import gradio as gr
import torch
from PIL.ImageDraw import Draw
from diffusers import StableDiffusionPipeline
from PIL import Image, ImageOps
import os

# Load pipeline once
device = "cuda" if torch.cuda.is_available() else "cpu"
checkpoint = "Deci/DeciDiffusion-v2-0"
auth_token = os.environ.get("HF_ACCESS_TOKEN", False)

pipe = StableDiffusionPipeline.from_pretrained(checkpoint, custom_pipeline=checkpoint, torch_dtype=torch.float32, use_auth_token=auth_token)
pipe.unet = pipe.unet.from_pretrained(checkpoint, subfolder='flexible_unet', torch_dtype=torch.float32, use_auth_token=auth_token)
pipe = pipe.to(device)


def read_content(file_path: str) -> str:
    """read the content of target file
    """
    with open(file_path, 'r', encoding='utf-8') as f:
        content = f.read()

    return content


def predict(_prompt: str, _seed: int = 42, _guidance_scale: float = 7.5, _guidance_rescale: float = 0.7, _negative_prompt: str = ""):
    _negative_prompt = [_negative_prompt] if _negative_prompt else None

    output = pipe(prompt=[_prompt],
                  negative_prompt=_negative_prompt,
                  num_inference_steps=16,
                  guidance_scale=_guidance_scale,
                  guidance_rescale=_guidance_rescale,
                  generator=torch.Generator(device).manual_seed(_seed),
                  )
    output_image = output.images[0]

    # Add border beneath the image with Deci logo + prompt
    if len(_prompt) > 52:
        _prompt = _prompt[:52] + "..."

    original_image_height = output_image.size[1]
    output_image = ImageOps.expand(output_image, border=(0, 0, 0, 64), fill='white')
    deci_logo = Image.open('./deci_logo_white.png')
    output_image.paste(deci_logo, (0, original_image_height))
    Draw(output_image).text((deci_logo.size[0], original_image_height + 26), _prompt, (127, 127, 127))
    return output_image


css = '''
.gradio-container {
  max-width: 1100px !important;
  background-image: url(https://huggingface.co/spaces/Deci/Deci-DeciDiffusionClean/resolve/main/background-image.png);
  background-size: cover;
  background-position: center center;
  background-repeat: no-repeat;
}

.footer {margin-bottom: 45px;margin-top: 35px !important;text-align: center;border-bottom: 1px solid #e5e5e5}
.footer>p {font-size: .8rem; display: inline-block; padding: 0 10px;transform: translateY(10px);background: white}
.dark .footer {border-color: #303030}
.dark .footer>p {background: #0b0f19}
.acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%}
@keyframes spin {
    from {
        transform: rotate(0deg);
    }
    to {
        transform: rotate(360deg);
    }
}
'''

demo = gr.Blocks(css=css, elem_id="total-container")
with demo:
    gr.HTML(read_content("header.html"))
    with gr.Row():
        with gr.Column():
            with gr.Row(mobile_collapse=False, equal_height=True):
                prompt = gr.Textbox(placeholder="Your prompt", show_label=False, elem_id="prompt", autofocus=True, lines=3, )

            with gr.Accordion(label="Advanced Settings", open=False):
                with gr.Row(mobile_collapse=False, equal_height=True):
                    seed = gr.Slider(value=42, minimum=1, maximum=100, step=1, label="seed", interactive=True)
                    guidance_scale = gr.Slider(value=7.5, minimum=2, maximum=20, step=0.5, label='guidance_scale', interactive=True)
                    guidance_rescale = gr.Slider(value=0.8, minimum=0.0, maximum=0.99, step=0.05, label='guidance_rescale', interactive=True)


                with gr.Row(mobile_collapse=False, equal_height=True):
                    negative_prompt = gr.Textbox(label="negative_prompt", placeholder="Your negative prompt",
                                                 info="what you don't want to see in the image", lines=3)
            with gr.Row():
                btn = gr.Button(value="Generate!", elem_id="run_button")

        with gr.Column():
            image_out = gr.Image(label="Output", elem_id="output-img", height=400)

    btn.click(fn=predict,
              inputs=[prompt, seed, guidance_scale, guidance_rescale, negative_prompt],
              outputs=[image_out],
              api_name='run')

    gr.HTML(
        """
            <div class="footer">
                <p>Model by <a href="https://deci.ai" style="text-decoration: underline;" target="_blank">Deci.ai</a> - Gradio Demo by 🤗 Hugging Face
                </p>
            </div>
            <div class="acknowledgments">
                    <p><h4>LICENSE</h4>
The model is licensed with a <a href="https://huggingface.co/Deci/DeciDiffusion-v1-0/blob/main/LICENSE-WEIGHTS.md" style="text-decoration: underline;" target="_blank">CreativeML Open RAIL-M</a> license. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in this license. The license forbids you from sharing any content that violates any laws, produce any harm to a person, disseminate any personal information that would be meant for harm, spread misinformation and target vulnerable groups. For the full list of restrictions please <a href="https://huggingface.co/Deci/DeciDiffusion-v1-0/blob/main/LICENSE-WEIGHTS.md" target="_blank" style="text-decoration: underline;" target="_blank">read the license</a></p>
                    <p><h4>Biases and content acknowledgment</h4>
Despite how impressive being able to turn text into image is, beware to the fact that this model may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography and violence. The model was trained on the <a href="https://laion.ai/blog/laion-5b/" style="text-decoration: underline;" target="_blank">LAION-5B dataset</a>, which scraped non-curated image-text-pairs from the internet (the exception being the removal of illegal content) and is meant for research purposes. You can read more in the <a href="https://huggingface.co/Deci/DeciDiffusion-v1-0" style="text-decoration: underline;" target="_blank">model card</a></p>
               </div>
        """
    )

demo.queue(max_size=50).launch()