[
    {
      "file_path": "videos/00000001.mp4",
      "text": "A group of young men in suits and sunglasses are walking down a city street.",
      "type": "video"
    },
    {
      "file_path": "videos/00000002.mp4",
      "text": "A notepad with a drawing of a woman on it.",
      "type": "video"
    }
    .....
]

export DATASET_NAME=“” # 训练数据所在目录
export DATASET_META_NAME=“datasets/Minimalism/metadata_add_width_height.json” # 训练文件地址

import json
import requests


def post_diffusion_transformer(diffusion_transformer_path, url='http://127.0.0.1:7860', token=None):
    datas = json.dumps({
        "diffusion_transformer_path": diffusion_transformer_path
    })
    head = {
        'Authorization': token
    }
    r = requests.post(f'{url}/easyanimate/update_diffusion_transformer', data=datas, headers=head, timeout=15000)
    data = r.content.decode('utf-8')
    return data

def post_update_edition(edition, url='http://0.0.0.0:7860',token=None):
    head = {
        'Authorization': token
    }

    datas = json.dumps({
        "edition": edition
    })
    r = requests.post(f'{url}/easyanimate/update_edition', data=datas, headers=head)
    data = r.content.decode('utf-8')
    return data
  
if __name__ == '__main__':
    url = '<eas-service-url>'
    token = '<eas-service-token>'

    # -------------------------- #
    #  Step 1: update edition
    # -------------------------- #
    edition = "v2"
    outputs = post_update_edition(edition,url = url,token=token)
    print('Output update edition: ', outputs)

    # -------------------------- #
    #  Step 2: update edition
    # -------------------------- #
    # 默认路径不可修改
    diffusion_transformer_path = "/mnt/models/Diffusion_Transformer/EasyAnimateV2-XL-2-512x512"
    outputs = post_diffusion_transformer(diffusion_transformer_path, url = url, token=token)
    print('Output update edition: ', outputs)

import base64
import json
import sys
import time
from datetime import datetime
from io import BytesIO

import cv2
import requests
import base64


def post_infer(is_image, length_slider, url='http://127.0.0.1:7860',token=None):
    head = {
        'Authorization': token
    }

    datas = json.dumps({
        "base_model_path": "none",
        "motion_module_path": "none",
        "lora_model_path": "none", 
        "lora_alpha_slider": 0.55, 
        "prompt_textbox": "This video shows Mount saint helens, washington - the stunning scenery of a rocky mountains during golden hours - wide shot. A soaring drone footage captures the majestic beauty of a coastal cliff, its red and yellow stratified rock faces rich in color and against the vibrant turquoise of the sea.", 
        "negative_prompt_textbox": "Strange motion trajectory, a poor composition and deformed video, worst quality, normal quality, low quality, low resolution, duplicate and ugly, strange body structure, long and strange neck, bad teeth, bad eyes, bad limbs, bad hands, rotating camera, blurry camera, shaking camera", 
        "sampler_dropdown": "Euler", 
        "sample_step_slider": 30, 
        "width_slider": 672, 
        "height_slider": 384, 
        "is_image": is_image,
        "length_slider": length_slider,
        "cfg_scale_slider": 6,
        "seed_textbox": 43,
    })
    r = requests.post(f'{url}/easyanimate/infer_forward', data=datas, headers=head,timeout=1500)
    data = r.content.decode('utf-8')
    return data

if __name__ == '__main__':
    # initiate time
    now_date    = datetime.now()
    time_start  = time.time()  
    
    url = '<eas-service-url>'
    token = '<eas-service-token>'

    # -------------------------- #
    #  Step 3: infer
    # -------------------------- #
    is_image = False
    length_slider = 27
    outputs = post_infer(is_image, length_slider, url = url, token=token)
    
    # Get decoded data
    outputs = json.loads(outputs)
    base64_encoding = outputs["base64_encoding"]
    decoded_data = base64.b64decode(base64_encoding)

    if is_image or length_slider == 1:
        file_path = "1.png"
    else:
        file_path = "1.mp4"
    with open(file_path, "wb") as file:
        file.write(decoded_data)
        
    # End of record time
    time_end = time.time()  
    time_sum = (time_end - time_start) % 60 
    print('# --------------------------------------------------------- #')
    print(f'#   Total expenditure: {time_sum}s')
    print('# --------------------------------------------------------- #')