Update app.py

app.py

@@ -3,9 +3,12 @@ import numpy as np
 import pandas as pd
 import gradio as gr
 from pickle import load
+from datetime import datetime
 import sqlalchemy
 from radiomics import featureextractor
 from sqlalchemy.orm import sessionmaker
+import nibabel as nib
+from PIL import Image
 
 extractor3D = featureextractor.RadiomicsFeatureExtractor("3DParams.yaml")
 with open("model.pickle", "rb") as file:
@@ -24,8 +27,41 @@ def validation(username : str, password : str):
 with gr.Blocks(title="Clasificación") as AIModel:
     with gr.Row():
         with gr.Column():
-          image_file = gr.File(file_count="single", file_types=[".nii.gz", ".nii"], type="filepath", label="Imagen")
-          segment_file = gr.File(file_count="single", file_types=[".nii.gz", ".nii"], type="filepath", label="Segmento")
+            image_file = gr.File(file_count="single", file_types=[".nii.gz", ".nii"], type="filepath", label="Imagen")
+            segment_file = gr.File(file_count="single", file_types=[".nii.gz", ".nii"], type="filepath", label="Segmento")
+            dropdown_navigator = gr.Dropdown(value="eje X", choices=["eje X", "eje Y", "eje Z"], filterable=True, type="value", label="Eje")
+            
+            def change_slider(image, slider_value, axis):
+                brain_volume_data  = nib.load(image).get_fdata()
+                if axis == "eje X":
+                    slice = brain_volume_data[slider_value, :, :]
+                    image = Image.fromarray(slice)
+                    image = image.rotate(90)
+                    return image
+                elif axis == "eje Y":
+                    slice = brain_volume_data[:, slider_value, :]
+                    image = Image.fromarray(slice)
+                    image = image.rotate(90)
+                    return image
+                else:
+                    slice = brain_volume_data[:, :, slider_value]
+                    image = Image.fromarray(slice)
+                    return image
+            
+            @gr.render(inputs=[image_file, dropdown_navigator])
+            def preview_image (image, axis):
+                if image == None or image == "":
+                    return
+                if axis == "eje X":
+                    slider = gr.Slider(value=int(nib.load(image).get_fdata().shape[0] // 2), minimum=0, maximum=nib.load(image).get_fdata().shape[0] - 1, label="Control deslizante")
+                elif axis == "eje Y":
+                    slider = gr.Slider(value=int(nib.load(image).get_fdata().shape[1] // 2), minimum=0, maximum=nib.load(image).get_fdata().shape[1] - 1, label="Control deslizante")
+                else:
+                    slider = gr.Slider(value=int(nib.load(image).get_fdata().shape[2] // 2), minimum=0, maximum=nib.load(image).get_fdata().shape[2] - 1, label="Control deslizante")
+            
+                image_preview = gr.Image(label="Previsualización", interactive=False, type="pil")
+                slider.change(fn=change_slider, inputs=[image_file, slider, dropdown_navigator], outputs=[image_preview])
+            
         with gr.Column():
           label_output = gr.Label(label="Resultado")
           comment_output = gr.Textbox(label="Observación", type="text", interactive=True)
@@ -37,22 +73,28 @@ with gr.Blocks(title="Clasificación") as AIModel:
                 with gr.Column():
                     submit_button = gr.Button(value="Enviar", variant="primary")
         with gr.Column():
-            flag_button = gr.Button(value="Marcar")
+            flag_button = gr.Button(value="Marcar")       
 
     def make_prediction(image, label_output, comment_output):
         grade1 = list(label_output.values())[0]
         grade2 = list(label_output.values())[1]
-        engine = sqlalchemy.create_engine("sqlite:///database.db", echo=False)
+        engine = sqlalchemy.create_engine("sqlite:///database_test.db", echo=False)
         Session = sessionmaker(bind=engine)
         session = Session()
+        metadata = sqlalchemy.MetaData()
+        predictions_table = sqlalchemy.Table("Predicciones", metadata, autoload_with=engine)
+
         new_prediction = {
             "Imagen": image,
-            "Grado 1": grade1,
+            "Grado 1":grade1,
             "Grado 2": grade2,
             "Observacion": comment_output,
             "Usuario ID": 1,
+            "Ultima actualizacion": datetime.utcnow(),
+            "Creado el": datetime.utcnow()
         }
-        stmt = predictions.insert().values(**new_prediction)
+        
+        stmt = predictions_table.insert().values(**new_prediction)
         session.execute(stmt)
         session.commit()
         
@@ -70,6 +112,7 @@ with gr.Blocks(title="Clasificación") as AIModel:
 
     flag_button.click(fn=make_prediction, inputs=[image_file, label_output, comment_output])
     submit_button.click(fn=image_classifier, inputs=[image_file, segment_file], outputs=[label_output])
+    
 
 with gr.Blocks(title="Historial de diagnósticos") as ViewingHistory:
     temp = pd.read_sql_table("Predicciones", "sqlite:///database_test.db")
@@ -92,7 +135,7 @@ with gr.Blocks(title="Base de datos") as Database:
     
 with gr.Blocks(title="Información de usuario", delete_cache=[60, 120]) as AdminInformation:
     
-    username : str = ""
+    username : str = "" 
     first_names : str = ""
     last_names : str = ""
     email : str = ""