gpt4free/g4f/Provider/needs_auth/OpenaiChat.py
2024-09-24 13:23:53 +03:00

820 lines
33 KiB
Python

from __future__ import annotations
import asyncio
import uuid
import json
import base64
import time
from aiohttp import ClientWebSocketResponse
from copy import copy
try:
import webview
has_webview = True
except ImportError:
has_webview = False
try:
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
except ImportError:
pass
from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
from ...webdriver import get_browser
from ...typing import AsyncResult, Messages, Cookies, ImageType, AsyncIterator
from ...requests import get_args_from_browser, raise_for_status
from ...requests.aiohttp import StreamSession
from ...image import ImageResponse, ImageRequest, to_image, to_bytes, is_accepted_format
from ...errors import MissingAuthError, ResponseError
from ...providers.conversation import BaseConversation
from ..helper import format_cookies
from ..openai.har_file import getArkoseAndAccessToken, NoValidHarFileError
from ..openai.proofofwork import generate_proof_token
from ... import debug
DEFAULT_HEADERS = {
"accept": "*/*",
"accept-encoding": "gzip, deflate, br, zstd",
"accept-language": "en-US,en;q=0.5",
"referer": "https://chatgpt.com/",
"sec-ch-ua": "\"Brave\";v=\"123\", \"Not:A-Brand\";v=\"8\", \"Chromium\";v=\"123\"",
"sec-ch-ua-mobile": "?0",
"sec-ch-ua-platform": "\"Windows\"",
"sec-fetch-dest": "empty",
"sec-fetch-mode": "cors",
"sec-fetch-site": "same-origin",
"sec-gpc": "1",
"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36"
}
class OpenaiChat(AsyncGeneratorProvider, ProviderModelMixin):
"""A class for creating and managing conversations with OpenAI chat service"""
label = "OpenAI ChatGPT"
url = "https://chatgpt.com"
working = True
supports_gpt_4 = True
supports_message_history = True
supports_system_message = True
default_model = None
default_vision_model = "gpt-4o"
models = [ "auto", "gpt-4o-mini", "gpt-4o", "gpt-4", "gpt-4-gizmo"]
model_aliases = {
#"gpt-4-turbo": "gpt-4",
#"gpt-4": "gpt-4-gizmo",
#"dalle": "gpt-4",
}
_api_key: str = None
_headers: dict = None
_cookies: Cookies = None
_expires: int = None
@classmethod
async def create(
cls,
prompt: str = None,
model: str = "",
messages: Messages = [],
action: str = "next",
**kwargs
) -> Response:
"""
Create a new conversation or continue an existing one
Args:
prompt: The user input to start or continue the conversation
model: The name of the model to use for generating responses
messages: The list of previous messages in the conversation
history_disabled: A flag indicating if the history and training should be disabled
action: The type of action to perform, either "next", "continue", or "variant"
conversation_id: The ID of the existing conversation, if any
parent_id: The ID of the parent message, if any
image: The image to include in the user input, if any
**kwargs: Additional keyword arguments to pass to the generator
Returns:
A Response object that contains the generator, action, messages, and options
"""
# Add the user input to the messages list
if prompt is not None:
messages.append({
"role": "user",
"content": prompt
})
generator = cls.create_async_generator(
model,
messages,
return_conversation=True,
**kwargs
)
return Response(
generator,
action,
messages,
kwargs
)
@classmethod
async def upload_image(
cls,
session: StreamSession,
headers: dict,
image: ImageType,
image_name: str = None
) -> ImageRequest:
"""
Upload an image to the service and get the download URL
Args:
session: The StreamSession object to use for requests
headers: The headers to include in the requests
image: The image to upload, either a PIL Image object or a bytes object
Returns:
An ImageRequest object that contains the download URL, file name, and other data
"""
# Convert the image to a PIL Image object and get the extension
data_bytes = to_bytes(image)
image = to_image(data_bytes)
extension = image.format.lower()
data = {
"file_name": "" if image_name is None else image_name,
"file_size": len(data_bytes),
"use_case": "multimodal"
}
# Post the image data to the service and get the image data
async with session.post(f"{cls.url}/backend-api/files", json=data, headers=headers) as response:
cls._update_request_args(session)
await raise_for_status(response)
image_data = {
**data,
**await response.json(),
"mime_type": is_accepted_format(data_bytes),
"extension": extension,
"height": image.height,
"width": image.width
}
# Put the image bytes to the upload URL and check the status
async with session.put(
