dl=0", "resnet_50_23dataset.pth")

# Clasificador de imágen SVM
if not os.path.exists("svm_model.pickle"): 
    urlretrieve("https://dl.dropboxusercontent.com/s/n3tb3r6oyf06xfx/svm_model.pickle?dl=0", "svm_model.pickle")
    
# Nivel de riesgo
if not os.path.exists("mlp_probabilidad.h5"):  
    urlretrieve("https://dl.dropboxusercontent.com/s/78fjlg374mvjygd/mlp_probabilidad.h5?dl=0", "mlp_probabilidad.h5")

# Scaler para scores
if not os.path.exists("scaler.pickle"): 
    urlretrieve("https://dl.dropboxusercontent.com/s/ow6pe4k45r3xkbl/scaler.pickle?dl=0", "scaler.pickle")
    
# Archivo de texto para reportes
if not os.path.exists("report.txt"): 
    urlretrieve("https://dl.dropboxusercontent.com/s/ycjpkd65rhlicxq/report.txt?dl=0", "report.txt")
    

path_3d_unet = 'unet.h5'
weight_path = 'resnet_50_23dataset.pth'
svm_path = "svm_model.pickle"
prob_model_path = "mlp_probabilidad.h5"
scaler_path = "scaler.pickle" 
report_path = "report.txt"

'''---------------------------- Carga de modelos ------------------------------'''
# 3D U-Net
with tf.device("cpu:0"):
    model_unet = utils.import_3d_unet(path_3d_unet)

# MedNet
device_ids = [0]
mednet_model = utils.create_mednet(weight_path, device_ids)

# SVM model
svm_model = pickle.load(open(svm_path, 'rb'))

# Nivel de riesgo
with tf.device("cpu:0"):
    prob_model = load_model(prob_model_path)

# Scaler
scaler = pickle.load(open(scaler_path, 'rb'))

'''-------------------------------- Funciones ---------------------------------'''
def load_img(file):
    sitk, array = utils.load_img(file.name)  
    
    # Redimención
    mri_image = np.transpose(array)
    mri_image = np.append(mri_image, np.zeros((192-mri_image.shape[0],256,256,)), axis=0)
    
    # Rotación
    mri_image = mri_image.astype(np.float32)
    mri_image = np.rot90(mri_image, axes=(1,2))
    
    return sitk, mri_image

def show_img(img, mri_slice, update):
    fig = plt.figure()
    plt.imshow(img[mri_slice,:,:], cmap='gray')
    
    if update == True:
        return fig, gr.update(visible=True), gr.update(visible=True)
    else:
        return fig

# def show_brain(brain, brain_slice):
#     fig = plt.figure()
#     plt.imshow(brain[brain_slice,:,:], cmap='gray')
    
#     return fig, gr.update(visible=True)

def process_img(img, brain_slice):
    # progress(None,desc="Processing...")
    
    with tf.device("cpu:0"):
        brain = utils.brain_stripping(img, model_unet)
        
        fig, update_slider, _ = show_img(brain, brain_slice, update=True)
        
    return brain, fig, update_slider, gr.update(visible=True)

def save_file(input_name, input_age, input_phone_num, input_emer_name, input_emer_phone_num,
              input_sex, input_MMSE, input_GDSCALE, input_CDR, input_FAQ, input_NPI_Q, input_Diastolic_blood_pressure,
              input_Systolic_blood_pressure, input_Body_heigth, input_Body_weight, input_Heart_rate,
              input_Respiratory_rate, input_Body_temperature, input_Pluse_oximetry,
              input_medications, input_allergies,diagnosis):
    
    
    with open(report_path, 'w') as f:
            # Save Patient Data
            f.write("Patient data:\n")
            f.write(f"\tName: {input_name.capitalize()}\n")
            f.write(f"\tSex: {input_sex}\n")
            f.write(f"\tAge: {input_age}\n")
            f.write(f"\tPhone Number: {input_phone_num}\n")
            f.write(f"\tEmergency Contact Name: {input_emer_name.capitalize()}\n")
            f.write(f"\tEmergency Contact Phone Number: {input_emer_phone_num}\n\n")
    
