app.py

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

# Load the model and processor
processor = AutoImageProcessor.from_pretrained("caidas/swin2SR-realworld-sr-x4-64-bsrgan-psnr")
model = Swin2SRForImageSuperResolution.from_pretrained("caidas/swin2SR-realworld-sr-x4-64-bsrgan-psnr").to('cpu')

# Define the function for super-resolution
def super_resolve(image):
    # Preprocess the input image
    inputs = processor(images=image, return_tensors="pt").to('cpu')

    # Perform super-resolution
    with torch.no_grad():
        outputs = model(**inputs)
    
    # Get the reconstructed tensor from the outputs
    reconstructed_tensor = outputs.reconstruction

    # Move the tensor to the CPU and convert it to a NumPy array
    image_tensor = reconstructed_tensor.squeeze().cpu()
    image_np = image_tensor.permute(1, 2, 0).numpy()  # Permute to make it HxWxC

    # Rescale the values from [0, 1] to [0, 255]
    image_np = np.clip(image_np, 0, 1)
    image_np = (image_np * 255).astype(np.uint8)

    # Convert the NumPy array back to an image
    output_image = Image.fromarray(image_np)

    return output_image

# Create the Gradio interface
inputs = gr.inputs.Image(type="pil", label="Upload an Image")
outputs = gr.outputs.Image(type="pil", label="Super-Resolved Image")

gr.Interface(fn=super_resolve, inputs=inputs, outputs=outputs, title="Image Super-Resolution with Swin2SR",
             description="Upload an image to upscale it using the Swin2SR model for real-world super-resolution."
).launch()