image_data["upload_url"],
data=data_bytes,
headers={
"Content-Type": image_data["mime_type"],
"x-ms-blob-type": "BlockBlob"
}
) as response:
await raise_for_status(response)
# Post the file ID to the service and get the download URL
async with session.post(
f"{cls.url}/backend-api/files/{image_data['file_id']}/uploaded",
json={},
headers=headers
) as response:
cls._update_request_args(session)
await raise_for_status(response)
image_data["download_url"] = (await response.json())["download_url"]
return ImageRequest(image_data)
@classmethod
async def get_default_model(cls, session: StreamSession, headers: dict):
"""
Get the default model name from the service
Args:
session: The StreamSession object to use for requests
headers: The headers to include in the requests
Returns:
The default model name as a string
"""
if not cls.default_model:
url = f"{cls.url}/backend-anon/models" if cls._api_key is None else f"{cls.url}/backend-api/models"
async with session.get(url, headers=headers) as response:
cls._update_request_args(session)
if response.status == 401:
raise MissingAuthError('Add a "api_key" or a .har file' if cls._api_key is None else "Invalid api key")
await raise_for_status(response)
data = await response.json()
if "categories" in data:
cls.default_model = data["categories"][-1]["default_model"]
return cls.default_model
raise ResponseError(data)
return cls.default_model
@classmethod
def create_messages(cls, messages: Messages, image_request: ImageRequest = None):
"""
Create a list of messages for the user input
Args:
prompt: The user input as a string
image_response: The image response object, if any
Returns:
A list of messages with the user input and the image, if any
"""
# Create a message object with the user role and the content
messages = [{
"id": str(uuid.uuid4()),
"author": {"role": message["role"]},
"content": {"content_type": "text", "parts": [message["content"]]},
} for message in messages]
# Check if there is an image response
if image_request is not None:
# Change content in last user message
messages[-1]["content"] = {
"content_type": "multimodal_text",
"parts": [{
"asset_pointer": f"file-service://{image_request.get('file_id')}",
"height": image_request.get("height"),
"size_bytes": image_request.get("file_size"),
"width": image_request.get("width"),
}, messages[-1]["content"]["parts"][0]]
}
# Add the metadata object with the attachments
messages[-1]["metadata"] = {
"attachments": [{
"height": image_request.get("height"),
"id": image_request.get("file_id"),
"mimeType": image_request.get("mime_type"),
"name": image_request.get("file_name"),
"size": image_request.get("file_size"),
"width": image_request.get("width"),
}]
}
return messages
@classmethod
async def get_generated_image(cls, session: StreamSession, headers: dict, line: dict) -> ImageResponse:
"""
Retrieves the image response based on the message content.
This method processes the message content to extract image information and retrieves the
corresponding image from the backend API. It then returns an ImageResponse object containing
the image URL and the prompt used to generate the image.
Args:
session (StreamSession): The StreamSession object used for making HTTP requests.
headers (dict): HTTP headers to be used for the request.
line (dict): A dictionary representing the line of response that contains image information.
Returns:
ImageResponse: An object containing the image URL and the prompt, or None if no image is found.
Raises:
RuntimeError: If there'san error in downloading the image, including issues with the HTTP request or response.
"""
if "parts" not in line["message"]["content"]:
return
first_part = line["message"]["content"]["parts"][0]
if "asset_pointer" not in first_part or "metadata" not in first_part:
return
if first_part["metadata"] is None or first_part["metadata"]["dalle"] is None:
return
prompt = first_part["metadata"]["dalle"]["prompt"]
file_id = first_part["asset_pointer"].split("file-service://", 1)[1]
try:
async with session.get(f"{cls.url}/backend-api/files/{file_id}/download", headers=headers) as response:
cls._update_request_args(session)
await raise_for_status(response)
download_url = (await response.json())["download_url"]
return ImageResponse(download_url, prompt)
except Exception as e:
raise RuntimeError(f"Error in downloading image: {e}")
@classmethod
async def delete_conversation(cls, session: StreamSession, headers: dict, conversation_id: str):
"""
Deletes a conversation by setting its visibility to False.
