Update app.py

app.py

@@ -1,6 +1,7 @@
-import gradio as gr
+import os
 import numpy as np
 import pandas as pd
+import gradio as gr
 from pickle import load
 from datetime import date
 from radiomics import featureextractor
@@ -9,17 +10,6 @@ extractor3D = featureextractor.RadiomicsFeatureExtractor("3DParams.yaml")
 
 with open("model.pickle", "rb") as file:
     loaded_model = load(file)
-    
-class TextStream:
-  def __init__(self):
-    self.data : list = []
-      
-  def write(self, s):
-    if s.strip():
-      self.data.append(s.strip())
-  
-  def flush(self):
-    pass
 
 def image_classifier(image, segment):
   features3D = extractor3D.execute(imageFilepath=image, maskFilepath=segment)
@@ -31,9 +21,9 @@ def image_classifier(image, segment):
       dict[key] = [value]
   dataframe = pd.DataFrame(dict).select_dtypes(exclude=["object"]).to_numpy()
   prediction = loaded_model.predict_proba(dataframe).tolist()[0]
-  return {"Grade 1": prediction[0], "Grade 2": prediction[1]}
+  return {"Grado 1": prediction[0], "Grado 2": prediction[1]}
 
-def logging(image, label_output):
+def logging(image, label_output, comment_output):
   grade1 = list(label_output.values())[0]
   grade2 = list(label_output.values())[1]
   now = date.today()
@@ -41,19 +31,32 @@ def logging(image, label_output):
     "Imagen": [image],
     "Grado 1": [grade1],
     "Grado 2": [grade2],
-    "Observación": [""],
+    "Observación": [comment_output],
     "Fecha": [now.strftime("%d/%m/%Y")],
-    "Acción": [f"\"\"\"[Descargar]({image})\"\"\""]
+    "Acción": [f"[Descargar]({image})"]
   }
   dataframe = pd.DataFrame(data=dictionary)
   dataframe.to_csv(path_or_buf="log.csv", sep=";", mode="a", index=False)
-  print(dataframe)
-
+  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()
+
 with gr.Blocks(title="Historial de diagnósticos") as ViewingHistory:
-  dataframe = pd.read_csv(filepath_or_buffer="log.csv", sep=";")
-  gr.Dataframe(
+  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"],
@@ -64,21 +67,31 @@ with gr.Blocks(title="Historial de diagnósticos") as ViewingHistory:
 with gr.Blocks(title="Base de datos") as Database:
   with gr.Row():
     with gr.Column():
-      gr.Dropdown(
-          choices=["Usuarios", "Imágenes", "Resultados"],
-          filterable=True, label="Tabla",
-          scale=2
+      dropdown = gr.Dropdown(
+        choices=["Usuarios", "Imágenes", "Resultados"],
+        filterable=False, label="Tabla",
+        scale=2
       )
+    with gr.Column():
+      button = gr.Button(value="Buscar")
+      
   with gr.Row():
-    gr.Dataframe(
-      headers=["Imagen", "Grado 1", "Grado 2", "Observación", "Fecha"],
-      datatype=["str", "number", "number", "str", "date"],
-      row_count=(3, "dynamic"),
-      col_count=(5, "dynamic"),
+    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():
@@ -104,20 +117,21 @@ with gr.Blocks(title="Información de usuario") as AdminInformation:
 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="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="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.Column():
-          clear_button = gr.ClearButton(value="Borrar", components=[image_file, segment_file, label_output])
+          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")
-  flag_button.click(fn=logging, inputs=[image_file, label_output])
+  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(
@@ -139,7 +153,36 @@ demo = gr.TabbedInterface(
   tab_names=["Aplicación", "Historial", "Base de datos", "Administrador"],
 )
 
-demo.launch(
+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")
+  
+  def password_validation(emailInput, passwordInput):
+    
+    if emailInput == "" or passwordInput == "":
+      return gr.Info("Ingresar correo eléctronico y contraseña")
+    
+    userTable = pd.read_sql_table("users", "sqlite:///database.db")
+    userRow = userTable[userTable["email"] == emailInput]
+    
+    if userRow.empty:
+      return gr.Warning("Correo electrónico o contraseña incorrecta")
+    
+    userPassword = userRow["password"].to_numpy().tolist()[0]
+    print(userPassword, passwordInput, userPassword == passwordInput)
+    
+    if userPassword == passwordInput:
+      Login = demo.launch(inline=True)
+    else:
+      return gr.Warning("Correo electrónico o contraseña incorrecta")
+  
+  button.click(password_validation, inputs=[email, password])
+
+
+
+Login.launch(
     share=True,
     debug=True
 )