from __future__ import annotations import re import asyncio import uuid import json import base64 import time import requests from aiohttp import ClientWebSocketResponse from copy import copy try: import nodriver from nodriver.cdp.network import get_response_body has_nodriver = True except ImportError: has_nodriver = False try: from platformdirs import user_config_dir has_platformdirs = True except ImportError: has_platformdirs = False from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin from ...typing import AsyncResult, Messages, Cookies, ImageType, AsyncIterator from ...requests.raise_for_status import 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 get_request_config, NoValidHarFileError from ..openai.har_file import RequestConfig, arkReq, arkose_url, start_url, conversation_url, backend_url, backend_anon_url from ..openai.proofofwork import generate_proof_token from ..openai.new import get_requirements_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 needs_auth = True supports_gpt_4 = True supports_message_history = True supports_system_message = True default_model = "auto" default_vision_model = "gpt-4o" fallback_models = ["auto", "gpt-4", "gpt-4o", "gpt-4o-mini", "gpt-4o-canmore", "o1-preview", "o1-mini"] vision_models = fallback_models image_models = fallback_models _api_key: str = None _headers: dict = None _cookies: Cookies = None _expires: int = None @classmethod def get_models(cls): if not cls.models: try: response = requests.get(f"{cls.url}/backend-anon/models") response.raise_for_status() data = response.json() cls.models = [model.get("slug") for model in data.get("models")] except Exception: cls.models = cls.fallback_models return cls.models @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 .har file for OpenaiChat' 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 = [{ "author": {"role": message["role"]}, "content": {"content_type": "text", "parts": [message["content"]]}, "id": str(uuid.uuid4()), "create_time": int(time.time()), "id": str(uuid.uuid4()), "metadata": {"serialization_metadata": {"custom_symbol_offsets": []}} } 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, element: 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. """ try: prompt = element["metadata"]["dalle"]["prompt"] file_id = element["asset_pointer"].split("file-service://", 1)[1] except Exception as e: raise RuntimeError(f"No Image: {e.__class__.__name__}: {e}") 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 = False, 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 try: await get_request_config(proxy) cls._create_request_args(RequestConfig.cookies, RequestConfig.headers) cls._set_api_key(RequestConfig.access_token) except NoValidHarFileError as e: await cls.nodriver_auth(proxy) try: image_request = await cls.upload_image(session, cls._headers, image, image_name) if image else None except Exception as e: image_request = None debug.log("OpenaiChat: Upload image failed") debug.log(f"{e.__class__.__name__}: {e}") model = cls.get_model(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": get_requirements_token(RequestConfig.proof_token) if RequestConfig.proof_token else None}, headers=cls._headers ) as response: cls._update_request_args(session) await raise_for_status(response) chat_requirements = await response.json() need_turnstile = chat_requirements.get("turnstile", {}).get("required", False) need_arkose = chat_requirements.get("arkose", {}).get("required", False) chat_token = chat_requirements.get("token") if need_arkose and RequestConfig.arkose_token is None: await get_request_config(proxy) cls._create_request_args(RequestConfig,cookies, RequestConfig.headers) cls._set_api_key(RequestConfig.access_token) if RequestConfig.arkose_token is None: raise MissingAuthError("No arkose token found in .har file") if "proofofwork" in chat_requirements: proofofwork = generate_proof_token( **chat_requirements["proofofwork"], user_agent=cls._headers["user-agent"], proof_token=RequestConfig.proof_token ) [debug.log(text) for text in ( f"Arkose: {'False' if not need_arkose else RequestConfig.arkose_token[:12]+'...'}", f"Proofofwork: {'False' if proofofwork is None else proofofwork[:12]+'...'