app.py

import gradio as gr
from PIL import Image
import torch
from transformers import AutoImageProcessor, Swin2SRForImageSuperResolution

# ---- Device ----
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# ---- Model IDs / paths ----
PRETRAINED_ID = "caidas/swin2SR-realworld-sr-x4-64-bsrgan-psnr"          # crystal clear
FINETUNED_ID = "swin2sr_div2k_finetuned_x4_1000steps"                     # smooth (local folder in repo)

# ---- Load processors ----
processor_pre = AutoImageProcessor.from_pretrained(PRETRAINED_ID)
processor_ft  = AutoImageProcessor.from_pretrained(FINETUNED_ID, local_files_only=True)

# ---- Load models ----
model_pre = Swin2SRForImageSuperResolution.from_pretrained(PRETRAINED_ID).to(device)
model_ft  = Swin2SRForImageSuperResolution.from_pretrained(FINETUNED_ID, local_files_only=True).to(device)

model_pre.eval()
model_ft.eval()


ABOUT_TEXT = """
    **About this tool**

    This is a free image enhancement tool that uses Swin2SR to reconstruct and refine image detail at the pixel level (“pixel refabrication”).  
    I built it because many of my own photos are distorted or only exist as low‑quality copies on social media.

    **Tip for social media images**

    If your only copy is on Instagram, open Instagram on a computer, view the photo in full‑screen, take a screenshot, and crop it tightly around the image before uploading here.

    **Usage notes**

    Processing and queue times can occasionally exceed ten minutes, especially during heavy use.  
    If this tool gets regular traffic, I plan to upgrade to a stronger GPU backend.

    Please reach out if you need any help or have questions.
    """  


def swin2sr_upscale(input_image: Image.Image, mode: str):
    if input_image is None:
        return None, "No image uploaded."

    try:
        input_image = input_image.convert("RGB")
        w_lr, h_lr = input_image.size

        if mode == "Smooth (Over Smoothing | tuned_1)":
            model = model_ft
            processor = processor_ft
        else:
            model = model_pre
            processor = processor_pre

        inputs = processor(images=input_image, return_tensors="pt").to(device)
        with torch.no_grad():
            outputs = model(**inputs)

        sr_tensor = outputs.reconstruction.squeeze().clamp(0, 1)
        sr_array = (sr_tensor.mul(255).byte().cpu().permute(1, 2, 0).numpy())
        sr_image = Image.fromarray(sr_array).convert("RGB")

        w_sr, h_sr = sr_image.size
        msg = (
            f"Original resolution: {w_lr}×{h_lr} pixels\n"
            f"Super-resolved: {w_sr}×{h_sr} pixels "
            f"(scale factors: {w_sr / w_lr:.1f}×, {h_sr / h_lr:.1f}×)"
        )
        return sr_image, msg

    except Exception as e:
        return None, f"Error: {type(e).__name__}: {e}"
'''

# ---- Inference function ----
def swin2sr_upscale(input_image: Image.Image, mode: str):
    """
    Run 4x super-resolution using Swin2SR.
    mode: "Crystal clear (pretrained)" or "Smooth (fine-tuned)".
    """
    if input_image is None:
        return None
    
    # original size
    w_lr, h_lr = input_image.size
    
    if mode == "Smooth (fine-tuned)":
        model = model_ft
        processor = processor_ft
    else:
        model = model_pre
        processor = processor_pre

    inputs = processor(images=input_image, return_tensors="pt").to(device)

    with torch.no_grad():
        outputs = model(**inputs)

    sr_tensor = outputs.reconstruction.squeeze().clamp(0, 1)
    sr_array = (sr_tensor.mul(255).byte().cpu().permute(1, 2, 0).numpy())
    sr_image = Image.fromarray(sr_array)

    # ---ensure JPEG-compatible mode ---
    sr_image = sr_image.convert("RGB")
    # ----OG and new size ---
    w_sr, h_sr = sr_image.size
    msg = (
        f"Original resolution: {w_lr}×{h_lr} pixels\n"
        f"Super-resolved: {w_sr}×{h_sr} pixels "
        f"(scale factors: {w_sr / w_lr:.1f}×, {h_sr / h_lr:.1f}×)"
    )

  

    return sr_image, msg
'''
# ---- Gradio UI ----
with gr.Blocks() as demo:
    gr.Markdown("# Image Super-Resolution (Swin2SR x4)")
    gr.Markdown(
        "Choose **Clear (pretrained)** for the original Swin2SR model, "
        "or **Smooth (fine-tuned)** for the Swin2SR version we fine-tuned on DIV2K patches."
    )

    with gr.Row():
        input_image = gr.Image(type="pil", label="Upload low-res image")
        output_image = gr.Image(type="pil",format="jpeg", label="4x Super-resolved image")
    
    
    res_info = gr.Markdown("Resolution info.")
     # ---- info / help box ----
    gr.Markdown(ABOUT_TEXT)
    
    mode_dropdown = gr.Dropdown(
        label="Style",
        choices=["Clear (OFtB)", "Smooth (Over Smoothing | tuned_1)"],
        value="Clear (OFtB)",
        interactive=True,
    )

    run_btn = gr.Button("Upscale")

    run_btn.click(
        fn=swin2sr_upscale,
        inputs=[input_image, mode_dropdown],
        outputs=[output_image, res_info]
    )

if __name__ == "__main__":
    demo.launch()