import torch
from diffusers import FluxPriorReduxPipeline, FluxPipeline
from diffusers.utils import load_image
import gradio as gr
import spaces
# from live_preview_helpers import flux_pipe_call_that_returns_an_iterable_of_images

pipe_prior_redux = FluxPriorReduxPipeline.from_pretrained("black-forest-labs/FLUX.1-Redux-dev", revision="refs/pr/8", torch_dtype=torch.bfloat16).to("cuda")
pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev" , 
    text_encoder=None,
    text_encoder_2=None,
    torch_dtype=torch.bfloat16
)
# pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
# pipe.enable_sequential_cpu_offload()

@spaces.GPU(duration=120)
def enhance_image(image_path, keep_aspect_ratio=False):
    print(image_path)
    image = load_image(image_path)
    print(image.size)
    width, height = image.size if keep_aspect_ratio else (None, None)
    pipe_prior_output = pipe_prior_redux(image)
    images = pipe(
        height=height,
        width=width,
        guidance_scale=2.5,
        num_inference_steps=50,
        generator=torch.Generator("cpu").manual_seed(0),
        **pipe_prior_output,
    ).images
    return images[0]


with gr.Blocks(title="Magnific") as demo:
    gr.HTML("<center><h1>Magnific</h1></center>")
    gr.Markdown("This space is an attempt at replicating the functionality of the [Magnific](https://magnific.ai/) service.")

    with gr.Row():
        with gr.Column():
            image_path = gr.Image(label="Image", type="filepath")
            keep_aspect_ratio = gr.Checkbox(label="Keep Aspect Ratio", value=False)
            submit_btn = gr.Button(value="Submit", variant="primary")
        enhanced_image = gr.Image(label="Enhanced Image", type="pil")

    submit_btn.click(enhance_image, inputs=[image_path, keep_aspect_ratio], outputs=enhanced_image)

demo.queue().launch(share=False)