Update app.py

app.py

@@ -1,188 +1,170 @@
-import os
+import sqlite3
 import numpy as np
 import pandas as pd
 import gradio as gr
 from pickle import load
-from datetime import date
+import sqlalchemy
 from radiomics import featureextractor
+from sqlalchemy.orm import sessionmaker
 
 extractor3D = featureextractor.RadiomicsFeatureExtractor("3DParams.yaml")
-
 with open("model.pickle", "rb") as file:
     loaded_model = load(file)
-
-def image_classifier(image, segment):
-  features3D = extractor3D.execute(imageFilepath=image, maskFilepath=segment)
-  dict = {}
-  for key, value in zip(features3D.keys(), features3D.values()):
-    if isinstance(value, np.ndarray):
-      dict[key] = [value.tolist()]
-    else:
-      dict[key] = [value]
-  dataframe = pd.DataFrame(dict).select_dtypes(exclude=["object"]).to_numpy()
-  prediction = loaded_model.predict_proba(dataframe).tolist()[0]
-  return {"Grado 1": prediction[0], "Grado 2": prediction[1]}
-
-def logging(image, label_output, comment_output):
-  grade1 = list(label_output.values())[0]
-  grade2 = list(label_output.values())[1]
-  now = date.today()
-  dictionary = {
-    "Imagen": [image],
-    "Grado 1": [grade1],
-    "Grado 2": [grade2],
-    "Observación": [comment_output],
-    "Fecha": [now.strftime("%d/%m/%Y")],
-    "Acción": [f"[Descargar]({image})"]
-  }
-  dataframe = pd.DataFrame(data=dictionary)
-  dataframe.to_csv(path_or_buf="log.csv", sep=";", mode="a", index=False)
-  return dataframe
-# Logger = gr.SimpleCSVLogger()
-
-def on_selected(event : gr.SelectData):
-  return f"You selected {event.value} at {event.index} from {event.target}"
-
-gradioDataframe = gr.DataFrame()
+    
+def validation(username : str, password : str):
+    if username == "" or password == "":
+        return False
+    table = pd.read_sql_table(table_name="Usuarios", con="sqlite:///database_test.db")
+    row = table[table["Usuario"] == username]
+    if row.empty:
+        return False
+    password_db = row["Contraseña"].to_numpy().tolist()[0]
+    return password == password_db
+
+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")
+        with gr.Column():
+          label_output = gr.Label(label="Resultado")
+          comment_output = gr.Textbox(label="Observación", type="text", interactive=True)
+    with gr.Row():
+        with gr.Column():
+            with gr.Row():
+                with gr.Column():
+                    clear_button = gr.ClearButton(value="Borrar", components=[image_file, segment_file, label_output, comment_output])
+                with gr.Column():
+                    submit_button = gr.Button(value="Enviar", variant="primary")
+        with gr.Column():
+            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)
+        Session = sessionmaker(bind=engine)
+        session = Session()
+        new_prediction = {
+            "Imagen": image,
+            "Grado 1": grade1,
+            "Grado 2": grade2,
+            "Observacion": comment_output,
+            "Usuario ID": 1,
+        }
+        stmt = predictions.insert().values(**new_prediction)
+        session.execute(stmt)
+        session.commit()
+        
+    def image_classifier(image, segment):
+        features3D = extractor3D.execute(imageFilepath=image, maskFilepath=segment)
+        dict = {}
+        for key, value in zip(features3D.keys(), features3D.values()):
+            if isinstance(value, np.ndarray):
+                dict[key] = [value.tolist()]
+            else:
+                dict[key] = [value]
+        temp = pd.DataFrame(dict).select_dtypes(exclude=["object"]).to_numpy()
+        prediction = loaded_model.predict_proba(temp).tolist()[0]
+        return {"Grado 1": prediction[0], "Grado 2": prediction[1]}
+
+    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:
-  dataframe = pd.DataFrame({
-    "Imagen": [""],
-    "Grado 1": [0],
-    "Grado 2": [0],
-    "Observación": [""],
-    "Fecha": [""],
-    "Acción": [""],
-  })
-  if os.path.isfile("log.csv"):
-    dataframe = pd.read_csv(filepath_or_buffer="log.csv", sep=";")      
-  gradioDataframe = gr.Dataframe(
-    value=dataframe,
-    headers=["Imagen", "Grado 1", "Grado 2", "Observación", "Fecha", "Acción"],
-    datatype=["str", "number", "number", "str", "date", "markdown"],
-    type="pandas",
-    wrap=True
-  )
+    temp = pd.read_sql_table("Predicciones", "sqlite:///database_test.db")
+    gr.Dataframe(temp, type="pandas", wrap=True, interactive=False)
 
