import cv2
import numpy as np

import base64
import requests
from common.logger import config

version = config.get('default', 'version')
if version == "local":
    webui_url = 'http://192.168.1.57:57860/'
else:
    webui_url = 'http://0.0.0.0:57860/'

def encode_numpy_to_base64(img):
    retval, bytes = cv2.imencode('.png', img)
    encoded_image = base64.b64encode(bytes).decode('utf-8')
    return encoded_image


def webui_img2img(img, mask, prompt='', denoising_strength=0.35):
    url = f"{webui_url}sdapi/v1/img2img"
    request_dict = {
            "prompt": prompt,
            "negative_prompt": '(nsfw:1.5), ng_deepnegative_v1_75t, (badhandv4:1.2), (worst quality:2), (low quality:2), (normal quality:2), lowres, bad anatomy, bad hands, ((monochrome)), ((grayscale)) watermark, bad_pictures,easynegative',
            "sampler_name": "DPM++ 2M Karras",
            "batch_size": 1,
            "steps": 30,
            "width": img.shape[1],
            "height": img.shape[0],
            "cfg_scale": 7.0,
            "seed": 123456789,
            "mask_blur": 5,
            "init_images": [
                encode_numpy_to_base64(img)
            ],
            "inpaint_full_res": False,
            "inpainting_fill": 1,
            "inpainting_mask_invert": 0,
            "mask": encode_numpy_to_base64(mask),
            "denoising_strength": denoising_strength,
            "alwayson_scripts": {
            }
        }
    response = requests.post(url=url, json=request_dict)
    ret_json = response.json()
    result = ret_json['images'][0]
    img = cv2.imdecode(np.frombuffer(base64.b64decode(result.split(",", 1)[0]), np.uint8), cv2.IMREAD_COLOR)
    return img

def webui_super_res_img(img, ratio):
    url = f"{webui_url}sdapi/v1/extra-single-image"
    request_dict = {
                      "resize_mode": 0,
                      "show_extras_results": False,
                      "gfpgan_visibility": 0,
                      "codeformer_visibility": 1,
                      "codeformer_weight": 1,
                      "upscaling_resize": ratio,
                      "upscaler_1": "8x_NMKD-Superscale_150000_G",
                      "upscale_first": False,
                      "image": encode_numpy_to_base64(img)
                    }
    response = requests.post(url=url, json=request_dict)
    ret_json = response.json()
    result = ret_json['image']
    img = cv2.imdecode(np.frombuffer(base64.b64decode(result), np.uint8), cv2.IMREAD_COLOR)
    return img


def webui_tag_by_clip(img):
    url = f"{webui_url}sdapi/v1/interrogate"
    request_dict = {
                      "image": encode_numpy_to_base64(img),
                      "model": "clip"
                    }
    response = requests.post(url=url, json=request_dict)
    ret_json = response.json()
    return ret_json['caption']