Create app.py

app.py

@@ -0,0 +1,64 @@
+import gradio as gr
+import torch
+import torch.nn as nn
+from huggingface_hub import hf_hub_download
+
+# --- 1. DEFINE THE BRAIN ---
+class Generator(nn.Module):
+    def __init__(self):
+        super(Generator, self).__init__()
+        self.net = nn.Sequential(
+            nn.Linear(32, 128), nn.BatchNorm1d(128), nn.LeakyReLU(0.2),
+            nn.Linear(128, 64), nn.BatchNorm1d(64), nn.LeakyReLU(0.2),
+            nn.Linear(64, 1), nn.Sigmoid()
+        )
+    def forward(self, x): return self.net(x)
+
+# --- 2. LOAD MODEL ---
+# We download the weights directly from your model repo
+MODEL_ID = "BeefyDoesAI/Number-E"
+FILENAME = "NumberE.pth"
+
+try:
+    weights_path = hf_hub_download(repo_id=MODEL_ID, filename=FILENAME)
+    # Spaces run on CPU by default (which is fine for this tiny model)
+    device = torch.device("cpu") 
+    model = Generator().to(device)
+    model.load_state_dict(torch.load(weights_path, map_location=device))
+    model.eval()
+except Exception as e:
+    raise RuntimeError(f"Failed to load model: {e}")
+
+# --- 3. GENERATE FUNCTION ---
+def generate(count, digits):
+    count = int(count)
+    digits = int(digits)
+    
+    # Generate Noise
+    noise = torch.rand(count, 32).to(device)
+    
+    # Run Model
+    with torch.no_grad():
+        output = model(noise)
+    
+    # Process output
+    multiplier = 10 ** digits
+    raw = output.flatten().tolist()
+    integers = [str(int(val * multiplier)) for val in raw]
+    
+    return ", ".join(integers)
+
+# --- 4. UI ---
+with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
+    gr.Markdown(f"# Number-E Demo")
+    gr.Markdown("Generating numbers using a custom GAN architecture.")
+    
+    with gr.Row():
+        qty = gr.Slider(1, 100, value=10, label="Quantity", step=1)
+        dig = gr.Slider(1, 10, value=2, label="Digits", step=1)
+        btn = gr.Button("Generate", variant="primary")
+    
+    out = gr.Code(label="Output")
+    btn.click(generate, inputs=[qty, dig], outputs=out)
+
+demo.launch()