Update app.py

app.py

@@ -1,96 +1,19 @@
-import json
-import numpy as np
-import gradio as gr
-import onnxruntime
-from PIL import Image
-from torchvision import transforms
-import pandas as pd
-
-# Load model
-ort_session = onnxruntime.InferenceSession("model_new_new_final.onnx", providers=["CPUExecutionProvider"])
-
-# Load classes + metadata
-with open("dat.json", "r", encoding="utf-8") as f:
-    data = json.load(f)
-keys = list(data.keys())
-
-# Preprocessing
-test_tfms = transforms.Compose([
-    transforms.Resize((100, 100)),
-    transforms.ToTensor(),
-    transforms.Normalize(mean=[0.7611, 0.5869, 0.5923], std=[0.1266, 0.1487, 0.1619]),
-])
-
-def softmax(x: np.ndarray) -> np.ndarray:
-    x = x - np.max(x)
-    e = np.exp(x)
-    return e / e.sum()
-
-def probabilities_to_ints(probabilities: np.ndarray, total_sum: int = 100) -> np.ndarray:
-    probs = np.asarray(probabilities, dtype=np.float64)
-    if probs.ndim == 2 and probs.shape[0] == 1:
-        probs = probs
-    elif probs.ndim != 1:
-        raise ValueError(f"Unexpected probabilities shape: {probs.shape}")
-    positive = np.maximum(probs, 0)
-    s = positive.sum()
-    if s == 0:
-        return np.zeros_like(positive, dtype=int)
-    scaled = positive / s * total_sum
-    rounded = np.rint(scaled).astype(int)
-    diff = total_sum - int(rounded.sum())
-    if diff != 0:
-        order = np.argsort(-positive)
-        for i in range(abs(diff)):
-            idx = order[i % len(order)]
-            rounded[idx] += 1 if diff > 0 else -1
-    return rounded
-
-def Predict(image: Image.Image):
-    if not isinstance(image, Image.Image):
-        image = Image.fromarray(image)
-    tensor = test_tfms(image).unsqueeze(0).numpy().astype(np.float32)
-    input_name = ort_session.get_inputs().name
-    out = ort_session.run(None, {input_name: tensor})
-    logits = out
-    if logits.ndim == 2 and logits.shape == 1:
-        logits = logits
-    elif logits.ndim != 1:
-        raise ValueError(f"Unexpected model output shape: {logits.shape}")
-    pred_idx = int(np.argmax(logits))
-    probs = softmax(logits)
-    ints = probabilities_to_ints(probs).astype(int)
-    df = pd.DataFrame({"item": [str(k) for k in keys], "probability": [int(x) for x in ints.tolist()]})
-    disease_name = keys[pred_idx]
-    meta = data.get(disease_name, {})
-    description = meta.get("description", "N/A")
-    symptoms = meta.get("symptoms", "N/A")
-    causes = meta.get("causes", "N/A")
-    treatment = meta.get("treatment-1", meta.get("treatment", "N/A"))
-    return disease_name, description, symptoms, causes, treatment, df
-
-with gr.Blocks(title="Clasificacion de Enfermedades de la Piel") as demo:
-    gr.Markdown(
-        "Este espacio se ha desarrollado..."
