Create app.py

app.py

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+import gradio as gr
+import torch
+import torch.nn as nn
+import numpy as np
+from PIL import Image
+import torchvision.transforms as transforms
+
+# Modelo autoencoder
+class Autoencoder(nn.Module):
+    def __init__(self):
+        super(Autoencoder, self).__init__()
+        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=2, padding=1)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1)
+        self.fc1 = nn.Linear(64 * 16 * 16, 16)
+        self.fc2 = nn.Linear(16, 64 * 16 * 16)
+        self.conv3 = nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1, output_padding=1)
+        self.conv4 = nn.ConvTranspose2d(32, 1, kernel_size=3, stride=2, padding=1, output_padding=1)
+
+    def encode(self, x):
+        z = torch.tanh(self.conv1(x))
+        z = torch.tanh(self.conv2(z))
+        z = z.view(z.size(0), -1)
+        z = torch.tanh(self.fc1(z))
+        return z
+
+    def decode(self, x):
+        z = torch.tanh(self.fc2(x))
+        z = z.view(z.size(0), 64, 16, 16)
+        z = torch.tanh(self.conv3(z))
+        z = torch.sigmoid(self.conv4(z))
+        return z
+
+    def forward(self, x):
+        return self.decode(self.encode(x))
+
+# Cargar el modelo
+model = Autoencoder()
+model.load_state_dict(torch.load("autoencoder.pth", map_location=torch.device("cpu")))
+model.eval()
+
+# Transformación de entrada
+transform = transforms.Compose([
+    transforms.Grayscale(),
+    transforms.Resize((64, 64)),
+    transforms.ToTensor()
+])
+
+# Función de inferencia
+def detectar_anomalia(imagen):
+    with torch.no_grad():
+        img_tensor = transform(imagen).unsqueeze(0)  # Añadir batch
+        reconstruida = model(img_tensor).squeeze(0).squeeze(_