call_assistant_api.py

# install the lib of : https://github.com/pengwangucla/cv_utils
from vis_common import *
import vis_utils as v_uts

import json
import os
import time
import base64
import requests
from openai import OpenAI
from tenacity import retry, wait_random_exponential, stop_after_attempt, wait_fixed
from GPT_prompts import REWRITE_PROMPT_0


API_KEY = os.environ.get("BYTE_API_KEY")
class EditActionClassifier():
    def __init__(self):
        self.client = OpenAI()
        self.assistant_key = "asst_57vfLupV8VCsCZx0BJOppSnw"
        self.thread = self.client.beta.threads.create()

    @retry(wait=wait_fixed(10), stop=stop_after_attempt(3))
    def infer(self, edit_action):
        message = self.client.beta.threads.messages.create(
            thread_id=self.thread.id,
            role="user",
            content=edit_action
        )
        run = self.client.beta.threads.runs.create( 
          thread_id=self.thread.id,
          assistant_id=self.assistant_key,
        )
        pbar = tqdm(total=100)
        while run.status != 'completed':
          run = self.client.beta.threads.runs.retrieve(
            thread_id=self.thread.id,
            run_id=run.id
          )
          time.sleep(.5) # Sleep and check run status again
          pbar.update(1)
          pbar.set_description('Run Status: ' + run.status)
          if run.status == 'failed':
            break

        if run.status == 'failed':
          print("Run failed")
          return ""

        messages = self.client.beta.threads.messages.list(
          thread_id=self.thread.id
        )
        result = messages.data[0].content[0].text.value
        if "edit class" in results:
          try:
              class_name = json.loads(result)["edit class"]
          except Exception as e:
              print(f"{result}, can not be load by json")
              class_name = result

        return class_name


def test_personal_dalle3():
    # Call the API
    client = OpenAI()
    response = client.images.generate(
        model="dall-e-3",
        prompt="a cute cat with a hat on",
        size="1792x1024",
        quality="standard",
        n=1,
    )
    image_url = response.data[0].url
    image_url = "https://oaidalleapiprodscus.blob.core.windows.net/private/org-S0JkO5ALwPh1E3YpnKFiS7Gh/user-gJLc6S6Gmp2NCFBcEyZNgRNz/img-RDqXwfARPT6LSovnZXbMyzSO.png?st=2024-01-12T18%3A54%3A32Z&se=2024-01-12T20%3A54%3A32Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2024-01-11T22%3A56%3A51Z&ske=2024-01-12T22%3A56%3A51Z&sks=b&skv=2021-08-06&sig=BoeIYYvxu5Cnt4YfM53Az7EYlEYUTkWfXQCKrKaDWD0%3D"
    # Download the image from the URL
    image_response = requests.get(image_url)

    # Check if the request was successful
    if image_response.status_code == 200:
        # Save the image to a file
        with open('cute_cat_with_hat.jpg', 'wb') as file:
            file.write(image_response.content)
    else:
        print("Failed to download the image.")


def test_call_gpt4_api():
    from langchain_community.chat_models import AzureChatOpenAI
    from langchain.schema import HumanMessage

    BASE_URL = "https://search-us.byteintl.net/gpt/openapi/online/v2/crawl/"
    DEPLOYMENT_NAME = "gpt-4-0613"
    DEPLOYMENT_NAME = "gpt-4-1106-preview"
    model = AzureChatOpenAI(
            openai_api_base=BASE_URL,
            openai_api_version="2023-03-15-preview",
            deployment_name=DEPLOYMENT_NAME,
            openai_api_key=API_KEY,
            openai_api_type="azure",
            temperature=0.5,
            max_tokens=512,
        )

    content = REWRITE_PROMPT_0.format(prompt1="Create a diptych image that consists two images. \
The left image is front-view of lying real white 12 years old man. \
The right image keep everything the same but change the background of the subject to europe.")
    generate_log = model([HumanMessage(content=content)]).content
    print(generate_log)


def test_call_gpt4v_api():
    from langchain_community.chat_models import AzureChatOpenAI
    from langchain.schema import HumanMessage

