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faceID
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import os
import random
import websocket
import uuid
import json
import urllib.request
import urllib.parse
import gradio as gr
from glob import glob
import requests
from pathlib import Path
import base64
from PIL import Image
import time
import io

server_address = "127.0.0.1:8188"
client_id = str(uuid.uuid4())


def queue_prompt(prompt):
    p = {"prompt": prompt, "client_id": client_id}
    data = json.dumps(p).encode('utf-8')
    req =  urllib.request.Request("http://{}/prompt".format(server_address), data=data)
    return json.loads(urllib.request.urlopen(req).read())

def get_image(filename, subfolder, folder_type):
    data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
    url_values = urllib.parse.urlencode(data)
    with urllib.request.urlopen("http://{}/view?{}".format(server_address, url_values)) as response:
        return response.read()

def get_history(prompt_id):
    with urllib.request.urlopen("http://{}/history/{}".format(server_address, prompt_id)) as response:
        return json.loads(response.read())


def get_images(ws, prompt):
    prompt_id = queue_prompt(prompt)['prompt_id']
    output_images = {}
    while True:
        out = ws.recv()
        if isinstance(out, str):
            message = json.loads(out)
            if message['type'] == 'executing':
                data = message['data']
                if data['node'] is None and data['prompt_id'] == prompt_id:
                    break #Execution is done
        else:
            continue #previews are binary data

    history = get_history(prompt_id)[prompt_id]
    for o in history['outputs']:
        for node_id in history['outputs']:
            node_output = history['outputs'][node_id]
            if 'images' in node_output:
                images_output = []
                for image in node_output['images']:
                    image_data = get_image(image['filename'], image['subfolder'], image['type'])
                    images_output.append(image_data)
            output_images[node_id] = images_output

    return output_images

def detect(image):
    img = Path(image).read_bytes() 
    rsp = requests.post(f'http://cv.bytedance.net/aipet_head_det/run/predict', json={
        'data': ['data:image/png;base64,'+
            base64.b64encode(img).decode('utf-8'),
        ]
    })

    return rsp.json()['data'][1]

def clip_save(img_in,coords,path="img.png"):

    img = Image.open(img_in)
    img2 = img.crop((int(coords[0]), int(coords[1]), int(coords[2]), int(coords[3])))
    img2.save(path)


def load_template(img_in,seed):
    seed = int(seed)
    with open(workflow_base,encoding='utf-8') as file:
        template = json.load(file)
    template["14"]["inputs"]["image"] = img_in
    # template["7"]["inputs"]["text"] = animal + templates[style]
    template["3"]["inputs"]["seed"] = seed if seed > 0 else random.randint(1,1e8)
    # template["31"]["inputs"]["seed"] = seed if seed > 0 else random.randint(1,1e8)
    # template["30"]["inputs"]["lora_name"] = loras[style]
    # template["30"]["inputs"]["strength_model"] = w_lora
    # template["30"]["inputs"]["strength_clip"] = w_lora
    # if debug:
        # print(template["6"]["inputs"]["image"],template["7"]["inputs"]["text"],template["9"]["inputs"]["seed"],template["30"]["inputs"]["lora_name"],template["30"]["inputs"]["strength_model"],template["30"]["inputs"]["strength_clip"])
    return template

def generate(img_in,seed):
    seed = int(seed)

    template = load_template(img_in,seed)
    ws = websocket.WebSocket()
    ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id))
    images = get_images(ws, template)

    for node_id in images:
        for image_data in images[node_id]:
            image = Image.open(io.BytesIO(image_data))
            path_out = dir_cache+"/"+str(time.time()).split('.')[0]+"_"+str(template["3"]["inputs"]["seed"])+".png"
            image.save(path_out)

    return image

if __name__ == '__main__':


    workflow_base = "D:/faceID/workflow_api_anime_0306.json"
    dir_cache = "D:/faceID/cache"
    seed = -1
    # debug = True
    demo = gr.Interface(

        fn = generate,
        inputs = [
            gr.Image(type='filepath'),
            # gr.Textbox(label="自定义品种",value="", info="自定义品种,内部调试使用"),
            # gr.Radio(["发财麻将","东北大花","情人玫瑰","天使丘比特","爱心丘比特","美式证件照","新年工笔画","新年唐装","新年糖葫芦","宠物礼盒","生日快乐","雪地工笔画","破壳纪念","爱读书的学霸","米其林大厨","疯狂赛车手","工笔画","圣诞树","圣诞雪人","圣诞老人",], label="风格", info="更多风格规划中，敬请期待~"),
            # gr.Slider(0, 1, value=0.5,step=0.05,label='风格化程度',info='推荐值:低风格化0.3, 中风格化0.5, 高风格化0.7'),
            gr.Textbox(label="随机种子",value=-1, info="-1为随机种子,大于0时为自定义种子")        
        ],
        outputs = ["image"]
    )

    demo.queue(max_size=2)
    demo.launch(share=True)