mirror of
https://github.com/xtekky/gpt4free.git
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c617b18d12
Add AbstractProvider class Add ProviderType type Add get_last_provider function Add version module and VersionUtils Display used provider in gui Fix error response in api
63 lines
2.4 KiB
Python
63 lines
2.4 KiB
Python
from __future__ import annotations
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import json
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import re
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import time
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import requests
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from ...typing import Any, CreateResult
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from ..base_provider import AbstractProvider
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class DfeHub(AbstractProvider):
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url = "https://chat.dfehub.com/"
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supports_stream = True
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supports_gpt_35_turbo = True
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@staticmethod
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def create_completion(
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model: str,
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messages: list[dict[str, str]],
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stream: bool, **kwargs: Any) -> CreateResult:
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headers = {
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"authority" : "chat.dfehub.com",
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"accept" : "*/*",
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"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",
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"content-type" : "application/json",
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"origin" : "https://chat.dfehub.com",
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"referer" : "https://chat.dfehub.com/",
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"sec-ch-ua" : '"Not.A/Brand";v="8", "Chromium";v="114", "Google Chrome";v="114"',
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"sec-ch-ua-mobile" : "?0",
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"sec-ch-ua-platform": '"macOS"',
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"sec-fetch-dest" : "empty",
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"sec-fetch-mode" : "cors",
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"sec-fetch-site" : "same-origin",
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"user-agent" : "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36",
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"x-requested-with" : "XMLHttpRequest",
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}
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json_data = {
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"messages" : messages,
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"model" : "gpt-3.5-turbo",
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"temperature" : kwargs.get("temperature", 0.5),
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"presence_penalty" : kwargs.get("presence_penalty", 0),
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"frequency_penalty" : kwargs.get("frequency_penalty", 0),
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"top_p" : kwargs.get("top_p", 1),
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"stream" : True
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}
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response = requests.post("https://chat.dfehub.com/api/openai/v1/chat/completions",
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headers=headers, json=json_data, timeout=3)
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for chunk in response.iter_lines():
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if b"detail" in chunk:
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delay = re.findall(r"\d+\.\d+", chunk.decode())
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delay = float(delay[-1])
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time.sleep(delay)
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yield from DfeHub.create_completion(model, messages, stream, **kwargs)
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if b"content" in chunk:
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data = json.loads(chunk.decode().split("data: ")[1])
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yield (data["choices"][0]["delta"]["content"])
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