import os
import torch
import gradio as gr
import spaces
from PIL import Image
import numpy as np
from omegaconf import OmegaConf
import requests
from tqdm import tqdm

def download_file(url, filename):
    response = requests.get(url, stream=True)
    total_size = int(response.headers.get('content-length', 0))
    block_size = 1024
    with open(filename, 'wb') as file, tqdm(
        desc=filename,
        total=total_size,
        unit='iB',
        unit_scale=True,
        unit_divisor=1024,
    ) as progress_bar:
        for data in response.iter_content(block_size):
            size = file.write(data)
            progress_bar.update(size)

def setup_environment():
    os.makedirs("weights", exist_ok=True)
    if not os.path.exists("weights/real-world_ccsr.ckpt"):
        print("Downloading model checkpoint...")
        download_file(
            "https://huggingface.co./camenduru/CCSR/resolve/main/real-world_ccsr.ckpt",
            "weights/real-world_ccsr.ckpt"
        )
    else:
        print("Model checkpoint already exists. Skipping download.")

setup_environment()

from ccsr.models.ccsr import CCSR
from ccsr.utils.util import instantiate_from_config

config = OmegaConf.load("configs/model/ccsr_stage2.yaml")
model = instantiate_from_config(config.model)
ckpt = torch.load("weights/real-world_ccsr.ckpt", map_location="cpu")
model.load_state_dict(ckpt["state_dict"], strict=False)
model.cuda().eval()

@spaces.GPU
@torch.inference_mode()
def infer(image, sr_scale, t_max, t_min, color_fix_type):
    image = Image.open(image).convert("RGB").resize((256, 256), Image.LANCZOS)
    image = torch.from_numpy(np.array(image)).float().cuda() / 127.5 - 1
    image = image.permute(2, 0, 1).unsqueeze(0)

    output = model.super_resolution(
        image,
        sr_scale=sr_scale,
        t_max=t_max,
        t_min=t_min,
        color_fix_type=color_fix_type
    )

    output = ((output.squeeze().permute(1, 2, 0).cpu().numpy() + 1) * 127.5).clip(0, 255).astype(np.uint8)
    return Image.fromarray(output)

interface = gr.Interface(
    fn=infer,
    inputs=[
        gr.Image(type="filepath"),
        gr.Slider(minimum=1, maximum=8, step=1, value=4),
        gr.Slider(minimum=0, maximum=1, step=0.0001, value=0.6667),
        gr.Slider(minimum=0, maximum=1, step=0.0001, value=0.3333),
        gr.Dropdown(choices=["adain", "wavelet", "none"], value="adain"),
    ],
    outputs=gr.Image(type="pil"),
    title="CCSR: Continuous Contrastive Super-Resolution",
)

interface.launch()