import pandas as pd import requests import json import os import base64 from vlmeval.smp import * from vlmeval.api.base import BaseAPI from vlmeval.dataset import DATASET_TYPE from vlmeval.dataset import img_root_map API_ENDPOINT = 'https://jiutian.10086.cn/kunlun/ingress/api/h3t-eeceff/92390745235a40a484d850be19e1f8b4/ai-5d7ae47ec93f4280953273c4001aafee/service-7544ea5ee3e841ad9d01e7af44acef7c/v1/chat/completions' # noqa: E501 APP_CODE = 'eyJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiJ9.eyJzdWIiOiI5ZGQwNmQ2ZjU4YTU0ZGY0OGEzNjRhMjQyNGMwODEyNSIsImlzcyI6ImFwaS1hdXRoLWtleSIsImV4cCI6NDg4MjkwNDA3OX0.k5t_T-955xWMndzBbx4WQQNAgm5DpMos9mHm7vkFipQ3yebCFMfyufpSxORSfEVpBaDS3Nly0dd8ygQYGnDgIQcC72vQ1xtkjCP49LNcqlceoET4rGc1zwRi76XLPSGFES4GcwvEmr7Ilth7XtqZNxcDF_Z7HyHyf1-zF0JIQETYSoxenqLU-gNteNfqRUnlyCgaKh03DscAbYvtoMUxEaFa2ZqyRSwekdHI_SPKCq9aC9G19yDPHTjeiwl1ubtyC5uMy5pERn_ClRsZS3Wyb-GmD5QQsFofrWvCiU_fVJuUiez39pYZvEP8awH0R9B7SkpQ4XOzj3fdytTPYy3g6g' # noqa: E501 class JTVLChatWrapper(BaseAPI): is_api: bool = True INTERLEAVE = False def __init__(self, model: str = 'jt-vl-chat', retry: int = 5, wait: int = 5, api_base: str = API_ENDPOINT, key: str = APP_CODE, verbose: bool = True, system_prompt: str = None, temperature: float = 0.7, max_tokens: int = 2048, proxy: str = None, **kwargs): self.model = model self.temperature = temperature self.max_tokens = max_tokens self.api_base = api_base if key is None: key = os.environ.get('JTVLChat_API_KEY', None) assert key is not None, ( 'Please set the API Key (also called app_code, obtain it here: https://github.com/jiutiancv/JT-VL-Chat)' ) self.key = key super().__init__(wait=wait, retry=retry, system_prompt=system_prompt, verbose=verbose, **kwargs) def dump_image(self, line, dataset): """Dump the image(s) of the input line to the corresponding dataset folder. Args: line (line of pd.DataFrame): The raw input line. dataset (str): The name of the dataset. Returns: str | list[str]: The paths of the dumped images. """ ROOT = LMUDataRoot() assert isinstance(dataset, str) img_root = os.path.join(ROOT, 'images', img_root_map(dataset) if dataset in img_root_map(dataset) else dataset) os.makedirs(img_root, exist_ok=True) if 'image' in line: if isinstance(line['image'], list): tgt_path = [] assert 'image_path' in line for img, im_name in zip(line['image'], line['image_path']): path = osp.join(img_root, im_name) if not read_ok(path): decode_base64_to_image_file(img, path) tgt_path.append(path) else: tgt_path = osp.join(img_root, f"{line['index']}.jpg") if not read_ok(tgt_path): decode_base64_to_image_file(line['image'], tgt_path) tgt_path = [tgt_path] else: assert 'image_path' in line tgt_path = toliststr(line['image_path']) return tgt_path def use_custom_prompt(self, dataset): assert dataset is not None if listinstr(['MMMU_DEV_VAL','MMMU_TEST'], dataset): return False else: return True def build_multi_choice_prompt(self, line, dataset=None): question = line['question'] hint = line['hint'] if ('hint' in line and not pd.isna(line['hint'])) else None if hint is not None: question = hint + '\n' + question options = { cand: line[cand] for cand in string.ascii_uppercase if cand in line and not pd.isna(line[cand]) } for key, item in options.items(): question += f'\n{key}. {item}' prompt = question if len(options): prompt += '\n请直接回答选项字母。' if cn_string( prompt) else "\nAnswer with the option's letter from the given choices directly." else: prompt += '\n请直接回答问题。' if cn_string(prompt) else '\nAnswer the question directly.' return prompt def build_prompt(self, line, dataset=None): assert self.use_custom_prompt(dataset) assert dataset is None or isinstance(dataset, str) tgt_path = self.dump_image(line, dataset) if dataset is not