app/colorize_model.py

"""
Colorize model wrapper using FastAI GAN Colorization Model
Hammad712/GAN-Colorization-Model
"""

from __future__ import annotations

import logging
import os
from typing import Tuple

# Ensure cache directory is set before any HF imports
# (main.py should have set these, but ensure they're set here too)
cache_dir = os.environ.get("HF_HOME", "/tmp/hf_cache")
os.environ["HF_HOME"] = cache_dir
os.environ["TRANSFORMERS_CACHE"] = cache_dir
os.environ["HUGGINGFACE_HUB_CACHE"] = cache_dir
os.environ["HF_HUB_CACHE"] = cache_dir
os.environ["XDG_CACHE_HOME"] = cache_dir

import torch
from PIL import Image
from fastai.vision.all import *
from huggingface_hub import from_pretrained_fastai

from app.config import settings

logger = logging.getLogger(__name__)


def _ensure_cache_dir() -> str:
    cache_dir = os.environ.get("HF_HOME", "/tmp/hf_cache")
    try:
        os.makedirs(cache_dir, exist_ok=True)
    except Exception as exc:
        logger.warning("Could not create cache directory %s: %s", cache_dir, exc)
    # Ensure all cache env vars point to this directory
    os.environ["HF_HOME"] = cache_dir
    os.environ["TRANSFORMERS_CACHE"] = cache_dir
    os.environ["HUGGINGFACE_HUB_CACHE"] = cache_dir
    os.environ["HF_HUB_CACHE"] = cache_dir
    os.environ["XDG_CACHE_HOME"] = cache_dir
    return cache_dir


class ColorizeModel:
    """Colorization model using FastAI GAN model."""

    def __init__(self, model_id: str | None = None) -> None:
        self.cache_dir = _ensure_cache_dir()
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        os.environ.setdefault("OMP_NUM_THREADS", "1")

        # Use FastAI model ID from config or default
        self.model_id = model_id or settings.MODEL_ID
        self.output_caption = getattr(settings, "FASTAI_OUTPUT_CAPTION", "Colorized using GAN-Colorization-Model")

        logger.info("Loading FastAI GAN Colorization model: %s", self.model_id)
        try:
            self.learn = from_pretrained_fastai(self.model_id)
            logger.info("FastAI GAN Colorization model loaded successfully")
        except Exception as e:
            error_msg = (
                f"Failed to load FastAI model '{self.model_id}'. "
                f"Error: {str(e)}\n"
                f"Please check the MODEL_ID environment variable. "
                f"Default model: 'Hammad712/GAN-Colorization-Model'"
            )
            logger.error(error_msg)
            raise RuntimeError(error_msg) from e

    def colorize(self, image: Image.Image, num_inference_steps: int | None = None) -> Tuple[Image.Image, str]:
        """
        Colorize a grayscale or color image using FastAI GAN model.
        
        Args:
            image: PIL Image (grayscale or color)
            num_inference_steps: Ignored for FastAI model (kept for API compatibility)
            
        Returns:
            Tuple of (colorized PIL Image, caption string)
        """
        try:
            original_size = image.size
            
            # Ensure image is RGB
            if image.mode != "RGB":
                image = image.convert("RGB")
            
            # FastAI predict expects a PIL Image
            logger.info("Running FastAI GAN colorization...")
            
            # Use the model's predict method
            # FastAI predict for image models typically returns the output image directly
            # or as the first element of a tuple
            prediction = self.learn.predict(image)
            
            # Extract the colorized image from prediction
            # Handle different return types from FastAI
            if isinstance(prediction, (list, tuple)):
                # If tuple/list, first element is usually the prediction
                colorized = prediction[0] if len(prediction) > 0 else image
            else:
                # Direct return
                colorized = prediction
            
            # Ensure we have a PIL Image
            if not isinstance(colorized, Image.Image):
                # If it's a tensor, convert to PIL
                if isinstance(colorized, torch.Tensor):
                    # Handle tensor conversion
                    if colorized.dim() == 4:
                        colorized = colorized[0]  # Remove batch dimension
                    if colorized.dim() == 3:
                        # Convert CHW to HWC and denormalize if needed
                        colorized = colorized.permute(1, 2, 0).cpu()
                        # Clamp values to [0, 1] if float, or [0, 255] if uint8
                        if colorized.dtype == torch.float32 or colorized.dtype == torch.float16:
                            colorized = torch.clamp(colorized, 0, 1)
                            colorized = (colorized * 255).byte()
                        colorized = Image.fromarray(colorized.numpy(), 'RGB')
                    else:
                        raise ValueError(f"Unexpected tensor shape: {colorized.shape}")
                else:
                    raise ValueError(f"Unexpected prediction type: {type(colorized)}")
            
            # Ensure RGB mode
            if colorized.mode != "RGB":
                colorized = colorized.convert("RGB")
            
            # Resize back to original size if needed
            if colorized.size != original_size:
                colorized = colorized.resize(original_size, Image.Resampling.LANCZOS)
            
            logger.info("Colorization completed successfully")
            return colorized, self.output_caption
            
        except Exception as e:
            logger.error("Error during colorization: %s", str(e))
            raise RuntimeError(f"Colorization failed: {str(e)}") from e