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Create app.py
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app.py
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# Define el modelo y carga los pesos guardados
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model = efficientnet_b0(weights=EfficientNet_B0_Weights.DEFAULT)
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model.classifier[1] = torch.nn.Linear(in_features=1280, out_features=101)
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model.load_state_dict(torch.load('./Model_Food_ProyectoIA'))
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model.eval() # Poner el modelo en modo evaluaci贸n
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# Mueve el modelo a la GPU si est谩 disponible
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device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
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model.to(device)
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# Define las transformaciones
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transform_preprocess = transforms.Compose([
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transforms.Resize(256, interpolation=InterpolationMode.BICUBIC),
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transforms.CenterCrop(224),
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
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])
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# Cargar el conjunto de datos Food-101 para obtener la lista de clases
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image_path = '../../compartida/vision-project/'
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food101_dataset = datasets.Food101(image_path, split='train')
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classes = food101_dataset.classes
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# Funci贸n para predecir la clase de una nueva imagen
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def predict_image(image):
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image = Image.fromarray(image).convert('RGB') # Convertir la imagen cargada a PIL
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image = transform_preprocess(image).unsqueeze(0) # Preprocesar y a帽adir dimensi贸n de batch
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image = image.to(device) # Mover la imagen a la GPU si est谩 disponible
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with torch.no_grad():
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output = model(image) # Realizar la predicci贸n
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prediction = torch.nn.functional.softmax(output[0], dim=0) # Aplicar softmax para obtener probabilidades
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confidences = {classes[i]: float(prediction[i]) for i in range(101)} # Crear diccionario de clases y probabilidades
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return confidences # Devolver las probabilidades de cada clase
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# Crear la interfaz de Gradio
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interface = gr.Interface(
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fn=predict_image,
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inputs=gr.Image(type="numpy"),
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outputs=gr.Label(num_top_classes=3),
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title="Food101 Classifier",
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description="Sube una imagen de comida y el modelo clasificar谩 la imagen.",
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examples=["../../Alan/Proyecto_Food_101/hamb.jpg"] # Reemplaza con rutas de ejemplo
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)
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# Iniciar la interfaz
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interface.launch(share=True)
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