 with gr.Blocks(title="Base de datos") as Database:
-  with gr.Row():
-    with gr.Column():
-      dropdown = gr.Dropdown(
-        choices=["Usuarios", "Imágenes", "Resultados"],
-        filterable=False, label="Tabla",
-        scale=2
-      )
-    with gr.Column():
-      button = gr.Button(value="Buscar")
-      
-  with gr.Row():
-    dataframe = gr.Dataframe(
-      type="pandas",
-      wrap=True,
-      interactive=False
-    )
-  
-  def on_selected(selected_value):
-    if selected_value == "Usuarios":
-      db_df = pd.read_sql_table("users", "sqlite:///database.db")
-    elif selected_value == "Imágenes":
-      db_df = pd.read_sql_table("images", "sqlite:///database.db")
-    else:
-      db_df = pd.read_sql_table("predictions", "sqlite:///database.db")
-    return db_df
-  
-  button.click(on_selected, dropdown, dataframe)
-
-with gr.Blocks(title="Información de usuario") as AdminInformation:
-  with gr.Row():
-    with gr.Column():
-      gr.Image(interactive=True)
-    with gr.Column():
-      gr.Textbox(value="Nicolás Andrés", label="Nombres", interactive=True, show_copy_button=True, type="text", max_lines=1, container=False)
-      gr.Textbox(value="niplinig", label="Usuario", interactive=False, show_copy_button=True, type="text", max_lines=1, container=False)
-      gr.Textbox(value="Administrador", label="Rol", interactive=False, show_copy_button=True, type="text", max_lines=1, container=False)
-    with gr.Column():
-      gr.Textbox(value="Plaza Iñiguez", label="Apellidos", interactive=True, show_copy_button=True, type="text", max_lines=1, container=False)
-      gr.Textbox(value="niplinig@espol.edu.ec", label="Correo electrónico", interactive=True, show_copy_button=True, type="email", max_lines=1, container=False)
-      gr.Textbox(value="0939552946", label="Número de teléfono", interactive=True, show_copy_button=True, type="text", max_lines=1, container=False)
-  with gr.Row():
-    gr.Button(value="Guardar")
-
-# def on_select(event : gr.SelectData):
-  # print(event.value, event.index, event.target, sep=",")
-  # return f"You selected {event.value} at {event.index} from {event.target}"
-
-# image_file.select(fn=on_select, inputs=None, outputs=None)
-
-with gr.Blocks(title="Clasificación") as MyModel:
-  with gr.Row():
-    with gr.Column():
-      image_file = gr.File(file_count="single", file_types=[".nii.gz", ".nii"], type="binary", label="Imagen")
-      segment_file = gr.File(file_count="single", file_types=[".nii.gz", ".nii"], type="binary", label="Segmento")
-    with gr.Column():
-      label_output = gr.Label(label="Resultado")
-      comment_output = gr.Textbox(label="Observación", type="text", interactive=True)
-  with gr.Row():
-    with gr.Column():
-      with gr.Row():
+    with gr.Row():
         with gr.Column():
-          clear_button = gr.ClearButton(value="Borrar", components=[image_file, segment_file, label_output, comment_output])
+          dropdown = gr.Dropdown(choices=["Usuarios", "Predicciones"],
+          filterable=False, label="Tabla")
         with gr.Column():
-          submit_button = gr.Button(value="Enviar", variant="primary")  
-    with gr.Column():
-      flag_button = gr.Button(value="Marcar")
-  flag_button.click(fn=logging, inputs=[image_file, label_output, comment_output], outputs=[gradioDataframe])
-  submit_button.click(fn=image_classifier, inputs=[image_file, segment_file], outputs=[label_output])
-
-# MainModel = gr.Interface(
-#   fn=image_classifier,
-#   inputs=[image_file, segment_file],
-#   outputs=[gr.Label(label="Resultado")],
-#   title="Clasificación",
-#   description="Clasificación",
-#   allow_flagging="manual",
-#   flagging_callback=Logger,
-#   submit_btn="Enviar",
-#   stop_btn="Suspender",
-#   clear_btn="Borrar",
-#   show_progress="full",
-# )
-
-demo = gr.TabbedInterface(
-  interface_list=[MyModel, ViewingHistory, Database, AdminInformation],
-  tab_names=["Aplicación", "Historial", "Base de datos", "Administrador"],
-)
-
-with gr.Blocks(title="Inicio de sesión") as Login:
-  with gr.Column():
-    email = gr.Textbox(label="Correo electrónico", interactive=True, type="email", max_lines=1)
-    password = gr.Textbox(label="Contraseña", interactive=True, type="password", max_lines=1)
-    button = gr.Button("Acceder", variant="primary")
+          button = gr.Button(value="Buscar", variant="primary")
+    with gr.Row():
+        dataframe = gr.Dataframe(type="pandas", wrap=True, interactive=False)
   
-  def password_validation(emailInput, passwordInput):
+    def on_selected(selected_value):
+        return pd.read_sql_table(selected_value, "sqlite:///database_test.db")
     
-    if emailInput == "" or passwordInput == "":
-      return gr.Info("Ingresar correo eléctronico y contraseña")
+    button.click(on_selected, dropdown, dataframe)
     