This method sends an HTTP PATCH request to update the visibility of a conversation.
It's used to effectively delete a conversation from being accessed or displayed in the future.
Args:
session (StreamSession): The StreamSession object used for making HTTP requests.
headers (dict): HTTP headers to be used for the request.
conversation_id (str): The unique identifier of the conversation to be deleted.
Raises:
HTTPError: If the HTTP request fails or returns an unsuccessful status code.
"""
async with session.patch(
f"{cls.url}/backend-api/conversation/{conversation_id}",
json={"is_visible": False},
headers=headers
) as response:
cls._update_request_args(session)
...
@classmethod
async def create_async_generator(
cls,
model: str,
messages: Messages,
proxy: str = None,
timeout: int = 180,
api_key: str = None,
cookies: Cookies = None,
auto_continue: bool = False,
history_disabled: bool = True,
action: str = "next",
conversation_id: str = None,
conversation: Conversation = None,
parent_id: str = None,
image: ImageType = None,
image_name: str = None,
return_conversation: bool = False,
max_retries: int = 3,
**kwargs
) -> AsyncResult:
"""
Create an asynchronous generator for the conversation.
Args:
model (str): The model name.
messages (Messages): The list of previous messages.
proxy (str): Proxy to use for requests.
timeout (int): Timeout for requests.
api_key (str): Access token for authentication.
cookies (dict): Cookies to use for authentication.
auto_continue (bool): Flag to automatically continue the conversation.
history_disabled (bool): Flag to disable history and training.
action (str): Type of action ('next', 'continue', 'variant').
conversation_id (str): ID of the conversation.
parent_id (str): ID of the parent message.
image (ImageType): Image to include in the conversation.
return_conversation (bool): Flag to include response fields in the output.
**kwargs: Additional keyword arguments.
Yields:
AsyncResult: Asynchronous results from the generator.
Raises:
RuntimeError: If an error occurs during processing.
"""
async with StreamSession(
proxy=proxy,
impersonate="chrome",
timeout=timeout
) as session:
if cls._expires is not None and cls._expires < time.time():
cls._headers = cls._api_key = None
arkose_token = None
proofTokens = None
try:
arkose_token, api_key, cookies, headers, proofTokens = await getArkoseAndAccessToken(proxy)
cls._create_request_args(cookies, headers)
cls._set_api_key(api_key)
except NoValidHarFileError as e:
if cls._api_key is None and cls.needs_auth:
raise e
cls._create_request_args()
if cls.default_model is None:
cls.default_model = cls.get_model(await cls.get_default_model(session, cls._headers))
try:
image_request = await cls.upload_image(session, cls._headers, image, image_name) if image else None
except Exception as e:
image_request = None
if debug.logging:
print("OpenaiChat: Upload image failed")
print(f"{e.__class__.__name__}: {e}")
model = cls.get_model(model)
model = "text-davinci-002-render-sha" if model == "gpt-3.5-turbo" else model
if conversation is None:
conversation = Conversation(conversation_id, str(uuid.uuid4()) if parent_id is None else parent_id)
else:
conversation = copy(conversation)
if cls._api_key is None:
auto_continue = False
conversation.finish_reason = None
while conversation.finish_reason is None:
async with session.post(
f"{cls.url}/backend-anon/sentinel/chat-requirements"
if cls._api_key is None else
f"{cls.url}/backend-api/sentinel/chat-requirements",
json={"p": generate_proof_token(True, user_agent=cls._headers["user-agent"], proofTokens=proofTokens)},
headers=cls._headers
) as response:
cls._update_request_args(session)
await raise_for_status(response)
requirements = await response.json()
text_data = json.loads(requirements.get("text", "{}"))
need_arkose = text_data.get("turnstile", {}).get("required", False)
if need_arkose:
arkose_token = text_data.get("turnstile", {}).get("dx")
else:
need_arkose = requirements.get("arkose", {}).get("required", False)
chat_token = requirements["token"]
if need_arkose and arkose_token is None:
arkose_token, api_key, cookies, headers, proofTokens = await getArkoseAndAccessToken(proxy)
cls._create_request_args(cookies, headers)
cls._set_api_key(api_key)
if arkose_token is None:
raise MissingAuthError("No arkose token found in .har file")
if "proofofwork" in requirements:
proofofwork = generate_proof_token(
**requirements["proofofwork"],
user_agent=cls._headers["user-agent"],
proofTokens=proofTokens
)
if debug.logging:
print(
'Arkose:', False if not need_arkose else arkose_token[:12]+"...",
'Proofofwork:', False if proofofwork is None else proofofwork[:12]+"...",
)
ws = None
if need_arkose:
async with session.post(f"{cls.url}/backend-api/register-websocket", headers=cls._headers) as response:
wss_url = (await response.json()).get("wss_url")
if wss_url:
ws = await session.ws_connect(wss_url)
websocket_request_id = str(uuid.uuid4())
data = {
"action": action,
"conversation_mode": {"kind": "primary_assistant"},
"force_paragen": False,
"force_rate_limit": False,
"conversation_id": conversation.conversation_id,
"parent_message_id": conversation.message_id,
"model": model,
"history_and_training_disabled": history_disabled and not auto_continue and not return_conversation,
"websocket_request_id": websocket_request_id
}
if action != "continue":
messages = messages if conversation_id is None else [messages[-1]]
data["messages"] = cls.create_messages(messages, image_request)
headers = {
"accept": "text/event-stream",
"Openai-Sentinel-Chat-Requirements-Token": chat_token,
**cls._headers
}
if need_arkose:
headers["Openai-Sentinel-Arkose-Token"] = arkose_token
if proofofwork is not None:
headers["Openai-Sentinel-Proof-Token"] = proofofwork
async with session.post(
f"{cls.url}/backend-anon/conversation"
if cls._api_key is None else
f"{cls.url}/backend-api/conversation",
json=data,
headers=headers
) as response:
cls._update_request_args(session)
if response.status == 403 and max_retries > 0:
max_retries -= 1
if debug.logging:
print(f"Retry: Error {response.status}: {await response.text()}")
await asyncio.sleep(5)
continue
await raise_for_status(response)
async for chunk in cls.iter_messages_chunk(response.iter_lines(), session, conversation, ws):
if return_conversation:
history_disabled = False
return_conversation = False
yield conversation
yield chunk
if auto_continue and conversation.finish_reason == "max_tokens":
conversation.finish_reason = None
action = "continue"
await asyncio.sleep(5)
else:
break
if history_disabled and auto_continue:
await cls.delete_conversation(session, cls._headers, conversation.conversation_id)
@staticmethod
async def iter_messages_ws(ws: ClientWebSocketResponse, conversation_id: str, is_curl: bool) -> AsyncIterator:
while True:
if is_curl:
message = json.loads(ws.recv()[0])
else:
message = await ws.receive_json()
if message["conversation_id"] == conversation_id:
yield base64.b64decode(message["body"])
@classmethod
async def iter_messages_chunk(
cls,
messages: AsyncIterator,
session: StreamSession,
fields: Conversation,
ws = None
) -> AsyncIterator:
last_message: int = 0
async for message in messages:
if message.startswith(b'{"wss_url":'):
message = json.loads(message)
ws = await session.ws_connect(message["wss_url"]) if ws is None else ws
try:
async for chunk in cls.iter_messages_chunk(
cls.iter_messages_ws(ws, message["conversation_id"], hasattr(ws, "recv")),
session, fields
):
yield chunk
finally:
await ws.aclose() if hasattr(ws, "aclose") else await ws.close()
break
async for chunk in cls.iter_messages_line(session, message, fields):
if fields.finish_reason is not None:
break
elif isinstance(chunk, str):
if len(chunk) > last_message:
yield chunk[last_message:]
last_message = len(chunk)
else:
yield chunk
if fields.finish_reason is not None:
break
@classmethod
async def iter_messages_line(cls, session: StreamSession, line: bytes, fields: Conversation) -> AsyncIterator:
if not line.startswith(b"data: "):
return
elif line.startswith(b"data: [DONE]"):
if fields.finish_reason is None:
fields.finish_reason = "error"
return
try:
line = json.loads(line[6:])
except:
return
if "message" not in line:
return
if "error" in line and line["error"]:
raise RuntimeError(line["error"])
if "message_type" not in line["message"]["metadata"]:
return
image_response = await cls.get_generated_image(session, cls._headers, line)
if image_response is not None:
yield image_response
if line["message"]["author"]["role"] != "assistant":
return
if line["message"]["content"]["content_type"] != "text":
return
if line["message"]["metadata"]["message_type"] not in ("next", "continue", "variant"):
return
if line["message"]["recipient"] != "all":
return
if fields.conversation_id is None:
fields.conversation_id = line["conversation_id"]
fields.message_id = line["message"]["id"]
if "parts" in line["message"]["content"]:
yield line["message"]["content"]["parts"][0]
if "finish_details" in line["message"]["metadata"]:
fields.finish_reason = line["message"]["metadata"]["finish_details"]["type"]
@classmethod
async def webview_access_token(cls) -> str:
window = webview.create_window("OpenAI Chat", cls.url)
await asyncio.sleep(3)
prompt_input = None
while not prompt_input:
try:
await asyncio.sleep(1)
prompt_input = window.dom.get_element("#prompt-textarea")
except:
...
window.evaluate_js("""
this._fetch = this.fetch;
this.fetch = async (url, options) => {
const response = await this._fetch(url, options);
if (url == "https://chatgpt.com/backend-api/conversation") {
this._headers = options.headers;
return response;
}
return response;
};
""")
window.evaluate_js("""
document.querySelector('.from-token-main-surface-secondary').click();
""")
headers = None
while headers is None:
headers = window.evaluate_js("this._headers")
await asyncio.sleep(1)
headers["User-Agent"] = window.evaluate_js("this.navigator.userAgent")
cookies = [list(*cookie.items()) for cookie in window.get_cookies()]
window.destroy()
cls._cookies = dict([(name, cookie.value) for name, cookie in cookies])
cls._headers = headers
cls._expires = int(time.time()) + 60 * 60 * 4
cls._update_cookie_header()
@classmethod
async def nodriver_access_token(cls, proxy: str = None):
try:
import nodriver as uc
except ImportError:
return
try:
from platformdirs import user_config_dir
user_data_dir = user_config_dir("g4f-nodriver")
except:
user_data_dir = None
if debug.logging:
print(f"Open nodriver with user_dir: {user_data_dir}")
browser = await uc.start(
user_data_dir=user_data_dir,
browser_args=None if proxy is None else [f"--proxy-server={proxy}"],
)
page = await browser.get("https://chatgpt.com/")
await page.select("[id^=headlessui-menu-button-]", 240)
api_key = await page.evaluate(
"(async () => {"
"let session = await fetch('/api/auth/session');"
"let data = await session.json();"
"let accessToken = data['accessToken'];"
"let expires = new Date(); expires.setTime(expires.getTime() + 60 * 60 * 4 * 1000);"
"document.cookie = 'access_token=' + accessToken + ';expires=' + expires.toUTCString() + ';path=/';"
"return accessToken;"
"})();",
await_promise=True
)
cookies = {}
for c in await page.browser.cookies.get_all():
if c.domain.endswith("chatgpt.com"):
cookies[c.name] = c.value
user_agent = await page.evaluate("window.navigator.userAgent")
await page.close()
cls._create_request_args(cookies, user_agent=user_agent)
cls._set_api_key(api_key)
@classmethod
def browse_access_token(cls, proxy: str = None, timeout: int = 1200) -> None:
"""
Browse to obtain an access token.
Args:
proxy (str): Proxy to use for browsing.
Returns:
tuple[str, dict]: A tuple containing the access token and cookies.
