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, Union, AsyncIterator from ...requests import get_args_from_browser, raise_for_status from ...requests.aiohttp import StreamSession from ...image import to_image, to_bytes, ImageResponse, ImageRequest from ...errors import MissingAuthError, ResponseError from ...providers.conversation import BaseConversation from ..openai.har_file import getArkoseAndAccessToken, NoValidHarFileError from ... import debug class OpenaiChat(AsyncGeneratorProvider, ProviderModelMixin): """A class for creating and managing conversations with OpenAI chat service""" lebel = "OpenAI ChatGPT" url = "https://chat.openai.com" working = 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 = [], 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 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: cls._update_request_args() await raise_for_status(response) 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: 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: 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 = 120, 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, **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( proxies={"all": proxy}, impersonate="chrome", timeout=timeout ) as session: if cls._expires is not None and cls._expires < time.time(): cls._headers = cls._api_key = None if cls._headers is None or cookies is not None: cls._create_request_args(cookies) api_key = kwargs["access_token"] if "access_token" in kwargs else api_key if api_key is not None: cls._set_api_key(api_key) if cls.default_model is None and cls._api_key is not None: try: if not model: 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: api_key = cls._api_key = None cls._create_request_args() if debug.logging: print("OpenaiChat: Load default_model failed") print(f"{e.__class__.__name__}: {e}") arkose_token = None if cls.default_model is None: try: arkose_token, api_key, cookies = await getArkoseAndAccessToken(proxy) cls._create_request_args(cookies) cls._set_api_key(api_key) except NoValidHarFileError: ... if cls._api_key is None: await cls.nodriver_access_token() cls.default_model = cls.get_model(await cls.get_default_model(session, cls._headers)) async with session.post( f"{cls.url}/backend-anon/sentinel/chat-requirements" if not cls._api_key else f"{cls.url}/backend-api/sentinel/chat-requirements", json={"conversation_mode_kind": "primary_assistant"}, headers=cls._headers ) as response: cls._update_request_args(session) await raise_for_status(response) data = await response.json() blob = data["arkose"]["dx"] need_arkose = data["arkose"]["required"] chat_token = data["token"] if need_arkose and arkose_token is None: arkose_token, api_key, cookies = await getArkoseAndAccessToken(proxy) cls._create_request_args(cookies) cls._set_api_key(api_key) if arkose_token is None: raise MissingAuthError("No arkose token found in .har file") 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") 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: 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 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) await raise_for_status(response) async for chunk in cls.iter_messages_chunk(response.iter_lines(), session, conversation): 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 ) -> 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"]) 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 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 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://chat.openai.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): 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 browser = await uc.start(user_data_dir=user_data_dir) page = await browser.get("https://chat.openai.com/") while await page.query_selector("#prompt-textarea") is None: await asyncio.sleep(1) 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("chat.openai.com"): cookies[c.name] = c.value await page.close() cls._create_request_args(cookies) 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 { "accept-language": "en-US", "content-type": "application/json", "oai-device-id": str(uuid.uuid4()), "oai-language": "en-US", "sec-ch-ua": "\"Chromium\";v=\"122\", \"Not(A:Brand\";v=\"24\", \"Google Chrome\";v=\"122\"", "sec-ch-ua-mobile": "?0", "sec-ch-ua-platform": "\"Linux\"", "sec-fetch-dest": "empty", "sec-fetch-mode": "cors", "sec-fetch-site": "same-origin" } @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: Cookies = None): cls._headers = cls.get_default_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.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"] = cls._format_cookies(cls._cookies) 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