            # Save Vital Signs
            f.write("Vital Signs:\n")
            f.write(f"\tDiastolic blood pressure: {input_Diastolic_blood_pressure} mm Hg\n")
            f.write(f"\tSystolic blood pressure: {input_Systolic_blood_pressure} mm Hg\n")
            f.write(f"\tBody height: {input_Body_heigth} cm\n")
            f.write(f"\tBody weight: {input_Body_weight} kg\n")
            f.write(f"\tHeart rate: {input_Heart_rate} bpm\n")
            f.write(f"\tRespiratory rate: {input_Respiratory_rate} bpm\n")
            f.write(f"\tBody temperature: {input_Body_temperature} °C\n")
            f.write(f"\tPulse oximetry: {input_Pluse_oximetry}%\n\n")
    
            # Save Medications
            f.write("Medications:\n")
            f.write(f"\tMedications: {input_medications}\n")
            f.write(f"\tAllergies: {input_allergies}\n\n")
            
            # Save clinical data
            f.write("Clinical data:\n")
            f.write(f"\tMMSE total score: {input_MMSE}\n")
            f.write(f"\tGDSCALE total score: {input_GDSCALE}\n")
            f.write(f"\tGlobal CDR: {input_CDR}\n")
            f.write(f"\tFAQ total score: {input_FAQ}\n")
            f.write(f"\tNPI-Q total score: {input_NPI_Q}\n")
                
            # Save Diagnosis
            f.write("Diagnosis:\n")
            f.write(f"\t{diagnosis}\n")

def get_diagnosis(brain_img, input_name, input_age, input_phone_num, input_emer_name, input_emer_phone_num,
                  input_sex, input_MMSE, input_GDSCALE, input_CDR, input_FAQ, input_NPI_Q, input_Diastolic_blood_pressure,
                  input_Systolic_blood_pressure, input_Body_heigth, input_Body_weight, input_Heart_rate,
                  input_Respiratory_rate, input_Body_temperature, input_Pluse_oximetry,
                  input_medications, input_allergies):
    
    # Extracción de características de imagen
    features = utils.get_features(brain_img, mednet_model)
    
    # Clasificación de imagen
    label_img = np.array([svm_model.predict(features)])
    
    if input_sex == "Male":
        sex_dum = 1
    else:
        sex_dum = 0
    
    scores = np.array([input_age, input_MMSE, input_GDSCALE, input_CDR, input_FAQ, input_NPI_Q, sex_dum, label_img])
    
    print(scores)
    
    # Normalización de scores
    scores_norm = scaler.transform(scores.reshape(1,-1))
    
    print(scores_norm)
    
    with tf.device("cpu:0"):
        # Probabilidad de tener MCI
        prob = prob_model.predict(scores_norm)[0,0]
    
    # Probabilidad de tener MCI
    print(prob)
    diagnosis = f"The patient has a probability of {(100*prob):.2f}% of having MCI with a sensitivity of 92.00% and a specificity of 92.75%"
    
    save_file(input_name, input_age, input_phone_num, input_emer_name, input_emer_phone_num,
                  input_sex, input_MMSE, input_GDSCALE, input_CDR, input_FAQ, input_NPI_Q, input_Diastolic_blood_pressure,
                  input_Systolic_blood_pressure, input_Body_heigth, input_Body_weight, input_Heart_rate,
                  input_Respiratory_rate, input_Body_temperature, input_Pluse_oximetry,
                  input_medications, input_allergies,diagnosis)
    
    return gr.update(value=diagnosis), gr.update(value=report_path, visible=True), gr.update(visible=True)


def clear():
    return gr.File.update(value=None), gr.Plot.update(value=None), gr.update(visible=False), gr.Plot.update(value=None), gr.update(visible=False), gr.update(value="The diagnosis will show here..."), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)


'''--------------------------------- Interfaz ---------------------------------'''

with gr.Blocks(theme=gr.themes.Base()) as demo:
    
    with gr.Row():
        # gr.HTML(r"""<center><img src='https://user-images.githubusercontent.com/66338785/233529518-33e8bcdb-146f-49e8-94c4-27d6529ce4f7.png' width="30%" height="30%"></center>""")
        gr.HTML(r"""
                <center><img src='https://user-images.githubusercontent.com/66338785/233531457-f368e04b-5099-42a8-906d-6f1250ea0f1e.png' width="40%" height="40%"></center>
                """)
    
