Added new features

app.py

@@ -1,43 +1,63 @@
 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
 
-# '''--------------------------- Preprocesamiento ----------------------------'''
-# tic()
-# 3D U-Net\
+'''--------------------------- 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")
+
 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)
 
-# # Cargar imagen
-# img = utils.load_img('F:/Downloads/ADNI_002_S_0295_MR_MP-RAGE__br_raw_20070525135721811_1_S32678_I55275.nii')
+# MedNet
+device_ids = [7]
+mednet_model = utils.create_mednet(weight_path, device_ids)
 
-# # Extraer cerebro
-# with tf.device("cpu:0"):
-#     brain = utils.brain_stripping(img, model_unet)
-# print(toc())
+# SVM model
+svm_model = pickle.load(open(svm_path, 'rb'))
 
-# '''---------------------------- Procesamiento ------------------------------'''
-# # Med net
-# weight_path = 'resnet_50_23dataset.pth'
-# device_ids = [0]
-# mednet = utils.create_mednet(weight_path, device_ids)
+# Nivel de riesgo
+with tf.device("cpu:0"):
+    prob_model = load_model(prob_model_path)
 
-# # Extraer características
-# features = utils.get_features(brain, mednet)
+# Scaler
+scaler = pickle.load(open(scaler_path, 'rb'))
 
+'''-------------------------------- Funciones ---------------------------------'''
 def load_img(file):
     sitk, array = utils.load_img(file.name)  
     
@@ -66,28 +86,52 @@ def show_img(img, mri_slice, update):
     
 #     return fig, gr.update(visible=True)
 
-def process_img(img, brain_slice, progress=gr.Progress()):
-    progress(880,desc="Processing...")
+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
+    return brain, fig, update_slider, gr.update(visible=True)
 
-def clear():
-    return gr.File.update(value=None), gr.Plot.update(value=None), gr.update(visible=False)
+def get_diagnosis(brain_img, age, MMSE, GDSCALE, CDR, FAQ, NPI, sex):
+    # 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 sex == "Male":
+        sex_dum = 1
+    else:
+        sex_dum = 0
+    
+    scores = np.array([age, MMSE, GDSCALE, CDR, FAQ, NPI, 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"
+    
+    return gr.update(value=diagnosis)
 
-# gr.Textbox.update(placeholder='Ingrese nombre del paciente'), gr.Number.update(value=0), 
+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)
 
-# demo = gr.Interface(fn=load_img,
-#                     inputs=gr.File(file_count="single", file_type=[".nii"]),
-#                     outputs=gr.Plot()
-#                     # outputs='text'
-#                     )
 
-# theme = gr.themes.Base().load('css_new.json')
+'''--------------------------------- Interfaz ---------------------------------'''
 
 with gr.Blocks(theme=gr.themes.Base()) as demo:
     with gr.Row():
@@ -112,11 +156,36 @@ with gr.Blocks(theme=gr.themes.Base()) as demo:
                 input_sex = gr.Dropdown(["Male", "Female"], label="Sex")
                 
             with gr.Tab("Clinical data"):
-                input_MMSE = gr.Number(label='MMSE')
-                input_GDSCALE = gr.Number(label='GDSCALE')
-                input_CDR = gr.Number(label='Global CDR')
-                input_FAQ = gr.Number(label='FAQ Total Score')
-                input_NPI_Q = gr.Number(label='NPI-Q Total Score')      
+                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)')
@@ -142,33 +211,47 @@ with gr.Blocks(theme=gr.themes.Base()) as demo:
                 clear_button = gr.Button(value="Clear")
             
             # Botón para procesar imagen
-            process_button = gr.Button(value="Procesar", visible=False)
+            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")
     
         # Outputs 
         with gr.Column(variant="panel", scale=1):
             gr.Markdown('<h2 style="text-align: center; color:#235784;">MRI visualization</h2>')
             
-            # Plot para imágen original
-            plot_img_original = gr.Plot(label="Imagen MRI original")
+            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) 
             
-            # Slider para imágen original
-            mri_slider = gr.Slider(minimum=0,
-                                    maximum=192,
-                                    value=100,
-                                    step=1,
-                                    label="MRI Slice",
-                                    visible=False) 
-
-            # Plot para imágen procesada
-            plot_brain = gr.Plot(label="Imagen MRI procesada", visible=True)
+            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)
+                # 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...")
     
     # componentes = 
     
@@ -177,6 +260,7 @@ with gr.Blocks(theme=gr.themes.Base()) as demo:
     original_input_img = gr.State()
     brain_img = gr.State()
     
+     
     update_true = gr.State(True)
     update_false = gr.State(False)
     
@@ -191,10 +275,6 @@ with gr.Blocks(theme=gr.themes.Base()) as demo:
                           [original_input_img, mri_slider, update_true], 
                           [plot_img_original, mri_slider, process_button])
     
-    # Limpiar campos
-    clear_button.click(fn=clear, 
-                       outputs=[input_file, plot_img_original, mri_slider])
-    
     # Actualizar imagen original
     mri_slider.change(show_img, 
                       [original_input_img, mri_slider, update_false], 
@@ -203,42 +283,24 @@ with gr.Blocks(theme=gr.themes.Base()) as demo:
     # Procesar imagen
     process_button.click(fn=process_img, 
                          inputs=[original_input_sitk, brain_slider], 
-                         outputs=[brain_img,plot_brain,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.queue(concurrency_count=20)
     demo.launch()
-
-# # Visualización resultados 
-# mri_slice = 100
-
-# # Plot Comparación máscaras
-# fig, axs = plt.subplots(1,2)
-# fig.subplots_adjust(bottom=0.15)
-# fig.suptitle('Comparación Máscaras Obtenidas')
-
-# axs[0].set_title('MRI original')
-# axs[0].imshow(img[mri_slice,:,:],cmap='gray')
-
-# axs[1].set_title('Cerebro extraido con 3D U-Net')
-# axs[1].imshow(brain[mri_slice,:,:],cmap='gray')
-
-
-# # Slider para cambiar slice
-# ax_slider = plt.axes([0.15, 0.05, 0.75, 0.03])
-# mri_slice_slider = Slider(ax_slider, 'Slice', 0, 192, 100, valstep=1)
-
-# def update(val):
-#     mri_slice = mri_slice_slider.val
-    
-#     axs[0].imshow(img[:,:,mri_slice],cmap='gray')
-#     axs[1].imshow(brain[mri_slice,:,:],cmap='gray')
-    
-# # Actualizar plot comparación máscaras
-# mri_slice_slider.on_changed(update)

utils.py

@@ -27,7 +27,6 @@ def import_3d_unet(path_3d_unet):
         return (2. * K.sum(flat_y_true * flat_y_pred) + smoothing_factor) / (K.sum(flat_y_true) + K.sum(flat_y_pred) + smoothing_factor)
     
     # Cargar modelo preentrenado
-    # with tf.device('/cpu:0'):
     model = load_model(path_3d_unet, custom_objects={'dice_coefficient':dice_coefficient, 'iou_score':sm.metrics.IOUScore(threshold=0.5)})
     return model
     
@@ -179,4 +178,14 @@ def get_features(brain, mednet_model):
         features = features.cpu().numpy()
     
         torch.cuda.empty_cache()
-    return features
+    return features
+
+# Classify image
+def get_prediction(features, scores, svm_model, dl_model):
+    prediction = svm_model.predict(features)
+    
+    # x = np.concatenate((scores, prediction))
+    
+    # prob = dl_model.predict(x)
+    
+    return prediction