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app.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+import gradio as gr
+
+# Define the VAE model
+class ConvVAE(nn.Module):
+    def __init__(self, input_channels=3, latent_dim=16):
+        super(ConvVAE, self).__init__()
+        self.latent_dim = latent_dim
+        self.enc_conv1 = nn.Conv2d(input_channels, 32, kernel_size=3, stride=2, padding=1)
+        self.enc_conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1)
+        self.enc_conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
+        self.fc_mu = nn.Linear(5120, latent_dim)
+        self.fc_logvar = nn.Linear(5120, latent_dim)
+        self.fc_decode = nn.Linear(latent_dim, 5120)
+        self.dec_conv1 = nn.ConvTranspose2d(128, 64, kernel_size=3, stride=1, padding=1)
+        self.dec_conv2 = nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1, output_padding=1)
+        self.dec_conv3 = nn.ConvTranspose2d(32, 3, kernel_size=3, stride=2, padding=1, output_padding=(0,1))
+
+    def reparameterize(self, mu, logvar):
+        std = torch.exp(0.5 * logvar)
+        eps = torch.randn_like(std)
+        return mu + eps * std
+
+    def forward(self, x):
+        x = F.relu(self.enc_conv1(x))
+        x = F.relu(self.enc_conv2(x))
+        x = F.relu(self.enc_conv3(x))
+        x = x.view(x.size(0), -1)
+        mu = self.fc_mu(x)
+        logvar = self.fc_logvar(x)
+        z = self.reparameterize(mu, logvar)
+        return self.decode(z)
+
+    def decode(self, z):
+        x = F.relu(self.fc_decode(z))
+        x = x.view(x.size(0), 128, 4, 10)
+        x = F.relu(self.dec_conv1(x))
+        x = F.relu(self.dec_conv2(x))
+        x = self.dec_conv3(x)
+        return F.softmax(x, dim=1)
+
+# Load model
+model = ConvVAE()
+model.load_state_dict(torch.load("vae_supertux.pth", map_location=torch.device("cpu")))
+model.eval()
+
+def generate_map(seed: int = None):
+    if seed:
+        torch.manual_seed(seed)
+    z = torch.randn(1, model.latent_dim)
+    with torch.no_grad():
+        output = model.decode(z)  # Shape: (1, 3, 15, 40)
+    output = output.squeeze(0).argmax(dim=0)
+    grid = output.cpu().numpy()
+    padded_grid = np.vstack([np.zeros((5, grid.shape[1]), dtype=int), grid])  # Append 5 rows of zeros
+    return ["".join(map(str, row)) for row in padded_grid]  # Convert each row to a string
+
+gr.Interface(
+    fn=generate_map,
+    inputs=gr.Number(label="Seed"),
+    outputs=gr.JSON(label="Generated Map Grid"),
+    title="VAE Level Generator",
+    description="Returns a 20x40 grid as a list of strings where 0=air, 1=ground, 2=lava"
+).launch()

requirements.txt

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+torch
+gradio

vae_supertux.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3e887c368f7ef92170aed65ef3f9eddf719f6f972178dbcdd66b01ad097171f4
+size 1755214