    # Inputs
    with gr.Row():
        with gr.Column(variant="panel", scale=1):
            with gr.Tab("Patient Information"):  
                gr.Markdown('<h2 style="text-align: center; color:#235784;">Patient Information</h2>')
                with gr.Tab("Personal data"):
                    # Objeto para subir archivo nifti
                    input_name = gr.Textbox(placeholder='Enter the patient name', label='Patient name')
                    input_age = gr.Number(label='Age', value=None)
                    input_phone_num = gr.Number(label='Phone number')
                    input_emer_name = gr.Textbox(placeholder='Enter the emergency contact name', label='Emergency contact name')
                    input_emer_phone_num = gr.Number(label='Emergency contact name phone number', value=None)
                    input_sex = gr.Dropdown(["Male", "Female"], label="Sex", value="Male")
                    
                with gr.Tab("Clinical data"):
                    input_MMSE = gr.Slider(minimum=0,
                                           maximum=30,
                                           value=0,
                                           step=1,
                                           label="MMSE total score") 
                    
                    input_GDSCALE = gr.Slider(minimum=0,
                                           maximum=12,
                                           value=0,
                                           step=1,
                                           label="GDSCALE total score") 
                    
                    input_CDR = gr.Slider(minimum=0,
                                           maximum=3,
                                           value=0,
                                           step=0.5,
                                           label="Global CDR") 
                    
                    input_FAQ = gr.Slider(minimum=0,
                                           maximum=30,
                                           value=0,
                                           step=1,
                                           label="FAQ total score") 
                    
                    input_NPI_Q =  gr.Slider(minimum=0,
                                           maximum=30,
                                           value=0,
                                           step=1,
                                           label="NPI-Q total score") 
                          
                    
                with gr.Tab("Vital Signs"):
                    input_Diastolic_blood_pressure = gr.Number(label='Diastolic Blood Pressure(mm Hg)')
                    input_Systolic_blood_pressure = gr.Number(label='Systolic Blood Pressure(mm Hg)')
                    input_Body_heigth = gr.Number(label='Body heigth (cm)')
                    input_Body_weight = gr.Number(label='Body weigth (kg)')
                    input_Heart_rate = gr.Number(label='Heart rate (bpm)')
                    input_Respiratory_rate = gr.Number(label='Respiratory rate (bpm)')
                    input_Body_temperature = gr.Number(label='Body temperature (°C)')
                    input_Pluse_oximetry = gr.Number(label='Pluse oximetry (%)')
                    
                with gr.Tab("Medications"):
                    input_medications = gr.Textbox(label='Medications', lines=5)
                    input_allergies = gr.Textbox(label='Allergies', lines=5)
                    
                with gr.Box():
                    gr.Markdown('<h4 style="color:#235784;">Upload MRI</h4>')             
                    input_file = gr.File(file_count="single", label="MRI File (.nii)", file_types=[".nii"], show_label=False)
                
                with gr.Row():
                    # Botón para cargar imagen
                    load_img_button = gr.Button(value="Load")
                    
                    # Botón para borrar
                    clear_button = gr.Button(value="Clear")
                
                # Botón para procesar imagen
                process_button = gr.Button(value="Process MRI", visible=False, variant="primary")
                
                # Botón para obtener diagnostico
                diagnostic_button = gr.Button(value="Get diagnosis", visible=False, variant="primary")
                
                with gr.Box(visible=False) as download_box:
                    gr.Markdown('<h4 style="color:#235784;"> Download diagnosis report</h4>')  
                    # Descarga de archivo
                    output_file = gr.File(file_count="single", show_label=False, interactive=False, visible=True)
                
            with gr.Tab("About"):  
                gr.Markdown('''
                # SIMCI
                Alzheimer is a degenerative and irreversible neurological disorder that affects cognitive abilities and daily activities. It is divided into three stages: preclinical, mild cognitive impairment (MCI), and dementia. MCI represents a transition to dementia, characterized by a greater-than-expected decline in cognitive function without interference in daily activities. It is estimated that approximately 20% of individuals with MCI progress to dementia, but diagnostic errors are common in this early stage due to ambiguous cognitive changes, including those observed in neuroimaging.
                
                SIMCI is a system for detecting mild cognitive impairment that employs a multimodal approach and a stratification process to address this issue. The system utilizes demographic characteristics and clinical test results to provide medical interpretability and assist specialists in decision-making. The database includes magnetic resonance imaging (MRI) brain scans, clinical examination results, and demographic information. SIMCI achieves an F1-score of 0.9233, a sensitivity of 0.9200, and a specificity of 0.9275.
                
