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| 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 im谩gen 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") | |
| 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" | |
| '''---------------------------- 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) | |
| # Redimenci贸n | |
| mri_image = np.transpose(array) | |
| mri_image = np.append(mri_image, np.zeros((192-mri_image.shape[0],256,256,)), axis=0) | |
| # Rotaci贸n | |
| 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 get_diagnosis(brain_img, age, MMSE, GDSCALE, CDR, FAQ, NPI, sex): | |
| # Extracci贸n de caracter铆sticas de imagen | |
| features = utils.get_features(brain_img, mednet_model) | |
| # Clasificaci贸n de imagen | |
| label_img = np.array([svm_model.predict(features)]) | |
| if sex == "Male": | |
| sex_dum = 1 | |
| else: | |
| sex_dum = 0 | |
| scores = np.array([age, MMSE, GDSCALE, CDR, FAQ, NPI, sex_dum, label_img]) | |
| print(scores) | |
| # Normalizaci贸n 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" | |
| return gr.update(value=diagnosis) | |
| 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) | |
| '''--------------------------------- 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>""") | |
| # gr.Markdown(""" | |
| # # SIMCI | |
| # Interfaz de SIMCI | |
| # """) | |
| # Inputs | |
| with gr.Row(): | |
| with gr.Column(variant="panel", scale=1): | |
| 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') | |
| 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') | |
| input_sex = gr.Dropdown(["Male", "Female"], label="Sex") | |
| 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 (掳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) | |
| input_file = gr.File(file_count="single", label="MRI Image File (.nii)") | |
| with gr.Row(): | |
| # Bot贸n para cargar imagen | |
| load_img_button = gr.Button(value="Load") | |
| # Bot贸n para borrar | |
| clear_button = gr.Button(value="Clear") | |
| # Bot贸n para procesar imagen | |
| process_button = gr.Button(value="Process MRI", visible=False, variant="primary") | |
| # Bot贸n para obtener diagnostico | |
| diagnostic_button = gr.Button(value="Get diagnosis", visible=False, variant="primary") | |
| # 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 im谩gen original | |
| plot_img_original = gr.Plot(show_label=False) | |
| # Slider para im谩gen 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 im谩gen procesada | |
| plot_brain = gr.Plot(show_label=False, visible=True) | |
| # Slider para im谩gen 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...") | |
| # componentes = | |
| # 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_age, input_MMSE, input_GDSCALE, input_CDR, input_FAQ, input_NPI_Q, input_sex], | |
| outputs=[diagnosis_text]) | |
| # 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]) | |
| if __name__ == "__main__": | |
| # demo.queue(concurrency_count=20) | |
| demo.launch() | |