Update app.py

app.py

@@ -1,146 +1,148 @@
+import sys
+from datetime import date
 import gradio as gr
-import numpy as np
-import random
-from diffusers import DiffusionPipeline
-import torch
+import pandas as pd
+from pickle import load
+from radiomics import featureextractor
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
+extractor3D = featureextractor.RadiomicsFeatureExtractor("3DParams.yaml")
 
-if torch.cuda.is_available():
-    torch.cuda.max_memory_allocated(device=device)
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
-    pipe.enable_xformers_memory_efficient_attention()
-    pipe = pipe.to(device)
-else: 
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
-    pipe = pipe.to(device)
+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
 
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
+def image_classifier(image, segment):
+  features3D = extractor3D.execute(imageFilepath=image, maskFilepath=segment)
+  keys = [key for key in features3D.keys()]
+  values = [value for value in features3D.values()]
+  sortedLists = sorted(list(zip(keys, values)), key=lambda x: x[0])
+  sortedKeys, sortedValues = zip(*sortedLists)    
+  original_stdout = sys.stdout
+  text_stream = TextStream()
+  sys.stdout = text_stream
+  print(*sortedValues, sep="\n")
+  sys.stdout = original_stdout
+  sortedValues = text_stream.data[4:7] + text_stream.data[15:17] + text_stream.data[22:]
+  dataframe = pd.DataFrame(
+    data=sortedValues,
+  )
+  dataframe = dataframe.transpose()
+  prediction = loaded_model.predict_proba(dataframe).tolist()[0]
+  return {"Grade 1": prediction[0], "Grade 2": prediction[1]}
 
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
+def logging(image, label_output):
+  dataframe = pd.DataFrame(data={
+    "Imagen": image,
+    "Grado 1": label_output.values()[0],
+    "Grado 2": label_output.values()[1],
+    "Observación": "",
+    "Fecha": date.today(),
+    "Acción": f"[Descarga]({image})"
+  })
+  dataframe.to_csv(path_or_buf="flagged/log.csv", sep=";")
+  print(dataframe)
 
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-        
-    generator = torch.Generator().manual_seed(seed)
-    
-    image = pipe(
-        prompt = prompt, 
-        negative_prompt = negative_prompt,
-        guidance_scale = guidance_scale, 
-        num_inference_steps = num_inference_steps, 
-        width = width, 
-        height = height,
-        generator = generator
-    ).images[0] 
-    
-    return image
+# Logger = gr.SimpleCSVLogger()
+
+with gr.Blocks(title="Historial de diagnósticos") as ViewingHistory:
+  gr.Dataframe(
+    headers=["Imagen", "Grado 1", "Grado 2", "Observación", "Fecha", "Acción"],
+    datatype=["str", "number", "number", "str", "date", "markdown"],
+    row_count=(3, "dynamic"),
+    col_count=(6, "dynamic"),
+    type="pandas",
+    wrap=True
+  )
 
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
+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
+      )
+  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"),
+      type="pandas",
+      wrap=True,
+      interactive=False
+    )
 
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 520px;
-}
-"""
+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")
 
-if torch.cuda.is_available():
-    power_device = "GPU"
-else:
-    power_device = "CPU"
+# 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}"
 
-with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
-        # Text-to-Image Gradio Template
-        Currently running on {power_device}.
-        """)
-        
-        with gr.Row():
-            
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            
-            run_button = gr.Button("Run", scale=0)
-        
-        result = gr.Image(label="Result", show_label=False)
+# image_file.select(fn=on_select, inputs=None, outputs=None)
 
-        with gr.Accordion("Advanced Settings", open=False):
-            
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-            
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-            
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
-            with gr.Row():
-                
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
-                )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
-                )
-            
-            with gr.Row():
-                
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,
-                )
-                
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=12,
-                    step=1,
-                    value=2,
-                )
-        
-        gr.Examples(
-            examples = examples,
-            inputs = [prompt]
-        )
+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")
+    with gr.Column():
+      label_output = gr.Label(label="Resultado")
+  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])
+        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])
+  submit_button.click(fn=image_classifier, inputs=[image_file, segment_file], outputs=[label_output])
 
-    run_button.click(
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result]
-    )
+# 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"],
+)
 
-demo.queue().launch()
+demo.launch(
+    share=True,
+    debug=True
+)