None and listinstr(['MME'], dataset): question = line['question'] prompt = question + ' Answer the question using a single word or phrase.' elif dataset is not None and listinstr(['HallusionBench'], dataset): question = line['question'] prompt = question + ' Please answer yes or no. Answer the question using a single word or phrase.' elif dataset is not None and DATASET_TYPE(dataset) == 'MCQ': prompt = self.build_multi_choice_prompt(line, dataset) elif dataset is not None and DATASET_TYPE(dataset) == 'VQA': if listinstr(['MathVista', 'MathVision'], dataset): prompt = line['question'] elif listinstr(['LLaVABench'], dataset): question = line['question'] prompt = question + '\nAnswer this question in detail.' elif listinstr(['MMVet'], dataset): prompt = line['question'] else: question = line['question'] prompt = question + '\nAnswer the question using a single word or phrase.' else: prompt = line['question'] message = [dict(type='text', value=prompt)] message.extend([dict(type='image', value=s) for s in tgt_path]) return message def message_to_promptimg(self, message, dataset=None): assert not self.INTERLEAVE model_name = self.__class__.__name__ import warnings warnings.warn( f'Model {model_name} does not support interleaved input. ' 'Will use the first image and aggregated texts as prompt. ') num_images = len([x for x in message if x['type'] == 'image']) if num_images == 0: prompt = '\n'.join([x['value'] for x in message if x['type'] == 'text']) image = None else: prompt = '\n'.join([x['value'] for x in message if x['type'] == 'text']) if dataset == 'BLINK': image = concat_images_vlmeval( [x['value'] for x in message if x['type'] == 'image'], target_size=512) else: image = [x['value'] for x in message if x['type'] == 'image'][0] return prompt, image def get_send_data(self,prompt, image_path, temperature, max_tokens): image = '' with open(image_path, 'rb') as f: image = str(base64.b64encode(f.read()), 'utf-8') send_data = { "messages": [ { "role": "user", "content": prompt } ], "image_base64": image, "max_tokens": max_tokens, "temperature": temperature } return send_data def get_send_data_no_image(self,prompt, temperature, max_tokens): send_data = { "messages": [ { "role": "user", "content": prompt } ], "max_tokens": max_tokens, "temperature": temperature } return send_data def generate_inner(self, inputs, **kwargs) -> str: assert isinstance(inputs, str) or isinstance(inputs, list) inputs = [inputs] if isinstance(inputs, str) else inputs dataset = kwargs.get('dataset', None) prompt, image_path = self.message_to_promptimg(message=inputs, dataset=dataset) # print("prompt:",prompt) if image_path: send_data = self.get_send_data( prompt=prompt, image_path=image_path, temperature=self.temperature, max_tokens=self.max_tokens) else: send_data = self.get_send_data_no_image( prompt=prompt, temperature=self.temperature, max_tokens=self.max_tokens) json_data = json.dumps(send_data) header_dict = {'Content-Type': 'application/json', 'Authorization': 'Bearer ' + self.key} r = requests.post(self.api_base, headers=header_dict, data=json_data, timeout=3000) try: assert r.status_code == 200 r_json = r.json() output = r_json['choices'][0]['message']['content'] if self.verbose: self.logger.info(f'inputs: {inputs}\nanswer: {output}') return 0,output,'Succeeded! ' except: error_msg = f'Error! code {r.status_code} content: {r.content}' error_con = r.content.decode('utf-8') if self.verbose: self.logger.error(error_msg) self.logger.error(error_con) self.logger.error(f'The input messages are {inputs}.') return -1,error_msg,'' class JTVLChatAPI(JTVLChatWrapper): def generate(self, message, dataset=None): return super(JTVLChatAPI, self).generate(message, dataset=dataset)