-    userTable = pd.read_sql_table("users", "sqlite:///database.db")
-    userRow = userTable[userTable["email"] == emailInput]
+with gr.Blocks(title="Información de usuario", delete_cache=[60, 120]) as AdminInformation:
     
-    if userRow.empty:
-      return gr.Warning("Correo electrónico o contraseña incorrecta")
+    username : str = ""
+    first_names : str = ""
+    last_names : str = ""
+    email : str = ""
+    phone : int = 0
+    is_admin : bool = False
     
-    userPassword = userRow["password"].to_numpy().tolist()[0]
-    print(userPassword, passwordInput, userPassword == passwordInput)
+    table = pd.read_sql_table(table_name="Usuarios", con="sqlite:///database_test.db")
+    row = table[table["ID"] == 1]
+    
+    def make_interactive(input_first_names, input_username, input_last_names, input_email, input_phone):
+        return (
+            gr.Textbox(label="Nombres", interactive=True, max_lines=1),
+            gr.Textbox(label="Usuario", interactive=True, max_lines=1),
+            gr.Textbox(label="Apellidos", interactive=True, max_lines=1),
+            gr.Textbox(label="Correo electrónico", interactive=True, max_lines=1),
+            gr.Textbox(label="Número de teléfono", interactive=True, max_lines=1)
+        )
+    
+    def save(input_first_names, input_username, input_last_names, input_email, input_phone):
+        
+        connection = sqlite3.connect("database_test.db")
+        cursor = connection.cursor()
+        data = cursor.execute(f'''UPDATE Usuarios SET Nombres = '{input_first_names}', Usuario = '{input_username}', Apellidos = '{input_last_names}', "Correo electronico" = '{input_email}', Telefono = '{input_phone}'  WHERE ID==1;''')
+        connection.commit()
+        connection.close()
+        
+        return (
+            gr.Textbox(value=input_first_names, label="Nombres", interactive=False, max_lines=1),
+            gr.Textbox(value=input_username, label="Usuario", interactive=False, max_lines=1),
+            gr.Textbox(value=input_last_names, label="Apellidos", interactive=False, max_lines=1),
+            gr.Textbox(value=input_email, label="Correo electrónico", interactive=False, max_lines=1),
+            gr.Textbox(value=input_phone, label="Número de teléfono", interactive=False, max_lines=1)
+        )
+    
+    if not row.empty:
+        username = row["Usuario"].to_numpy().tolist()[0]
+        first_names = row["Nombres"].to_numpy().tolist()[0]
+        last_names = row["Apellidos"].to_numpy().tolist()[0]
+        email = row["Correo electronico"].to_numpy().tolist()[0]
+        phone = row["Telefono"].to_numpy().tolist()[0]
+        is_admin = row["Es Administrador"].to_numpy().tolist()[0]
+    
+    with gr.Row():
+        with gr.Column():
+            input_profile_image = gr.Image(interactive=False)
+        with gr.Column():
+            input_first_names = gr.Textbox(value=first_names, label="Nombres", interactive=False, type="text", max_lines=1)
+            input_username = gr.Textbox(value=username, label="Usuario", interactive=False, type="text", max_lines=1)
+            #gr.Textbox(value="Administrador", label="Rol", interactive=False, show_copy_button=True, type="text", max_lines=1, container=False)
+        with gr.Column():
+            input_last_names = gr.Textbox(value=last_names, label="Apellidos", interactive=False, type="text", max_lines=1)
+            input_email = gr.Textbox(value=email, label="Correo electrónico", interactive=False, type="email", max_lines=1)
+            input_phone = gr.Textbox(value=phone, label="Número de teléfono", interactive=False, type="text", max_lines=1)
+    with gr.Row():
+        with gr.Row():
+            edit_button = gr.Button(value="Editar")
+            save_button = gr.Button(value="Guardar", variant="primary")
+    
+    edit_button.click(make_interactive, inputs=[input_first_names, input_username, input_last_names, input_email, input_phone],
+                      outputs=[input_first_names, input_username, input_last_names, input_email, input_phone])
+    save_button.click(save, inputs=[input_first_names, input_username, input_last_names, input_email, input_phone], outputs=[input_first_names, input_username, input_last_names, input_email, input_phone])
     
-    if userPassword == passwordInput:
-      Login = demo.launch(inline=True)
-    else:
-      return gr.Warning("Correo electrónico o contraseña incorrecta")
-  
-  button.click(password_validation, inputs=[email, password])
-
 
+Demo = gr.TabbedInterface(
+  interface_list=[AIModel, ViewingHistory, Database, AdminInformation],
+  tab_names=["Aplicación", "Historial", "Base de datos", "Administrador"],
+)
 
-Login.launch(
-    share=True,
-    debug=True
-)
+if __name__ == "__main__":
+    Demo.launch(
+        share=True,
+        debug=True,
+        inbrowser=True,
+        auth=validation
+    )