import gradio as gr
import numpy as np

# import random

# import spaces #[uncomment to use ZeroGPU]
from diffusers import (
    # StableDiffusionControlNetImg2ImgPipeline,
    ControlNetModel,
    StableDiffusionXLControlNetPipeline,
)
import torch

import requests
from fastapi import FastAPI, HTTPException
from PIL import Image
from controlnet_aux import CannyDetector

device = "cuda" if torch.cuda.is_available() else "cpu"
# model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
model_repo_id = "runwayml/stable-diffusion-v1-5"

if torch.cuda.is_available():
    torch_dtype = torch.float16
else:
    torch_dtype = torch.float32

# controlnet = ControlNetModel.from_pretrained(
#     "lllyasviel/sd-controlnet-canny", torch_dtype=torch.float32
# )

controlnet = ControlNetModel.from_pretrained(
    "diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16
)

# pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
# pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
#     model_repo_id,
#     controlnet=controlnet,
#     torch_dtype=torch_dtype,
# ).to(device)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    controlnet=controlnet,
    torch_dtype=torch.float16,
    variant="fp16",
    use_safetensors=True,
).to(device)
# pipe = pipe.to(device)
canny = CannyDetector()

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024


# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(
    image_url,
    # negative_prompt,
    # seed,
    # randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
    progress=gr.Progress(track_tqdm=True),
):
    # if randomize_seed:
    #     seed = random.randint(0, MAX_SEED)

    # generator = torch.Generator().manual_seed(seed)

    # image = pipe(
    #     prompt=prompt,
    #     negative_prompt=negative_prompt,
    #     guidance_scale=guidance_scale,
    #     num_inference_steps=num_inference_steps,
    #     width=width,
    #     height=height,
    #     generator=generator,
    # ).images[0]

    # return image, seed

    width = int(width)
    height = int(height)

    try:
        resp = requests.get(image_url)
        resp.raise_for_status()
    except Exception as e:
        raise HTTPException(400, f"Could not download image: {e}")

    # img = Image.open(io.BytesIO(resp.content)).convert("RGB")
    img = Image.open(requests.get(image_url, stream=True).raw).convert("RGB")
    # img = img.resize((req.width, req.height))
    img = img.resize((width, height))

    control_net_image = canny(img).resize((width, height))

    prompt = (
        "redraw the logo from scratch, clean sharp vector-style, "
        # + STYLE_PROMPTS[req.style_preset]
    )

    output = pipe(
        prompt=prompt,
        negative_prompt=NEGATIVE,
        image=img,
        control_image=control_net_image,
        # strength=req.strength,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        height=height,
        width=width,
    ).images[0]

    return output


examples = [
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    "An astronaut riding a green horse",
    "A delicious ceviche cheesecake slice",
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 640px;
}
"""

NEGATIVE = "blurry, distorted, messy, gradients, background noise"
WIDTH = 512
HEIGHT = 512

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(" # Text-to-Image Gradio Template")

        with gr.Row():
            image_url = gr.Text(
                label="Image URL",
                show_label=False,
                # max_lines=1,
                placeholder="Provide a image URL",
                container=False,
            )

            run_button = gr.Button("Run", scale=0, variant="primary")

        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
            negative_prompt = gr.Label(
                label="Negative prompts",
                # max_lines=1,
                value=NEGATIVE,
                visible=True,
            )

            # seed = gr.Slider(
            #     label="Seed",
            #     minimum=0,
            #     maximum=MAX_SEED,
            #     step=1,
            #     value=0,
            # )

            # randomize_seed = gr.Checkbox(label="Randomize seed", value=True)

            with gr.Row():
                width = gr.Label(
                    label="Width",
                    value=WIDTH,
                    # minimum=256,
                    # maximum=MAX_IMAGE_SIZE,
                    # step=32,
                    # value=1024,  # Replace with defaults that work for your model
                )

                height = gr.Label(
                    label="Height",
                    value=HEIGHT,
                    # minimum=256,
                    # maximum=MAX_IMAGE_SIZE,
                    # step=32,
                    # value=1024,  # Replace with defaults that work for your model
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=8.5,  # Replace with defaults that work for your model
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=25,  # Replace with defaults that work for your model
                )

        # gr.Examples(examples=examples, inputs=[prompt])
    gr.on(
        triggers=[run_button.click, image_url.submit],
        fn=infer,
        inputs=[
            image_url,
            # negative_prompt,
            # seed,
            # randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[
            result,
            # seed,
        ],
    )

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