app.py

"""
PixelArtGen — Gradio Web UI

Interactive UI to generate pixel art from text prompts using
BitPixelLM — a 1.58-bit ternary transformer (BitNet b1.58).

Launch:
    python app.py
    Then open http://localhost:7860 in your browser.
"""

import sys
import json
import torch
import numpy as np
import gradio as gr
from pathlib import Path
from PIL import Image

sys.path.insert(0, str(Path(__file__).parent))

from model.tokenizer import PaletteTokenizer
from model.text_encoder import TextTokenizer, TextEncoder
from model.bit_pixel_decoder import BitPixelLMDecoder, BitPixelLM

# ─── Config ──────────────────────────────────────────────────────
DATA_DIR = Path(r"D:\PixelArtGen_Data\processed")
CHECKPOINT_PATH = Path("checkpoints_bit/best.pt")

# ─── Global state (loaded once) ─────────────────────────────────
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = None
palette_tok = None
text_tok = None


def load_tokenizers():
    """Load shared tokenizers."""
    global palette_tok, text_tok
    palette_tok = PaletteTokenizer(palette_path=str(DATA_DIR / "palette_256.npy"))
    with open(DATA_DIR / "vocab.json") as f:
        vocab = json.load(f)
    text_tok = TextTokenizer(vocab)


def load_model():
    """Load the BitPixelLM model from checkpoint."""
    global model
    if model is not None:
        return model

    if not CHECKPOINT_PATH.exists():
        raise FileNotFoundError(
            f"Checkpoint not found: {CHECKPOINT_PATH}\n"
            "BitPixelLM is still training — check back once training completes."
        )

    checkpoint = torch.load(str(CHECKPOINT_PATH), map_location=device, weights_only=False)
    model_args = checkpoint.get("args", {})

    d_model = model_args.get("d_model", 256)
    nhead = model_args.get("nhead", 8)
    text_layers = model_args.get("text_layers", 3)
    pixel_layers = model_args.get("pixel_layers", 6)
    dim_ff = model_args.get("dim_ff", 512)
    dropout = model_args.get("dropout", 0.1)
    max_text_len = model_args.get("max_text_len", 32)

    text_encoder = TextEncoder(
        vocab_size=text_tok.vocab_size,
        d_model=d_model,
        nhead=nhead,
        num_layers=text_layers,
        dim_feedforward=dim_ff,
        max_seq_len=max_text_len,
        dropout=dropout,
    )

    pixel_decoder = BitPixelLMDecoder(
        vocab_size=palette_tok.vocab_size,
        d_model=d_model,
        nhead=nhead,
        num_layers=pixel_layers,
        dim_feedforward=dim_ff,
        img_size=32,
        dropout=dropout,
    )
    m = BitPixelLM(text_encoder, pixel_decoder).to(device)

    m.load_state_dict(checkpoint["model_state_dict"])
    m.eval()
    model = m
    return model


def generate(
    prompt: str,
    temperature: float,
    top_k: int,
    top_p: float,
    num_samples: int,
    scale: int,
):
    """Generate pixel art from a text prompt."""
    if not prompt.strip():
        raise gr.Error("Please enter a prompt.")

    if model is None:
        raise gr.Error(
            "BitPixelLM is not loaded yet. "
            "It may still be training — check back once training completes."
        )

    text_tokens = text_tok.encode(prompt).unsqueeze(0).to(device)

    # Warn about unknown words (still generates, but quality may suffer)
    words = prompt.lower().strip().split()
    unknown = [w for w in words if w not in text_tok.word2idx and w not in ("<pad>", "<sos>", "<eos>", "<unk>")]
    
    images = []
    try:
        for _ in range(int(num_samples)):
            with torch.no_grad():
                generated_tokens = model.generate(
                    text_tokens,
                    sos_token=palette_tok.sos_token,
                    eos_token=palette_tok.eos_token,
                    temperature=temperature,
                    top_k=top_k,
                    top_p=top_p,
                )

            token_list = generated_tokens[0].cpu().tolist()
            img_array = palette_tok.decode_tokens(token_list)
            img = Image.fromarray(img_array, "RGB")

            # Upscale with nearest-neighbor for crisp pixels
            s = int(scale)
            if s > 1:
                img = img.resize((32 * s, 32 * s), Image.NEAREST)

            images.append(img)
    except Exception as e:
        raise gr.Error(f"Generation failed: {e}")

    if unknown:
        gr.Warning(
            f"Unknown words treated as <unk>: {', '.join(unknown)}. "
            f"Try using words from the vocabulary list below."
        )

    return images


# ─── Build UI ────────────────────────────────────────────────────

# Load vocabulary dynamically from processed data
def _load_vocab_words():
    try:
        with open(DATA_DIR / "vocab.json") as f:
            vocab = json.load(f)
        return sorted([w for w in vocab if not w.startswith("<")])
    except Exception:
        return ["pixel", "art", "sword", "red", "blue", "green"]

