app.py

import base64
import io
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from PIL import Image
import torch # Or tensorflow, etc. - Import necessary libraries
# Import your specific AnimeGAN model loading and processing functions
# This is HIGHLY DEPENDENT on the model you choose.
# Example Placeholder - Replace with actual model loading/inference
# from your_animegan_module import load_animegan_model, run_inference

# --- Configuration ---
# Set device (use GPU if available in your HF Space hardware)
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {DEVICE}")

# --- Model Loading ---
# Load your model ONCE when the application starts.
# This is a placeholder - replace with your actual model loading logic.
try:
    # Example: animegan_model = load_animegan_model('path/to/weights_or_hub_id').to(DEVICE)
    animegan_model = None # Replace this line
    # Make sure model is in evaluation mode if applicable (e.g., PyTorch)
    # if animegan_model:
    #    animegan_model.eval()
    print("AnimeGAN model placeholder loaded (Replace with actual model!).")
    # Dummy model for testing structure if real model fails initially
    # class DummyModel:
    #     def __call__(self, img):
    #         print("Dummy model processing...")
    #         # Simple grayscale effect as placeholder
    #         return img.convert("L").convert("RGB")
    # animegan_model = DummyModel()

except Exception as e:
    print(f"Error loading AnimeGAN model: {e}")
    animegan_model = None # Ensure it's None if loading failed


# --- FastAPI App ---
app = FastAPI()

# Define the input data model (expects a base64 encoded image string)
class ImageData(BaseModel):
    image_base64: str

# Define the output data model
class ResultData(BaseModel):
    result_base64: str

@app.get("/")
def read_root():
    return {"message": "AnimeGAN FastAPI Backend is running."}

@app.post("/process-animegan", response_model=ResultData)
async def process_image(data: ImageData):
    if not animegan_model:
         raise HTTPException(status_code=503, detail="AnimeGAN model is not loaded or failed to load.")

    try:
        # 1. Decode Base64 Image
        try:
            image_bytes = base64.b64decode(data.image_base64)
            input_image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
        except Exception as e:
            print(f"Error decoding base64 image: {e}")
            raise HTTPException(status_code=400, detail=f"Invalid base64 image data: {e}")

        print(f"Received image: {input_image.size}, format: {input_image.format}")

        # 2. Preprocess (if required by the model)
        # Example: transform = transforms.Compose([...])
        # processed_input = transform(input_image).unsqueeze(0).to(DEVICE)
        # This depends heavily on your chosen model!
        # For now, we assume the model takes a PIL image directly.
        processed_input = input_image


        # 3. Run Inference
        try:
            print("Running AnimeGAN inference...")
            # Add torch.no_grad() if using PyTorch to save memory
            # with torch.no_grad():
            #    output_tensor = animegan_model(processed_input)
            # Placeholder call:
            output_image_pil = animegan_model(processed_input) # Adjust based on model input/output

            # If model returns a tensor, convert it back to PIL
            # Example:
            # output_image_pil = transforms.ToPILImage()(output_tensor.squeeze().cpu())

            if not isinstance(output_image_pil, Image.Image):
                 raise TypeError(f"Model output was not a PIL Image, but {type(output_image_pil)}")

            print(f"Inference complete. Output size: {output_image_pil.size}")

        except Exception as e:
            print(f"Error during model inference: {e}")
            raise HTTPException(status_code=500, detail=f"Model inference failed: {e}")


        # 4. Convert Result to Base64
        buffer = io.BytesIO()
        # Save as PNG to preserve quality, or JPEG for smaller size
        output_image_pil.save(buffer, format="PNG")
        result_base64 = base64.b64encode(buffer.getvalue()).decode("utf-8")

        return {"result_base64": result_base64}

    except HTTPException as http_exc:
        # Re-raise FastAPI's HTTP exceptions
        raise http_exc
    except Exception as e:
        print(f"An unexpected error occurred: {e}")
        raise HTTPException(status_code=500, detail=f"An internal server error occurred: {e}")

# Optional: Add more endpoints or health checks if needed
@app.get("/health")
def health_check():
    return {"status": "ok", "model_loaded": animegan_model is not None}