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from __future__ import annotations
import json
import random
import requests
from ...typing import Any, CreateResult
from ..base_provider import BaseProvider
class Theb(BaseProvider):
url = "https://theb.ai"
working = True
supports_stream = True
supports_gpt_35_turbo = True
needs_auth = True
@staticmethod
def create_completion(
model: str,
messages: list[dict[str, str]],
stream: bool, **kwargs: Any) -> CreateResult:
conversation = "\n".join(f"{message['role']}: {message['content']}" for message in messages)
conversation += "\nassistant: "
auth = kwargs.get("auth", {
"bearer_token":"free",
"org_id":"theb",
})
bearer_token = auth["bearer_token"]
org_id = auth["org_id"]
headers = {
'authority' : 'beta.theb.ai',
'accept' : 'text/event-stream',
'accept-language' : 'id-ID,id;q=0.9,en-US;q=0.8,en;q=0.7',
'authorization' : 'Bearer '+bearer_token,
'content-type' : 'application/json',
'origin' : 'https://beta.theb.ai',
'referer' : 'https://beta.theb.ai/home',
'sec-ch-ua' : '"Chromium";v="116", "Not)A;Brand";v="24", "Google Chrome";v="116"',
'sec-ch-ua-mobile' : '?0',
'sec-ch-ua-platform': '"Windows"',
'sec-fetch-dest' : 'empty',
'sec-fetch-mode' : 'cors',
'sec-fetch-site' : 'same-origin',
'user-agent' : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36',
'x-ai-model' : 'ee8d4f29cb7047f78cbe84313ed6ace8',
}
req_rand = random.randint(100000000, 9999999999)
json_data: dict[str, Any] = {
"text" : conversation,
"category" : "04f58f64a4aa4191a957b47290fee864",
"model" : "ee8d4f29cb7047f78cbe84313ed6ace8",
"model_params": {
"system_prompt" : "You are ChatGPT, a large language model trained by OpenAI, based on the GPT-3.5 architecture.\nKnowledge cutoff: 2021-09\nCurrent date: {{YYYY-MM-DD}}",
"temperature" : kwargs.get("temperature", 1),
"top_p" : kwargs.get("top_p", 1),
"frequency_penalty" : kwargs.get("frequency_penalty", 0),
"presence_penalty" : kwargs.get("presence_penalty", 0),
"long_term_memory" : "auto"
}
}
response = requests.post(f"https://beta.theb.ai/api/conversation?org_id={org_id}&req_rand={req_rand}",
headers=headers, json=json_data, stream=True)
response.raise_for_status()
content = ""
next_content = ""
for chunk in response.iter_lines():
if b"content" in chunk:
next_content = content
data = json.loads(chunk.decode().split("data: ")[1])
content = data["content"]
yield data["content"].replace(next_content, "")
@classmethod
@property
def params(cls):
params = [
("model", "str"),
("messages", "list[dict[str, str]]"),
("auth", "list[dict[str, str]]"),
("stream", "bool"),
("temperature", "float"),
("presence_penalty", "int"),
("frequency_penalty", "int"),
("top_p", "int")
]
param = ", ".join([": ".join(p) for p in params])
return f"g4f.provider.{cls.__name__} supports: ({param})"
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