"""
driver = get_browser(proxy=proxy)
try:
driver.get(f"{cls.url}/")
WebDriverWait(driver, timeout).until(EC.presence_of_element_located((By.ID, "prompt-textarea")))
access_token = driver.execute_script(
"let session = await fetch('/api/auth/session');"
"let data = await session.json();"
"let accessToken = data['accessToken'];"
"let expires = new Date(); expires.setTime(expires.getTime() + 60 * 60 * 4 * 1000);"
"document.cookie = 'access_token=' + accessToken + ';expires=' + expires.toUTCString() + ';path=/';"
"return accessToken;"
)
args = get_args_from_browser(f"{cls.url}/", driver, do_bypass_cloudflare=False)
cls._headers = args["headers"]
cls._cookies = args["cookies"]
cls._update_cookie_header()
cls._set_api_key(access_token)
finally:
driver.close()
@classmethod
async def fetch_access_token(cls, session: StreamSession, headers: dict):
async with session.get(
f"{cls.url}/api/auth/session",
headers=headers
) as response:
if response.ok:
data = await response.json()
if "accessToken" in data:
return data["accessToken"]
@staticmethod
def get_default_headers() -> dict:
return {
**DEFAULT_HEADERS,
"content-type": "application/json",
}
@classmethod
def _create_request_args(cls, cookies: Cookies = None, headers: dict = None, user_agent: str = None):
cls._headers = cls.get_default_headers() if headers is None else headers
if user_agent is not None:
cls._headers["user-agent"] = user_agent
cls._cookies = {} if cookies is None else {k: v for k, v in cookies.items() if k != "access_token"}
cls._update_cookie_header()
@classmethod
def _update_request_args(cls, session: StreamSession):
for c in session.cookie_jar if hasattr(session, "cookie_jar") else session.cookies.jar:
cls._cookies[c.key if hasattr(c, "key") else c.name] = c.value
cls._update_cookie_header()
@classmethod
def _set_api_key(cls, api_key: str):
cls._api_key = api_key
cls._expires = int(time.time()) + 60 * 60 * 4
cls._headers["authorization"] = f"Bearer {api_key}"
@classmethod
def _update_cookie_header(cls):
cls._headers["cookie"] = format_cookies(cls._cookies)
if "oai-did" in cls._cookies:
cls._headers["oai-device-id"] = cls._cookies["oai-did"]
class Conversation(BaseConversation):
"""
Class to encapsulate response fields.
"""
def __init__(self, conversation_id: str = None, message_id: str = None, finish_reason: str = None):
self.conversation_id = conversation_id
self.message_id = message_id
self.finish_reason = finish_reason
class Response():
"""
Class to encapsulate a response from the chat service.
"""
def __init__(
self,
generator: AsyncResult,
action: str,
messages: Messages,
options: dict
):
self._generator = generator
self.action = action
self.is_end = False
self._message = None
self._messages = messages
self._options = options
self._fields = None
async def generator(self) -> AsyncIterator:
if self._generator is not None:
self._generator = None
chunks = []
async for chunk in self._generator:
if isinstance(chunk, Conversation):
self._fields = chunk
else:
yield chunk
chunks.append(str(chunk))
self._message = "".join(chunks)
if self._fields is None:
raise RuntimeError("Missing response fields")
self.is_end = self._fields.finish_reason == "stop"
def __aiter__(self):
return self.generator()
async def get_message(self) -> str:
await self.generator()
return self._message
async def get_fields(self) -> dict:
await self.generator()
return {
"conversation_id": self._fields.conversation_id,
"parent_id": self._fields.message_id
}
async def create_next(self, prompt: str, **kwargs) -> Response:
return await OpenaiChat.create(
**self._options,
prompt=prompt,
messages=await self.get_messages(),
action="next",
**await self.get_fields(),
**kwargs
)
async def do_continue(self, **kwargs) -> Response:
fields = await self.get_fields()
if self.is_end:
raise RuntimeError("Can't continue message. Message already finished.")
return await OpenaiChat.create(
**self._options,
messages=await self.get_messages(),
action="continue",
**fields,
**kwargs
)
async def create_variant(self, **kwargs) -> Response:
if self.action != "next":
raise RuntimeError("Can't create variant from continue or variant request.")
return await OpenaiChat.create(
**self._options,
messages=self._messages,
action="variant",
**await self.get_fields(),
**kwargs
)
async def get_messages(self) -> list:
messages = self._messages
messages.append({"role": "assistant", "content": await self.message()})
return messages