| import requests | |
| requests.packages.urllib3.disable_warnings() | |
| from vlmeval.smp import * | |
| from vlmeval.api.base import BaseAPI | |
| from vlmeval.dataset import DATASET_TYPE | |
| from vlmeval.smp.vlm import encode_image_file_to_base64 | |
| class GLMVisionWrapper(BaseAPI): | |
| is_api: bool = True | |
| def __init__(self, | |
| model: str, | |
| retry: int = 5, | |
| wait: int = 5, | |
| key: str = None, | |
| verbose: bool = True, | |
| system_prompt: str = None, | |
| max_tokens: int = 4096, | |
| proxy: str = None, | |
| **kwargs): | |
| from zhipuai import ZhipuAI | |
| self.model = model | |
| self.fail_msg = 'Failed to obtain answer via API. ' | |
| if key is None: | |
| key = os.environ.get('GLMV_API_KEY', None) | |
| assert key is not None, ( | |
| 'Please set the API Key (obtain it here: ' | |
| 'https://bigmodel.cn)' | |
| ) | |
| self.client = ZhipuAI(api_key=key) | |
| super().__init__(wait=wait, retry=retry, system_prompt=system_prompt, verbose=verbose, **kwargs) | |
| def build_msgs(self, msgs_raw, system_prompt=None, dataset=None): | |
| msgs = cp.deepcopy(msgs_raw) | |
| content = [] | |
| for i, msg in enumerate(msgs): | |
| if msg['type'] == 'text': | |
| content.append(dict(type='text', text=msg['value'])) | |
| elif msg['type'] == 'image': | |
| content.append(dict(type='image_url', image_url=dict(url=encode_image_file_to_base64(msg['value'])))) | |
| if dataset in {'HallusionBench', 'POPE'}: | |
| content.append(dict(type="text", text="Please answer yes or no.")) | |
| ret = [dict(role='user', content=content)] | |
| return ret | |
| def generate_inner(self, inputs, **kwargs) -> str: | |
| assert isinstance(inputs, str) or isinstance(inputs, list) | |
| inputs = [inputs] if isinstance(inputs, str) else inputs | |
| messages = self.build_msgs(msgs_raw=inputs, dataset=kwargs.get('dataset', None)) | |
| response = self.client.chat.completions.create( | |
| model=self.model, | |
| messages=messages, | |
| do_sample=False, | |
| max_tokens=2048 | |
| ) | |
| try: | |
| answer = response.choices[0].message.content.strip() | |
| if self.verbose: | |
| self.logger.info(f'inputs: {inputs}\nanswer: {answer}') | |
| return 0, answer, 'Succeeded!' | |
| except Exception as err: | |
| if self.verbose: | |
| self.logger.error(f'{type(err)}: {err}') | |
| self.logger.error(f'The input messages are {inputs}.') | |
| return -1, self.fail_msg, '' | |
| class GLMVisionAPI(GLMVisionWrapper): | |
| def generate(self, message, dataset=None): | |
| return super(GLMVisionAPI, self).generate(message, dataset=dataset) | |