from __future__ import annotations import re import asyncio import uuid import json import base64 import time import requests from copy import copy try: import nodriver from nodriver.cdp.network import get_response_body has_nodriver = True except ImportError: has_nodriver = 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 import StreamSession from ...requests import get_nodriver from ...image import ImageResponse, ImageRequest, to_image, to_bytes, is_accepted_format from ...errors import MissingAuthError from ...providers.response import BaseConversation, FinishReason, SynthesizeData 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 = [default_model, "gpt-4", "gpt-4o", "gpt-4o-mini", "gpt-4o-canmore", "o1-preview", "o1-mini"] vision_models = fallback_models image_models = fallback_models synthesize_content_type = "audio/mpeg" _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 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, "Create file failed") 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, "Send file failed") # 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, "Get download url failed") image_data["download_url"] = (await response.json())["download_url"] return ImageRequest(image_data) @classmethod def create_messages(cls, messages: Messages, image_request: ImageRequest = None, system_hints: list = 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": []}, "system_hints": system_hints}, } 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, prompt: str = None) -> 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 TypeError: return 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 create_async_generator( cls, model: str, messages: Messages, proxy: str = None, timeout: int = 180, 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, web_search: 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. """ await cls.login(proxy) async with StreamSession( proxy=proxy, impersonate="chrome", timeout=timeout ) as session: 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.get("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]+'...'}", f"AccessToken: {'False' if cls._api_key is None else cls._api_key[: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, "system_hints": ["search"] if web_search else None } 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, ["search"] if web_search else None) headers = { **cls._headers, "accept": "text/event-stream", "content-type": "application/json", "openai-sentinel-chat-requirements-token": chat_token, } 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) if return_conversation: yield conversation async for line in response.iter_lines(): async for chunk in cls.iter_messages_line(session, line, conversation): yield chunk if not history_disabled: yield SynthesizeData(cls.__name__, { "conversation_id": conversation.conversation_id, "message_id": conversation.message_id, "voice": "maple", }) if auto_continue and conversation.finish_reason == "max_tokens": conversation.finish_reason = None action = "continue" await asyncio.sleep(5) else: break yield FinishReason(conversation.finish_reason) @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": image = cls.get_generated_image(session, cls._headers, element) if image is not None: generated_images.append(image) 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 synthesize(cls, params: dict) -> AsyncIterator[bytes]: await cls.login() async with StreamSession( impersonate="chrome", timeout=900 ) as session: async with session.get( f"{cls.url}/backend-api/synthesize", params=params, headers=cls._headers ) as response: await raise_for_status(response) async for chunk in response.iter_content(): yield chunk @classmethod async def login(cls, proxy: str = None): 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: if has_nodriver: await cls.nodriver_auth(proxy) else: raise @classmethod async def nodriver_auth(cls, proxy: str = None): browser = await get_nodriver(proxy=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 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=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