from diffusers import DiffusionPipeline, DDIMScheduler
from PIL import Image
import imageio
import torch

import gradio as gr

stable_model_list = [
    "runwayml/stable-diffusion-v1-5",
    "stabilityai/stable-diffusion-2",
    "stabilityai/stable-diffusion-2-base",
    "stabilityai/stable-diffusion-2-1",
    "stabilityai/stable-diffusion-2-1-base"
]

stable_inpiant_model_list = [
    "stabilityai/stable-diffusion-2-inpainting",
    "runwayml/stable-diffusion-inpainting"
]

stable_prompt_list = [
        "a photo of a man.",
        "a photo of a girl."
    ]

stable_negative_prompt_list = [
        "bad, ugly",
        "deformed"
    ]


def stable_diffusion_inpaint(
    dict:str,
    model_path:str,
    prompt:str,
    negative_prompt:str,
    guidance_scale:int,
    num_inference_step:int,
    ):

    image = dict["image"].convert("RGB").resize((512, 512))
    mask_image = dict["mask"].convert("RGB").resize((512, 512))
    pipe = DiffusionPipeline.from_pretrained(
        model_path,
        revision="fp16",
        torch_dtype=torch.float16,
    )
    pipe.to('cuda')
    pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
    pipe.enable_xformers_memory_efficient_attention()

    output = pipe(
        prompt = prompt,
        image = image,
        mask_image=mask_image,
        negative_prompt = negative_prompt,
        num_inference_steps = num_inference_step,
        guidance_scale = guidance_scale,
    ).images

    return output[0]


def stable_diffusion_inpaint_app():
    with gr.Blocks():
        with gr.Row():
            with gr.Column():
                inpaint_image_file = gr.Image(
                    source='upload', 
                    tool='sketch', 
                    elem_id="image_upload", 
                    type="pil", 
                    label="Upload"
                )

                inpaint_model_id = gr.Dropdown(
                    choices=stable_inpiant_model_list, 
                    value=stable_inpiant_model_list[0], 
                    label='Inpaint Model Id'
                )

                inpaint_prompt = gr.Textbox(
                    lines=1, 
                    value=stable_prompt_list[0], 
                    label='Prompt'
                )

                inpaint_negative_prompt = gr.Textbox(
                    lines=1, 
                    value=stable_negative_prompt_list[0], 
                    label='Negative Prompt'
                )

                with gr.Accordion("Advanced Options", open=False):
                    inpaint_guidance_scale = gr.Slider(
                        minimum=0.1, 
                        maximum=15, 
                        step=0.1, 
                        value=7.5, 
                        label='Guidance Scale'
                    )

                    inpaint_num_inference_step = gr.Slider(
                        minimum=1, 
                        maximum=100, 
                        step=1, 
                        value=50, 
                        label='Num Inference Step'
                    )

                inpaint_predict = gr.Button(value='Generator')

            
            with gr.Column():
                output_image = gr.Gallery(label="Outputs")
            
        inpaint_predict.click(
            fn=stable_diffusion_inpaint,
            inputs=[
                inpaint_image_file,
                inpaint_model_id,
                inpaint_prompt,
                inpaint_negative_prompt,
                inpaint_guidance_scale,
                inpaint_num_inference_step,
            ],
            outputs=output_image
        )