135 lines
5 KiB
Python
135 lines
5 KiB
Python
from requests import post
|
|
from time import time
|
|
|
|
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:
|
|
|
|
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())
|
|
}
|
|
}) |