VOCAB_WORDS = _load_vocab_words()

EXAMPLE_PROMPTS = [
    "a red pixel art sword",
    "a green pixel art dragon",
    "a purple pixel art crystal",
    "a blue pixel art knight",
    "a gold pixel art castle",
    "a red pixel art phoenix",
    "a dark pixel art skeleton",
    "a teal pixel art wizard",
    "a silver pixel art robot",
    "a orange pixel art fox",
]


def build_ui():
    with gr.Blocks(
        title="PixelArtGen",
        theme=gr.themes.Soft(primary_hue="purple"),
        css="""
        .gallery-item img { image-rendering: pixelated !important; }
        .output-gallery img { image-rendering: pixelated !important; }
        #gallery img { image-rendering: pixelated !important; }
        """,
    ) as app:
        gr.Markdown(
            """
            # PixelArtGen
            ### Generate 32x32 pixel art from text prompts

            Powered by **BitPixelLM** — a custom 1.58-bit ternary transformer built from scratch
            using BitNet b1.58 with RMSNorm, SwiGLU, and 2D positional encoding.
            7.3M parameters (75% ternary weights at 1.58 bits per weight).
            """
        )

        with gr.Row():
            with gr.Column(scale=1):
                prompt = gr.Textbox(
                    label="Prompt",
                    placeholder="a red pixel art sword",
                    lines=2,
                )
                with gr.Row():
                    generate_btn = gr.Button("Generate", variant="primary", scale=2)
                    num_samples = gr.Slider(1, 8, value=4, step=1, label="Samples")

                with gr.Accordion("Advanced Settings", open=False):
                    temperature = gr.Slider(
                        0.1, 2.0, value=0.8, step=0.05,
                        label="Temperature",
                        info="Lower = more deterministic, higher = more creative"
                    )
                    top_k = gr.Slider(
                        0, 256, value=40, step=1,
                        label="Top-K",
                        info="0 = disabled. Limits sampling to top K tokens."
                    )
                    top_p = gr.Slider(
                        0.1, 1.0, value=0.9, step=0.05,
                        label="Top-P (Nucleus)",
                        info="Cumulative probability threshold for sampling."
                    )
                    scale = gr.Slider(
                        1, 16, value=8, step=1,
                        label="Upscale Factor",
                        info="8x = 256x256, 16x = 512x512"
                    )

                gr.Markdown(
                    f"**Known vocabulary:** {', '.join(VOCAB_WORDS)}"
                )

            with gr.Column(scale=2):
                gallery = gr.Gallery(
                    label="Generated Pixel Art",
                    columns=4,
                    rows=2,
                    height=520,
                    object_fit="contain",
                    elem_id="gallery",
                )

        gr.Markdown("### Examples")
        gr.Examples(
            examples=EXAMPLE_PROMPTS,
            inputs=[prompt],
            label="Click to try",
        )

        gr.Markdown(
            """
            ---
            **Architecture:**
            BitPixelLM treats pixel art generation as language modeling — each pixel is a token from a 256-color palette,
            generated left-to-right, top-to-bottom via a causal transformer with 2D positional encoding and cross-attention to text.
            Uses 1.58-bit ternary weights (BitNet b1.58) with RMSNorm and SwiGLU for extreme parameter efficiency.
            """
        )

        # Wire up the generate button
        generate_btn.click(
            fn=generate,
            inputs=[prompt, temperature, top_k, top_p, num_samples, scale],
            outputs=gallery,
        )

        # Also generate on Enter
        prompt.submit(
            fn=generate,
            inputs=[prompt, temperature, top_k, top_p, num_samples, scale],
            outputs=gallery,
        )

    return app


# ─── Main ────────────────────────────────────────────────────────
if __name__ == "__main__":
    print("Loading tokenizers...")
    load_tokenizers()
    print(f"  Palette: {palette_tok.vocab_size} tokens")
    print(f"  Text: {text_tok.vocab_size} words")
    print(f"  Device: {device}")

    # Load BitPixelLM
    print(f"Loading BitPixelLM from {CHECKPOINT_PATH}...")
    try:
        load_model()
        print(f"  BitPixelLM loaded successfully.")
    except FileNotFoundError as e:
        print(f"  {e}")
        print(f"  UI will launch but generation will be unavailable until training completes.")
    except Exception as e:
        print(f"  Failed to load BitPixelLM: {e}")

    print("\nLaunching UI...")
    app = build_ui()
    app.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,
        inbrowser=True,
    )