}", )] data = { "action": action, "messages": None, "parent_message_id": conversation.message_id, "model": model, "paragen_cot_summary_display_override": "allow", "history_and_training_disabled": history_disabled and not auto_continue and not return_conversation, "conversation_mode": {"kind":"primary_assistant"}, "websocket_request_id": str(uuid.uuid4()), "supported_encodings": ["v1"], "supports_buffering": True } if conversation.conversation_id is not None: data["conversation_id"] = conversation.conversation_id debug.log(f"OpenaiChat: Use conversation: {conversation.conversation_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 RequestConfig.arkose_token: headers["Openai-Sentinel-Arkose-Token"] = RequestConfig.arkose_token if proofofwork is not None: headers["Openai-Sentinel-Proof-Token"] = proofofwork if need_turnstile and RequestConfig.turnstile_token is not None: headers['openai-sentinel-turnstile-token'] = RequestConfig.turnstile_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) if response.status == 403 and max_retries > 0: max_retries -= 1 debug.log(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): 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) @classmethod async def iter_messages_chunk( cls, messages: AsyncIterator, session: StreamSession, fields: Conversation, ) -> AsyncIterator: async for message in messages: async for chunk in cls.iter_messages_line(session, message, fields): yield chunk @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 isinstance(line, dict) and "v" in line: v = line.get("v") if isinstance(v, str) and fields.is_recipient: yield v elif isinstance(v, list) and fields.is_recipient: for m in v: if m.get("p") == "/message/content/parts/0": yield m.get("v") elif m.get("p") == "/message/metadata": fields.finish_reason = m.get("v", {}).get("finish_details", {}).get("type") break elif isinstance(v, dict): if fields.conversation_id is None: fields.conversation_id = v.get("conversation_id") debug.log(f"OpenaiChat: New conversation: {fields.conversation_id}") m = v.get("message", {}) fields.is_recipient = m.get("recipient") == "all" if fields.is_recipient: c = m.get("content", {}) if c.get("content_type") == "multimodal_text": generated_images = [] for element in c.get("parts"): if isinstance(element, dict) and element.get("content_type") == "image_asset_pointer": generated_images.append( cls.get_generated_image(session, cls._headers, element) ) for image_response in await asyncio.gather(*generated_images): yield image_response if m.get("author", {}).get("role") == "assistant": fields.message_id = v.get("message", {}).get("id") return if "error" in line and line.get("error"): raise RuntimeError(line.get("error")) @classmethod async def nodriver_auth(cls, proxy: str = None): if not has_nodriver: return if has_platformdirs: user_data_dir = user_config_dir("g4f-nodriver") else: user_data_dir = None debug.log(f"Open nodriver with user_dir: {user_data_dir}") browser = await nodriver.start( user_data_dir=user_data_dir, browser_args=None if proxy is None else [f"--proxy-server={proxy}"], ) page = browser.main_tab def on_request(event: nodriver.cdp.network.RequestWillBeSent): if event.request.url == start_url or event.request.url.startswith(conversation_url): RequestConfig.access_request_id = event.request_id RequestConfig.headers = event.request.headers elif event.request.url in (backend_url, backend_anon_url): if "OpenAI-Sentinel-Proof-Token" in event.request.headers: RequestConfig.proof_token = json.loads(base64.b64decode( event.request.headers["OpenAI-Sentinel-Proof-Token"].split("gAAAAAB", 1)[-1].encode() ).decode()) if "OpenAI-Sentinel-Turnstile-Token" in event.request.headers: RequestConfig.turnstile_token = event.request.headers["OpenAI-Sentinel-Turnstile-Token"] if "Authorization" in event.request.headers: RequestConfig.access_token = event.request.headers["Authorization"].split()[-1] elif event.request.url == arkose_url: RequestConfig.arkose_request = arkReq( arkURL=event.request.url, arkBx=None, arkHeader=event.request.headers, arkBody=event.request.post_data, userAgent=event.request.headers.get("user-agent") ) await page.send(nodriver.cdp.network.enable()) page.add_handler(nodriver.cdp.network.RequestWillBeSent, on_request) page = await browser.get(cls.url) try: if RequestConfig.access_request_id is not None: body = await page.send(get_response_body(RequestConfig.access_request_id)) if isinstance(body, tuple) and body: body = body[0] if body: match = re.search(r'"accessToken":"(.*?)"', body) if match: RequestConfig.access_token = match.group(1) except KeyError: pass for c in await page.send(nodriver.cdp.network.get_cookies([cls.url])): RequestConfig.cookies[c.name] = c.value RequestConfig.user_agent = await page.evaluate("window.navigator.userAgent") await page.select("#prompt-textarea", 240) while True: if RequestConfig.proof_token: break await asyncio.sleep(1) await page.close() cls._create_request_args(RequestConfig.cookies, RequestConfig.headers, user_agent=RequestConfig.user_agent) cls._set_api_key(RequestConfig.access_token) @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 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 if api_key: cls._headers["authorization"] = f"Bearer {api_key}" @classmethod def _update_cookie_header(cls): cls._headers["cookie"] = 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 self.is_recipient = False 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