Intial

app.py

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+import gradio as gr
+import torch
+import torch.nn as nn
+import torchvision.utils as vutils
+from torchvision import transforms
+from PIL import Image
+
+# --- 1. Define the Brain Structure (Must match your training code!) ---
+class Generator(nn.Module):
+    def __init__(self, ngpu):
+        super(Generator, self).__init__()
+        self.ngpu = ngpu
+        nz = 100 # Noise size
+        self.main = nn.Sequential(
+            # Input is Z (Noise Vector)
+            nn.ConvTranspose2d(nz, 512, 4, 1, 0, bias=False),
+            nn.BatchNorm2d(512),
+            nn.ReLU(True),
+            # 4x4 -> 8x8
+            nn.ConvTranspose2d(512, 256, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(256),
+            nn.ReLU(True),
+            # 8x8 -> 16x16
+            nn.ConvTranspose2d(256, 128, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(128),
+            nn.ReLU(True),
+            # 16x16 -> 32x32
+            nn.ConvTranspose2d(128, 64, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(64),
+            nn.ReLU(True),
+            # 32x32 -> 64x64
+            nn.ConvTranspose2d(64, 3, 4, 2, 1, bias=False),
+            nn.Tanh()
+        )
+
+    def forward(self, input):
+        return self.main(input)
+
+# --- 2. Load the Model ---
+device = torch.device("cpu") # Spaces run on CPU by default
+model = Generator(ngpu=0).to(device)
+
+# Load weights (map_location is vital for CPU spaces)
+model.load_state_dict(torch.load("generator_final.pth", map_location=device))
+model.eval()
+
+# --- 3. The Generation Function ---
+def generate_art(seed, noise_level):
+    torch.manual_seed(int(seed))
+    
+    # Create noise vector
+    noise = torch.randn(1, 100, 1, 1, device=device)
+    
+    # Generate image
+    with torch.no_grad():
+        fake = model(noise).detach().cpu()
+    
+    # Process image (Un-normalize from -1,1 to 0,1)
+    img_tensor = (fake[0] * 0.5) + 0.5
+    img_pil = transforms.ToPILImage()(img_tensor)
+    
+    # Upscale nicely for the web
+    img_pil = img_pil.resize((256, 256), resample=Image.NEAREST)
+    return img_pil
+
+# --- 4. The Interface ---
+with gr.Blocks() as demo:
+    gr.Markdown("# 👾 8-Bit Hero Generator (2025 Edition)")
+    gr.Markdown("I trained this AI from scratch on New Year's Eve 2025 using PyTorch & DCGAN.")
+    
+    with gr.Row():
+        with gr.Column():
+            seed_slider = gr.Slider(0, 9999, label="Character DNA (Seed)", value=42)
+            btn = gr.Button("Generate New Hero")
+        with gr.Column():
+            output_image = gr.Image(label="Result", type="pil")
+
+    btn.click(fn=generate_art, inputs=[seed_slider, seed_slider], outputs=output_image)
+
+demo.launch()

generator_final.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1a087fa0cda3a0761a3e4a465d2a201e7804ce20c50acb47df958a8fd8a3615d
+size 14323408

requirements.txt

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+torch
+torchvision
+gradio