                <center><img src='https://github.com/SebastianBravo/Battery_monitoring_system/assets/66338785/64cd8821-8249-4020-a00d-a6ae5a7a7e53' width="80%" height="80%"></center>
                
                ## Repository 
                https://github.com/SebastianBravo/SIMCI
                
                ## Authors
                
                 - Daniel Stiven Zambrano Acosta, <span>B.Sc</span>
                 - Juan Sebastián Bravo Santacruz, <span>B.Sc</span>
                 - Ing. Wilson Javier Arenas López, <span>M.Sc</span>
                 - Psy. Pablo Alexander Reyes Gavilan, PhD
                 - Ing. Miguel Alfonso Altuve, PhD
                
                
                ## Acknowledgement
                
                 - Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
                 - We thank [MedicalNet](https://github.com/Tencent/MedicalNet) and [segmentation_models_3D](https://github.com/ZFTurbo/segmentation_models_3D) which we build SIMCI.
                ''')
    
        # Outputs 
        with gr.Column(variant="panel", scale=1):
            gr.Markdown('<h2 style="text-align: center; color:#235784;">MRI visualization</h2>')
            
            with gr.Box():
                gr.Markdown('<h4 style="color:#235784;">Loaded MRI</h4>')
                # Plot para imágen original
                plot_img_original = gr.Plot(show_label=False)
                
                # Slider para imágen original
                mri_slider = gr.Slider(minimum=0,
                                       maximum=192,
                                       value=100,
                                       step=1,
                                       label="MRI Slice",
                                       visible=False) 
            
            with gr.Box():
                gr.Markdown('<h4 style="color:#235784;">Proccessed MRI</h4>')
                
                # Plot para imágen procesada
                plot_brain = gr.Plot(show_label=False, visible=True)
            
                # Slider para imágen procesada
                brain_slider = gr.Slider(minimum=0,
                                         maximum=192,
                                         value=100,
                                         step=1,
                                         label="MRI Slice",
                                         visible=False)
                
            with gr.Box():
                gr.Markdown('<h2 style="text-align: center; color:#235784;">Diagnosis</h2>')
                
                # Texto del diagnostico
                diagnosis_text = gr.Textbox(label="Diagnosis",interactive=False, placeholder="The diagnosis will show here...")
    
    
    
    # Variables
    original_input_sitk = gr.State()
    original_input_img = gr.State()
    brain_img = gr.State()
    
     
    update_true = gr.State(True)
    update_false = gr.State(False)
    
    # Cambios
    # Cargar imagen nueva
    input_file.change(load_img, 
                      input_file, 
                      [original_input_sitk, original_input_img])
    
    # Mostrar imagen nueva
    load_img_button.click(show_img, 
                          [original_input_img, mri_slider, update_true], 
                          [plot_img_original, mri_slider, process_button])
    
    # Actualizar imagen original
    mri_slider.change(show_img, 
                      [original_input_img, mri_slider, update_false], 
                      [plot_img_original])
    
    # Procesar imagen
    process_button.click(fn=process_img, 
                         inputs=[original_input_sitk, brain_slider], 
                         outputs=[brain_img,plot_brain,brain_slider, diagnostic_button])
    
    # Actualizar imagen procesada
    brain_slider.change(show_img,
                        [brain_img, brain_slider, update_false], 
                        [plot_brain])
    
    # Actualizar diagnostico
    diagnostic_button.click(fn=get_diagnosis, 
                            inputs=[brain_img, input_name, input_age, input_phone_num, input_emer_name, input_emer_phone_num,
                                    input_sex, input_MMSE, input_GDSCALE, input_CDR, input_FAQ, input_NPI_Q, input_Diastolic_blood_pressure,
                                    input_Systolic_blood_pressure, input_Body_heigth, input_Body_weight, input_Heart_rate,
                                    input_Respiratory_rate, input_Body_temperature, input_Pluse_oximetry,
                                    input_medications, input_allergies],
                            outputs=[diagnosis_text, output_file, download_box])
    
    # Limpiar campos
    clear_button.click(fn=clear, 
                       outputs=[input_file, plot_img_original, mri_slider, plot_brain, brain_slider, diagnosis_text, process_button, diagnostic_button, download_box])
    


if __name__ == "__main__":
    # demo.queue(concurrency_count=20)
    demo.launch()