import gradio as gr
import torch
import numpy as np
import PIL
import base64
from io import BytesIO
from PIL import Image
# import for face detection
import retinaface

from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
from diffusers import UniPCMultistepScheduler

from modelconfig import ModelConfig
from spiga.inference.framework import SPIGAFramework
import spiga.demo.analyze.track.retinasort.config as cfg

import matplotlib.pyplot as plt
from matplotlib.path import Path
import matplotlib.patches as patches

# Bounding boxes
config = cfg.cfg_retinasort
face_detector = retinaface.RetinaFaceDetector(model=config['retina']['model_name'],
                                              device='cuda' if torch.cuda.is_available() else 'cpu',
                                              extra_features=config['retina']['extra_features'],
                                              cfg_postreat=config['retina']['postreat'])
# Landmark extraction
spiga_extractor = SPIGAFramework(ModelConfig("300wpublic", False))

uncanny_controlnet = ControlNetModel.from_pretrained(
    "multimodalart/uncannyfaces_25K", torch_dtype=torch.float16
)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
    "stabilityai/stable-diffusion-2-1-base", controlnet=uncanny_controlnet, safety_checker=None, torch_dtype=torch.float16
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cuda")

# Generator seed,
generator = torch.manual_seed(0)

canvas_html = "<face-canvas id='canvas-root' style='display:flex;max-width: 500px;margin: 0 auto;'></face-canvas>"
load_js = """
async () => {
const url = "https://huggingface.co/datasets/radames/gradio-components/raw/main/face-canvas.js"
fetch(url)
  .then(res => res.text())
  .then(text => {
    const script = document.createElement('script');
    script.type = "module"
    script.src = URL.createObjectURL(new Blob([text], { type: 'application/javascript' }));
    document.head.appendChild(script);
  });
}
"""
get_js_image = """
async (image_in_img, prompt, image_file_live_opt, live_conditioning) => {
  const canvasEl = document.getElementById("canvas-root");
  const imageData = canvasEl? canvasEl._data : null;
  return [image_in_img, prompt, image_file_live_opt, imageData]
}
"""


def get_bounding_box(image):
    pil_image = Image.fromarray(image)
    face_detector.set_input_shape(pil_image.size[1], pil_image.size[0])
    features = face_detector.inference(pil_image)

    if (features is None) and (len(features['bbox']) <= 0):
        raise Exception("No face detected")
    # get the first face detected
    bbox = features['bbox'][0]
    x1, y1, x2, y2 = bbox[:4]
    bbox_wh = [x1, y1, x2-x1, y2-y1]
    return bbox_wh


def get_landmarks(image, bbox):
    features = spiga_extractor.inference(image, [bbox])
    return features['landmarks'][0]


def get_patch(landmarks, color='lime', closed=False):
    contour = landmarks
    ops = [Path.MOVETO] + [Path.LINETO]*(len(contour)-1)
    facecolor = (0, 0, 0, 0)      # Transparent fill color, if open
    if closed:
        contour.append(contour[0])
        ops.append(Path.CLOSEPOLY)
        facecolor = color
    path = Path(contour, ops)
    return patches.PathPatch(path, facecolor=facecolor, edgecolor=color, lw=4)


def conditioning_from_landmarks(landmarks, size=512):
    # Precisely control output image size
    dpi = 72
    fig, ax = plt.subplots(
        1, figsize=[size/dpi, size/dpi], tight_layout={'pad': 0})
    fig.set_dpi(dpi)

    black = np.zeros((size, size, 3))
    ax.imshow(black)

    face_patch = get_patch(landmarks[0:17])
    l_eyebrow = get_patch(landmarks[17:22], color='yellow')
    r_eyebrow = get_patch(landmarks[22:27], color='yellow')
    nose_v = get_patch(landmarks[27:31], color='orange')
    nose_h = get_patch(landmarks[31:36], color='orange')
    l_eye = get_patch(landmarks[36:42], color='magenta', closed=True)
    r_eye = get_patch(landmarks[42:48], color='magenta', closed=True)
    outer_lips = get_patch(landmarks[48:60], color='cyan', closed=True)
    inner_lips = get_patch(landmarks[60:68], color='blue', closed=True)

