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app.py

@@ -1,4 +1,6 @@
+import os
 import utils
+import numpy as np
 import gradio as gr
 import tensorflow as tf
 import matplotlib.pyplot as plt
@@ -7,9 +9,12 @@ from urllib.request import urlretrieve
 
 # '''--------------------------- Preprocesamiento ----------------------------'''
 # tic()
-# 3D U-Net
-urlretrieve("https://dl.dropboxusercontent.com/s/ay5q8caqzlad7h5/unet.h5?dl=0", "unet.h5")
-urlretrieve("https://dl.dropboxusercontent.com/s/otxsgx3e31d5h9i/resnet_50_23dataset.pth?dl=0", "resnet_50_23dataset.pth")
+# 3D U-Net\
+if not os.path.exists("unet.h5"):  
+    urlretrieve("https://dl.dropboxusercontent.com/s/ay5q8caqzlad7h5/unet.h5?dl=0", "unet.h5")
+    
+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")
 
 path_3d_unet = 'unet.h5'
 
@@ -34,26 +39,35 @@ with tf.device("cpu:0"):
 # features = utils.get_features(brain, mednet)
 
 def load_img(file):
-    sitk, array = utils.load_img(file.name)     
-    return sitk, array
+    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):
     fig = plt.figure()
-    plt.imshow(img[:,:,mri_slice], cmap='gray')
+    plt.imshow(img[mri_slice,:,:], cmap='gray')
     
     return fig, gr.update(visible=True)
 
-def show_brain(brain, brain_slice):
-    fig = plt.figure()
-    plt.imshow(brain[brain_slice,:,:], cmap='gray')
+# def show_brain(brain, brain_slice):
+#     fig = plt.figure()
+#     plt.imshow(brain[brain_slice,:,:], cmap='gray')
     
-    return fig, gr.update(visible=True)
+#     return fig, gr.update(visible=True)
 
 def process_img(img, brain_slice):
     with tf.device("cpu:0"):
         brain = utils.brain_stripping(img, model_unet)
         
-        fig, update = show_brain(brain, brain_slice)
+        fig, update = show_img(brain, brain_slice)
         
     return brain, fig, update
 
@@ -68,21 +82,30 @@ def clear():
 #                     # outputs='text'
 #                     )
 
-with gr.Blocks() as demo:
+with gr.Blocks(theme=gr.themes.Base(primary_hue="teal")) as demo:
     with gr.Row():
-        gr.Markdown("""
-                    ![MarineGEO circle logo](Header.svg "MarineGEO logo")
-                    # SIMCI
-                    Interfaz de SIMCI
-                    """)
+        # 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(scale=1):  
-            # Objeto para subir archivo nifti
-            input_name = gr.Textbox(placeholder='Ingrese nombre del paciente', label='Nombre')
-            input_age = gr.Number(label='Edad')
-            
+        with gr.Column(scale=1):
+            with gr.Tab("Personal data"):
+                # Objeto para subir archivo nifti
+                input_name = gr.Textbox(placeholder='Ingrese nombre del paciente', label='Name')
+                input_sex = gr.Dropdown(["Male", "Female"], label="Sex")
+                input_age = gr.Number(label='Age')
+                
+            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_file = gr.File(file_count="single", file_type=[".nii"], label="Archivo Imagen MRI")
                 
@@ -103,12 +126,12 @@ with gr.Blocks() as demo:
             
             # Slider para imágen original
             mri_slider = gr.Slider(minimum=0,
-                                    maximum=166,
+                                    maximum=192,
                                     value=100,
                                     step=1,
                                     label="MRI Slice",
                                     visible=False) 
-            
+
             # Plot para imágen procesada
             plot_brain = gr.Plot(label="Imagen MRI procesada")
             
@@ -153,7 +176,7 @@ with gr.Blocks() as demo:
                          outputs=[brain_img,plot_brain,brain_slider])
     
     # Actualizar imagen procesada
-    brain_slider.change(show_brain,
+    brain_slider.change(show_img,
                         [brain_img, brain_slider], 
                         [plot_brain,brain_slider])