from __future__ import annotations import asyncio import uuid import json import os 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 format_prompt, get_cookies from ...webdriver import get_browser, get_driver_cookies from ...typing import AsyncResult, Messages, Cookies, ImageType from ...requests import StreamSession from ...image import to_image, to_bytes, ImageResponse, ImageRequest from ...errors import MissingRequirementsError, MissingAuthError 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 default_model = None models = ["gpt-3.5-turbo", "gpt-4", "gpt-4-gizmo"] model_aliases = {"text-davinci-002-render-sha": "gpt-3.5-turbo"} _cookies: dict = {} @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: data = await response.json() if "categories" in data: cls.default_model = data["categories"][-1]["default_model"] else: raise RuntimeError(f"Response: {data}") return cls.default_model @classmethod def create_messages(cls, prompt: str, 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 """ # Check if there is an image response if not image_request: # Create a content object with the text type and the prompt content = {"content_type": "text", "parts": [prompt]} else: # Create a content object with the multimodal text type and the image and the prompt 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"), }, prompt] } # Create a message object with the user role and the content messages = [{ "id": str(uuid.uuid4()), "author": {"role": "user"}, "content": content, }] # Check if there is an image response if image_request: # Add the metadata object with the attachments messages[0]["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 file_id = first_part["asset_pointer"].split("file-service://", 1)[1] prompt = first_part["metadata"]["dalle"]["prompt"] 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: response.raise_for_status() @classmethod async def create_async_generator( cls, model: str, messages: Messages, proxy: str = None, timeout: int = 120, access_token: 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, 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. access_token (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()) if not cookies: cookies = cls._cookies or get_cookies("chat.openai.com", False) if not access_token and "access_token" in cookies: access_token = cookies["access_token"] if not access_token: login_url = os.environ.get("G4F_LOGIN_URL") if login_url: yield f"Please login: [ChatGPT]({login_url})\n\n" try: access_token, cookies = cls.browse_access_token(proxy) except MissingRequirementsError: raise MissingAuthError(f'Missing "access_token"') cls._cookies = cookies auth_headers = {"Authorization": f"Bearer {access_token}"} async with StreamSession( proxies={"https": proxy}, impersonate="chrome110", timeout=timeout, headers={"Cookie": "; ".join(f"{k}={v}" for k, v in cookies.items())} ) as session: try: image_response = None if image: image_response = await cls.upload_image(session, auth_headers, image, kwargs.get("image_name")) except Exception as e: yield e end_turn = EndTurn() model = cls.get_model(model or await cls.get_default_model(session, auth_headers)) model = "text-davinci-002-render-sha" if model == "gpt-3.5-turbo" else model while not end_turn.is_end: arkose_token = await cls.get_arkose_token(session) 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": prompt = format_prompt(messages) if not conversation_id else messages[-1]["content"] data["messages"] = cls.create_messages(prompt, image_response) async with session.post( f"{cls.url}/backend-api/conversation", json=data, headers={ "Accept": "text/event-stream", "OpenAI-Sentinel-Arkose-Token": arkose_token, **auth_headers } ) as response: if not response.ok: raise RuntimeError(f"Response {response.status_code}: {await response.text()}") last_message: int = 0 async for line in response.iter_lines(): if not line.startswith(b"data: "): continue elif line.startswith(b"data: [DONE]"): break try: line = json.loads(line[6:]) except: continue if "message" not in line: continue if "error" in line and line["error"]: raise RuntimeError(line["error"]) if "message_type" not in line["message"]["metadata"]: continue try: image_response = await cls.get_generated_image(session, auth_headers, line) if image_response: yield image_response except Exception as e: yield e if line["message"]["author"]["role"] != "assistant": continue if line["message"]["content"]["content_type"] != "text": continue if line["message"]["metadata"]["message_type"] not in ("next", "continue", "variant"): continue conversation_id = line["conversation_id"] parent_id = line["message"]["id"] if response_fields: response_fields = False yield ResponseFields(conversation_id, parent_id, end_turn) if "parts" in line["message"]["content"]: new_message = line["message"]["content"]["parts"][0] if len(new_message) > last_message: yield new_message[last_message:] last_message = len(new_message) if "finish_details" in line["message"]["metadata"]: if line["message"]["metadata"]["finish_details"]["type"] == "stop": end_turn.end() if not auto_continue: break action = "continue" await asyncio.sleep(5) if history_disabled and auto_continue: await cls.delete_conversation(session, auth_headers, conversation_id) @classmethod def browse_access_token(cls, proxy: str = None, timeout: int = 1200) -> tuple[str, dict]: """ 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);" "document.cookie = 'access_token=' + accessToken + ';expires=' + expires.toUTCString() + ';path=/';" "return accessToken;" ) return access_token, get_driver_cookies(driver) 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}") 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, message_id: str, end_turn: EndTurn): self.conversation_id = conversation_id self.message_id = message_id self._end_turn = end_turn 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.is_end 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