app.py

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(_