path: root/g4f/.v1/unfinished/t3nsor/__init__.py

from time import time

from requests import post

headers = {
    'authority': 'www.t3nsor.tech',
    'accept': '*/*',
    'accept-language': 'en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3',
    'cache-control': 'no-cache',
    'content-type': 'application/json',
    'origin': 'https://www.t3nsor.tech',
    'pragma': 'no-cache',
    'referer': 'https://www.t3nsor.tech/',
    'sec-ch-ua': '"Chromium";v="112", "Google Chrome";v="112", "Not:A-Brand";v="99"',
    'sec-ch-ua-mobile': '?0',
    'sec-ch-ua-platform': '"macOS"',
    'sec-fetch-dest': 'empty',
    'sec-fetch-mode': 'cors',
    'sec-fetch-site': 'same-origin',
    'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36',
}


class T3nsorResponse:
    class Completion:
        class Choices:
            def __init__(self, choice: dict) -> None:
                self.text = choice['text']
                self.content = self.text.encode()
                self.index = choice['index']
                self.logprobs = choice['logprobs']
                self.finish_reason = choice['finish_reason']

            def __repr__(self) -> str:
                return f'''<__main__.APIResponse.Completion.Choices(\n    text           = {self.text.encode()},\n    index          = {self.index},\n    logprobs       = {self.logprobs},\n    finish_reason  = {self.finish_reason})object at 0x1337>'''

        def __init__(self, choices: dict) -> None:
            self.choices = [self.Choices(choice) for choice in choices]

    class Usage:
        def __init__(self, usage_dict: dict) -> None:
            self.prompt_tokens = usage_dict['prompt_chars']
            self.completion_tokens = usage_dict['completion_chars']
            self.total_tokens = usage_dict['total_chars']

        def __repr__(self):
            return f'''<__main__.APIResponse.Usage(\n    prompt_tokens      = {self.prompt_tokens},\n    completion_tokens  = {self.completion_tokens},\n    total_tokens       = {self.total_tokens})object at 0x1337>'''

    def __init__(self, response_dict: dict) -> None:
        self.response_dict = response_dict
        self.id = response_dict['id']
        self.object = response_dict['object']
        self.created = response_dict['created']
        self.model = response_dict['model']
        self.completion = self.Completion(response_dict['choices'])
        self.usage = self.Usage(response_dict['usage'])

    def json(self) -> dict:
        return self.response_dict


class Completion:
    model = {
        'model': {
            'id': 'gpt-3.5-turbo',
            'name': 'Default (GPT-3.5)'
        }
    }

    def create(
            prompt: str = 'hello world',
            messages: list = []) -> T3nsorResponse:
        response = post('https://www.t3nsor.tech/api/chat', headers=headers, json=Completion.model | {
            'messages': messages,
            'key': '',
            'prompt': prompt
        })

        return T3nsorResponse({
            'id': f'cmpl-1337-{int(time())}',
            'object': 'text_completion',
            'created': int(time()),
            'model': Completion.model,
            'choices': [{
                'text': response.text,
                'index': 0,
                'logprobs': None,
                'finish_reason': 'stop'
            }],
            'usage': {
                'prompt_chars': len(prompt),
                'completion_chars': len(response.text),
                'total_chars': len(prompt) + len(response.text)
            }
        })


class StreamCompletion:
    model = {
        'model': {
            'id': 'gpt-3.5-turbo',
            'name': 'Default (GPT-3.5)'
        }
    }

    def create(
            prompt: str = 'hello world',
            messages: list = []) -> T3nsorResponse:
        print('t3nsor api is down, this may not work, refer to another module')

        response = post('https://www.t3nsor.tech/api/chat', headers=headers, stream=True, json=Completion.model | {
            'messages': messages,
            'key': '',
            'prompt': prompt
        })

        for chunk in response.iter_content(chunk_size=2046):
            yield T3nsorResponse({
                'id': f'cmpl-1337-{int(time())}',
                'object': 'text_completion',
                'created': int(time()),
                'model': Completion.model,

                'choices': [{
                    'text': chunk.decode(),
                    'index': 0,
                    'logprobs': None,
                    'finish_reason': 'stop'
                }],

                'usage': {
                    'prompt_chars': len(prompt),
                    'completion_chars': len(chunk.decode()),
                    'total_chars': len(prompt) + len(chunk.decode())
                }
            })