    ax.add_patch(face_patch)
    ax.add_patch(l_eyebrow)
    ax.add_patch(r_eyebrow)
    ax.add_patch(nose_v)
    ax.add_patch(nose_h)
    ax.add_patch(l_eye)
    ax.add_patch(r_eye)
    ax.add_patch(outer_lips)
    ax.add_patch(inner_lips)

    plt.axis('off')
    fig.canvas.draw()
    buffer, (width, height) = fig.canvas.print_to_buffer()
    assert width == height
    assert width == size
    buffer = np.frombuffer(buffer, np.uint8).reshape((height, width, 4))
    buffer = buffer[:, :, 0:3]
    plt.close(fig)
    return PIL.Image.fromarray(buffer)


def get_conditioning(image):
    # Steps: convert to BGR and then:
    # - Retrieve bounding box using `dlib`
    # - Obtain landmarks using `spiga`
    # - Create conditioning image with custom `matplotlib` code
    # TODO: error if bbox is too small
    image.thumbnail((512, 512))
    image = np.array(image)
    image = image[:, :, ::-1]
    bbox = get_bounding_box(image)
    landmarks = get_landmarks(image, bbox)
    spiga_seg = conditioning_from_landmarks(landmarks)
    return spiga_seg


def generate_images(image_in_img, prompt, image_file_live_opt='file', live_conditioning=None):
    if image_in_img is None and 'image' not in live_conditioning:
        raise gr.Error("Please provide an image")
    try:
        if image_file_live_opt == 'file':
            conditioning = get_conditioning(image_in_img)
        elif image_file_live_opt == 'webcam':
            base64_img = live_conditioning['image']
            image_data = base64.b64decode(base64_img.split(',')[1])
            conditioning = Image.open(BytesIO(image_data)).convert(
                'RGB').resize((512, 512))

        output = pipe(
            prompt,
            conditioning,
            generator=generator,
            num_images_per_prompt=3,
            num_inference_steps=20,
        )
        return [conditioning] + output.images
    except Exception as e:
        raise gr.Error(str(e))


def toggle(choice):
    if choice == "file":
        return gr.update(visible=True, value=None), gr.update(visible=False, value=None)
    elif choice == "webcam":
        return gr.update(visible=False, value=None), gr.update(visible=True, value=canvas_html)


with gr.Blocks() as blocks:
    gr.Markdown("""
        ## Generate Uncanny Faces with ControlNet Stable Diffusion
        [Check out our blog to see how this was done (and train your own controlnet)](https://huggingface.co/blog/train-your-controlnet)
    """)
    with gr.Row():
        live_conditioning = gr.JSON(value={}, visible=False)
        with gr.Column():
            image_file_live_opt = gr.Radio(["file", "webcam"], value="file",
                                           label="How would you like to upload your image?")
            image_in_img = gr.Image(source="upload", visible=True, type="pil")
            canvas = gr.HTML(None, elem_id="canvas_html", visible=False)

            image_file_live_opt.change(fn=toggle,
                                       inputs=[image_file_live_opt],
                                       outputs=[image_in_img, canvas],
                                       queue=False)
            prompt = gr.Textbox(
                label="Enter your prompt",
                max_lines=1,
                placeholder="best quality, extremely detailed",
            )
            run_button = gr.Button("Generate")
        with gr.Column():
            gallery = gr.Gallery().style(grid=[2], height="auto")
    run_button.click(fn=generate_images,
                     inputs=[image_in_img, prompt,
                             image_file_live_opt, live_conditioning],
                     outputs=[gallery],
                     _js=get_js_image)
    blocks.load(None, None, None, _js=load_js)
    gr.Examples(fn=generate_images,
                examples=[
                    ["./examples/pedro-512.jpg",
                        "Highly detailed photograph of young woman smiling, with palm trees in the background"],
                    ["./examples/image1.jpg",
                        "Highly detailed photograph of a scary clown"],
                    ["./examples/image0.jpg",
                        "Highly detailed photograph of Madonna"],
                ],
                inputs=[image_in_img, prompt],
                outputs=[gallery],
                cache_examples=True)
    gr.Markdown('''
    This Space was trained on synthetic 3D faces to learn how to keep a pose - however it also learned that all faces are synthetic 3D faces, [learn more on our blog](https://huggingface.co/blog/train-your-controlnet), it uses a custom visualization based on SPIGA face landmarks for conditioning.
    ''')
blocks.launch()