    BASE_URL = "https://search-us.byteintl.net/gpt/openapi/online/v2/crawl/"
    DEPLOYMENT_NAME = "openai_gpt-4-vision" # gptv 或 openai_gpt-4-vision
    model = AzureChatOpenAI(
        openai_api_base=BASE_URL,
        openai_api_version="2023-07-01-preview",
        deployment_name=DEPLOYMENT_NAME,
        openai_api_key=API_KEY,
        openai_api_type="azure",
        temperature=0.5,
        max_tokens=512,
      )

    image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
    input_ip = {
        "url": image_url
    }

    image_path = "./imgs/dataset.jpg"
    base64_image = v_uts.encode_b64(image_path)
    input_ip = {
        "url": f"data:image/jpeg;base64,{base64_image}"
    }

    generate_log = model([HumanMessage(content=[
        {
            "type": "text",
            "text": "What’s in this image?"
        },
        {
            "type": "image_url",
            "image_url": input_ip
        }
    ])])
    print(generate_log)


# curl --location --request POST 'https://search.bytedance.net/gpt/openapi/online/v2/crawl?ak=业务方AK' \
# --header 'Content-Type: application/json' \
# --header 'X-TT-LOGID: 请求方logID，方便定位问题' \    
# --data-raw '{
#     "prompt": "A poster of Microsoft", // 文字描述画图内容
#     "size": "1024x1024",               // 图片大小。只支持 1024x1024 / 1024x1792 / 1792x1024
#     "quality": "standard",             // 图片质量，默认standard
#     "style": "vivid",                  // 图片风格，模型vivid
#     "n": 1,
#     "model": "dall-e-3"                // 对应模型名称，必填
# }'

# // response
# {
#     "created": 1702889995,
#     "data": [
#         {
#             "url": "https://dalleprodsec.blob.core.windows.net/private/images/0811eacd-bf25-4961-814f-36d7f453907c/generated_00.png?se=2023-12-19T09%3A00%3A09Z&sig=cIRz7je1Qbjlt5GjeyLGKoxPRFggr7NAxLSeeCuGyYk%3D&ske=2023-12-22T11%3A18%3A13Z&skoid=e52d5ed7-0657-4f62-bc12-7e5dbb260a96&sks=b&skt=2023-12-15T11%3A18%3A13Z&sktid=33e01921-4d64-4f8c-a055-5bdaffd5e33d&skv=2020-10-02&sp=r&spr=https&sr=b&sv=2020-10-02",
#             "revised_prompt": "A designed poster featuring the logo of a prominent technology company, accompanied by various emboldened text denoting the company's name and a motivational slogan. The distinct, four rectangular logo in bright colors is situated at the center of the poster, against a plain background. The composition strikes a balance between minimalism and impact, typifying the company's powerful image in the global technology industry."
#         }
#     ]
# }

def test_call_dalle3_api():
    """ openai==1.2.0, httpx==0.23.0
    """
    from openai import AzureOpenAI
    BASE_URL = "https://search-va.byteintl.net/gpt/openapi/online/v2/crawl"
    DEPLOYMENT_NAME = "dall-e-3"
    API_KEY = "hpjWvnz7wM2mzDg4Ggnt96xcOjeYcktj" 
    client = AzureOpenAI(
        api_version="2023-12-01-preview",  
        api_key=API_KEY,
        azure_endpoint=BASE_URL)

    result = client.images.generate(
        model=DEPLOYMENT_NAME, # the name of your DALL-E 3 deployment
        prompt="A soldier girl holding a USA flag",
        n=1,
        size="1024x1024",
        quality="standard",
        style="vivid"
    )
    image_url = result.data[0].url
    image_response = requests.get(image_url)

    # Check if the request was successful
    if image_response.status_code == 200:
        # Save the image to a file
        with open('.jpg', 'wb') as file:
            file.write(image_response.content)
    else:
        print("Failed to download the image.")


if __name__ == "__main__":
    # classifier = EditActionClassifier()
    # class_name = classifier.infer("Remove the background of the image")
    # print(class_name)
    # test_personal_dalle3()

    # test_call_gpt4_api()
    # test_call_gpt4v_api()
    test_call_dalle3_api()