app.py

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()