-    )
-    inp = gr.Image(type="pil", label="Imagen de la piel")
-    out_name = gr.Textbox(label="Nombre de la Enfermedad")
-    out_desc = gr.Textbox(label="Descripcion")
-    out_symp = gr.Textbox(label="Sintomas")
-    out_causes = gr.Textbox(label="Causas")
-    out_treat = gr.Textbox(label="Tratamiento")
-    bar = gr.BarPlot(
-        x="item",
-        y="probability",
-        y_title="Probabilidad",
-        x_title="Nombre de la Enfermedad",
-        title="Distribucion de Probabilidad",
-        tooltip=["item", "probability"],
-        vertical=False,
-        label="Probabilidades"
-    )
-    btn = gr.Button("Predecir")
-    btn.click(Predict, inputs=inp, outputs=[out_name, out_desc, out_symp, out_causes, out_treat, bar])
-
-if __name__ == "__main__":
-    demo.launch(debug=True)
+import json  import cv2  import numpy as np  import gradio as gr  import onnxruntime  from PIL import Image  from torchvision import transforms  import pandas as pd  
+  # Cargar el modelo ONNX  ort_session = onnxruntime.InferenceSession("model_new_new_final.onnx")  
+  # Abrir archivo JSON  with open('dat.json') as f:      data = json.load(f)  
+  keys = list(data)  
+  # DataFrame de ejemplo  simple = pd.DataFrame(      {          "item": keys,          "probability": [0] * len(keys)      }  )  
+  def Predict(image):      # Preprocesar la imagen      img = cv2.resize(image, (100, 100))  
+      # Convertir el arreglo NumPy de vuelta a una imagen PIL      image = Image.fromarray(image)            # Preprocesar la imagen      img = image.resize((100, 100))  
+      # Definir transformaciones      test_tfms = transforms.Compose([          transforms.Resize((100, 100)),          transforms.ToTensor(),          transforms.Normalize(mean=[0.7611, 0.5869, 0.5923], std=[0.1266, 0.1487, 0.1619])      ])            # Aplicar transformaciones      input_image = test_tfms(img).unsqueeze(0).numpy()  # Agregar dimension de lote y convertir a arreglo numpy  
+      # Preparar tensor de entrada      input_name = ort_session.get_inputs()[0].name      input_dict = {input_name: input_image}  
+      # Ejecutar inferencia      output = ort_session.run(None, input_dict)  
+      # Obtener el indice de la clase predicha      prediction_idx = np.argmax(output)  
+      # Recuperar informacion del JSON basada en la clase predicha      disease_name = keys[prediction_idx]      description = data[disease_name]['description']      symptoms = data[disease_name]['symptoms']      causes = data[disease_name]['causes']      treatment = data[disease_name]['treatment-1']  
+      # Obtener probabilidades para cada clase y convertirlas a enteros      probabilities = output[0]      ints_probabilities = probabilities_to_ints(probabilities)  
+      # Actualizar la probabilidad en el DataFrame      simple["probability"] = ints_probabilities[0]  
+      # Crear grafico de barras para las probabilidades      bar_plot = gr.BarPlot(          value=simple,          x="item",          y="probability",          y_title="Probabilidad",          x_title="Nombre de la Enfermedad",          title="Distribucion de Probabilidad",          tooltip=["item", "probability"],          vertical = False      )  
+      return disease_name, description, symptoms, causes, treatment, bar_plot  
+  # Funcion para convertir probabilidades a enteros  def probabilities_to_ints(probabilities, total_sum=100):      # Filtrar los valores negativos      positive_values = np.maximum(probabilities, 0)            # Encontrar el peso positivo total      total_positive_weight = np.sum(positive_values)            # Calcular probabilidades escaladas para valores positivos      scaled_probabilities = np.zeros_like(probabilities)      if total_positive_weight > 0:          scaled_probabilities = positive_values / total_positive_weight * total_sum            # Redondear las probabilidades escaladas a enteros      rounded_probabilities = np.round(scaled_probabilities).astype(int)            # Ajustar por errores de redondeo      rounding_diff = total_sum - np.sum(rounded_probabilities)      if rounding_diff != 0 and np.sum(positive_values) > 0:          # Agregar la diferencia de redondeo a la clase con mayor peso positivo          max_positive_index = np.argmax(positive_values)          flattened_probabilities = rounded_probabilities.flatten()          flattened_probabilities[max_positive_index] += rounding_diff          rounded_probabilities = np.reshape(flattened_probabilities, rounded_probabilities.shape)            return rounded_probabilities  
+  # Definir la interfaz Gradio  demo = gr.Interface(fn=Predict,                      inputs="image",                      outputs=[                          gr.Textbox(label='Nombre de la Enfermedad', type="text"),                          gr.Textbox(label='Descripcion', type="text"),                          gr.Textbox(label='Sintomas', type="text"),                          gr.Textbox(label='Causas', type="text"),                          gr.Textbox(label='Tratamiento', type="text"),                          "bar_plot"                      ],                      title="Clasificacion de Enfermedades de la Piel",                      description = 'Este espacio se ha desarrollado como parte de una tesis para la Universidad Central de Venezuela con el proposito de realizar diagnosticos precisos sobre una variedad de lesiones cutaneas. Su objetivo es ayudar en la identificacion temprana y precisa de condiciones dermatologicas, incluyendo:\n\n1)Queratosis Actinica \n\n2)Carcinoma Basocelular \n\n3)Dermatofibroma \n\n4)Melanoma \n\n5)Nevus \n\n6)Queratosis Pigmentada Benigna \n\n7)Queratosis Seborreica \n\n8)Carcinoma de Celulas Escamosas \n\n9)Lesion Vascular \n\n')  
+  demo.launch(debug=True)