from __future__ import annotations
import asyncio
import uuid
import json
import os
import base64
import time
from aiohttp import ClientWebSocketResponse
try:
from py_arkose_generator.arkose import get_values_for_request
from async_property import async_cached_property
has_requirements = True
except ImportError:
async_cached_property = property
has_requirements = 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 ..helper import get_cookies
from ...webdriver import get_browser
from ...typing import AsyncResult, Messages, Cookies, ImageType, Union, AsyncIterator
from ...requests import get_args_from_browser
from ...requests.aiohttp import StreamSession
from ...image import to_image, to_bytes, ImageResponse, ImageRequest
from ...errors import MissingRequirementsError, MissingAuthError
from ... import debug
class OpenaiChat(AsyncGeneratorProvider, ProviderModelMixin):
"""A class for creating and managing conversations with OpenAI chat service"""
url = "https://chat.openai.com"
working = True
needs_auth = True
supports_gpt_35_turbo = True
supports_gpt_4 = True
supports_message_history = True
supports_system_message = True
default_model = None
models = ["gpt-3.5-turbo", "gpt-4", "gpt-4-gizmo"]
model_aliases = {"text-davinci-002-render-sha": "gpt-3.5-turbo", "": "gpt-3.5-turbo"}
_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 = [],
history_disabled: bool = False,
action: str = "next",
conversation_id: str = None,
parent_id: str = None,
image: ImageType = None,
**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:
messages.append({
"role": "user",
"content": prompt
})
generator = cls.create_async_generator(
model,
messages,
history_disabled=history_disabled,
action=action,
conversation_id=conversation_id,
parent_id=parent_id,
image=image,
response_fields=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
image = to_image(image)
extension = image.format.lower()
# Convert the image to a bytes object and get the size
data_bytes = to_bytes(image)
data = {
"file_name": image_name if image_name else f"{image.width}x{image.height}.{extension}",
"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:
response.raise_for_status()
image_data = {
**data,
**await response.json(),
"mime_type": f"image/{extension}",
"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:
response.raise_for_status()
# 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:
response.raise_for_status()
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:
async with session.get(f"{cls.url}/backend-api/models", headers=headers) as response:
cls._update_request_args(session)
response.raise_for_status()
data = await response.json()
if "categories" in data:
cls.default_model = data["categories"][-1]["default_model"]
return cls.default_model
raise RuntimeError(f"Response: {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:
# 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:
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:
response.raise_for_status()
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:
...
@classmethod
async def create_async_generator(
cls,
model: str,
messages: Messages,
proxy: str = None,
timeout: int = 120,
api_key: str = None,
cookies: Cookies = None,
auto_continue: bool = False,
history_disabled: bool = True,
action: str = "next",
conversation_id: str = None,
parent_id: str = None,
image: ImageType = None,
image_name: str = None,
response_fields: bool = False,
**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.
response_fields (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.
"""
if not has_requirements:
raise MissingRequirementsError('Install "py-arkose-generator" and "async_property" package')
if not parent_id:
parent_id = str(uuid.uuid4())
# Read api_key from arguments
api_key = kwargs["access_token"] if "access_token" in kwargs else api_key
async with StreamSession(
proxies={"https": proxy},
impersonate="chrome",
timeout=timeout
) as session:
# Read api_key and cookies from cache / browser config
if cls._headers is None or time.time() > cls._expires:
if api_key is None:
# Read api_key from cookies
cookies = get_cookies("chat.openai.com", False) if cookies is None else cookies
api_key = cookies["access_token"] if "access_token" in cookies else api_key
cls._create_request_args(cookies)
else:
api_key = cls._api_key if api_key is None else api_key
# Read api_key with session cookies
if api_key is None and cookies:
api_key = await cls.fetch_access_token(session, cls._headers)
# Load default model
if cls.default_model is None and api_key is not None:
try:
if not model:
cls._set_api_key(api_key)
cls.default_model = cls.get_model(await cls.get_default_model(session, cls._headers))
else:
cls.default_model = cls.get_model(model)
except Exception as e:
if debug.logging:
print("OpenaiChat: Load default_model failed")
print(f"{e.__class__.__name__}: {e}")
# Browse api_key and default model
if api_key is None or cls.default_model is None:
login_url = os.environ.get("G4F_LOGIN_URL")
if login_url:
yield f"Please login: [ChatGPT]({login_url})\n\n"
try:
cls.browse_access_token(proxy)
except MissingRequirementsError:
raise MissingAuthError(f'Missing "access_token". Add a "api_key" please')
cls.default_model = cls.get_model(await cls.get_default_model(session, cls._headers))
else:
cls._set_api_key(api_key)
try:
