inference.py

from pathlib import Path
import torch
from PIL import Image
import base64
import io
from enhancer import ESRGANUpscaler, ESRGANUpscalerCheckpoints

checkpoints = ESRGANUpscalerCheckpoints(
    esrgan=Path("checkpoints/4x-UltraSharp.pth")
)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32

enhancer = ESRGANUpscaler(
    checkpoints=checkpoints,
    device=device,
    dtype=dtype
)

def inference(inputs: dict) -> dict:
    if "image" not in inputs:
        return {"error": "No image provided"}

    image_data = inputs["image"]
    if image_data.startswith("data:image"):
        image_data = image_data.split(",")[1]
    image_bytes = base64.b64decode(image_data)
    input_image = Image.open(io.BytesIO(image_bytes)).convert("RGB")

    enhanced_image = enhancer.upscale(input_image)

    buf = io.BytesIO()
    enhanced_image.save(buf, format="PNG")
    b64 = base64.b64encode(buf.getvalue()).decode("utf-8")

    return {
        "enhanced_image": b64,
        "original_size": input_image.size,
        "enhanced_size": enhanced_image.size
    }