image_request = await cls.upload_image(session, cls._headers, image, image_name) if image else None
except Exception as e:
if debug.logging:
print("OpenaiChat: Upload image failed")
print(f"{e.__class__.__name__}: {e}")
model = cls.get_model(model).replace("gpt-3.5-turbo", "text-davinci-002-render-sha")
fields = ResponseFields()
while fields.finish_reason is None:
arkose_token = await cls.get_arkose_token(session)
conversation_id = conversation_id if fields.conversation_id is None else fields.conversation_id
parent_id = parent_id if fields.message_id is None else fields.message_id
data = {
"action": action,
"arkose_token": arkose_token,
"conversation_mode": {"kind": "primary_assistant"},
"force_paragen": False,
"force_rate_limit": False,
"conversation_id": conversation_id,
"parent_message_id": parent_id,
"model": model,
"history_and_training_disabled": history_disabled and not auto_continue,
}
if action != "continue":
messages = messages if conversation_id is None else [messages[-1]]
data["messages"] = cls.create_messages(messages, image_request)
async with session.post(
f"{cls.url}/backend-api/conversation",
json=data,
headers={
"Accept": "text/event-stream",
"OpenAI-Sentinel-Arkose-Token": arkose_token,
**cls._headers
}
) as response:
cls._update_request_args(session)
if not response.ok:
raise RuntimeError(f"Response {response.status}: {await response.text()}")
async for chunk in cls.iter_messages_chunk(response.iter_lines(), session, fields):
if response_fields:
response_fields = False
yield fields
yield chunk
if not auto_continue:
break
action = "continue"
await asyncio.sleep(5)
if history_disabled and auto_continue:
await cls.delete_conversation(session, cls._headers, fields.conversation_id)
@staticmethod
async def iter_messages_ws(ws: ClientWebSocketResponse, conversation_id: str) -> AsyncIterator:
while True:
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: ResponseFields) -> AsyncIterator:
last_message: int = 0
async for message in messages:
if message.startswith(b'{"wss_url":'):
message = json.loads(message)
async with session.ws_connect(message["wss_url"]) as ws:
async for chunk in cls.iter_messages_chunk(cls.iter_messages_ws(ws, message["conversation_id"]), session, fields):
yield chunk
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: ResponseFields) -> AsyncIterator:
if not line.startswith(b"data: "):
return
elif line.startswith(b"data: [DONE]"):
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
try:
image_response = await cls.get_generated_image(session, cls._headers, line)
if image_response is not None:
yield image_response
except Exception as e:
yield e
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 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
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 get_arkose_token(cls, session: StreamSession) -> str:
"""
Obtain an Arkose token for the session.
Args:
session (StreamSession): The session object.
Returns:
str: The Arkose token.
Raises:
RuntimeError: If unable to retrieve the token.
"""
config = {
"pkey": "3D86FBBA-9D22-402A-B512-3420086BA6CC",
"surl": "https://tcr9i.chat.openai.com",
"headers": {
"User-Agent": 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/107.0.0.0 Safari/537.36'
},
"site": cls.url,
}
args_for_request = get_values_for_request(config)
async with session.post(**args_for_request) as response:
response.raise_for_status()
decoded_json = await response.json()
if "token" in decoded_json:
return decoded_json["token"]
raise RuntimeError(f"Response: {decoded_json}")
@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 _format_cookies(cookies: Cookies):
return "; ".join(f"{k}={v}" for k, v in cookies.items() if k != "access_token")
@classmethod
def _create_request_args(cls, cookies: Union[Cookies, None]):
cls._headers = {}
cls._cookies = {} if cookies is None else cookies
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.name if hasattr(c, "name") else c.key] = 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"] = cls._format_cookies(cls._cookies)
class EndTurn:
"""
Class to represent the end of a conversation turn.
"""
def __init__(self):
self.is_end = False
def end(self):
self.is_end = True
class ResponseFields:
"""
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):
if self._generator:
self._generator = None
chunks = []
async for chunk in self._generator:
if isinstance(chunk, ResponseFields):
self._fields = chunk
else:
yield chunk
chunks.append(str(chunk))
self._message = "".join(chunks)
if not self._fields:
raise RuntimeError("Missing response fields")
self.is_end = self._fields.end_turn
def __aiter__(self):
return self.generator()
@async_cached_property
async def message(self) -> str:
await self.generator()
return self._message
async def get_fields(self):
await self.generator()
return {"conversation_id": self._fields.conversation_id, "parent_id": self._fields.message_id}
async def next(self, prompt: str, **kwargs) -> Response:
return await OpenaiChat.create(
**self._options,
prompt=prompt,
messages=await self.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.messages,
action="continue",
**fields,
**kwargs
)
async def 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_cached_property
async def messages(self):
messages = self._messages
messages.append({"role": "assistant", "content": await self.message})
return messages
