mirror of https://github.com/xtekky/gpt4free.git
719 lines
28 KiB
Python
719 lines
28 KiB
Python
from __future__ import annotations
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import asyncio
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import uuid
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import json
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import base64
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import time
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from aiohttp import ClientWebSocketResponse
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from copy import copy
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try:
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import webview
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has_webview = True
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except ImportError:
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has_webview = False
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try:
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from selenium.webdriver.common.by import By
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from selenium.webdriver.support.ui import WebDriverWait
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from selenium.webdriver.support import expected_conditions as EC
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except ImportError:
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pass
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from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
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from ...webdriver import get_browser
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from ...typing import AsyncResult, Messages, Cookies, ImageType, Union, AsyncIterator
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from ...requests import get_args_from_browser, raise_for_status
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from ...requests.aiohttp import StreamSession
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from ...image import to_image, to_bytes, ImageResponse, ImageRequest
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from ...errors import MissingAuthError
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from ...providers.conversation import BaseConversation
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from ..openai.har_file import getArkoseAndAccessToken
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from ... import debug
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class OpenaiChat(AsyncGeneratorProvider, ProviderModelMixin):
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"""A class for creating and managing conversations with OpenAI chat service"""
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url = "https://chat.openai.com"
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working = True
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needs_auth = True
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supports_gpt_35_turbo = True
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supports_gpt_4 = True
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supports_message_history = True
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supports_system_message = True
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default_model = None
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models = ["gpt-3.5-turbo", "gpt-4", "gpt-4-gizmo"]
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model_aliases = {"text-davinci-002-render-sha": "gpt-3.5-turbo", "": "gpt-3.5-turbo"}
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_api_key: str = None
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_headers: dict = None
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_cookies: Cookies = None
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_expires: int = None
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@classmethod
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async def create(
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cls,
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prompt: str = None,
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model: str = "",
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messages: Messages = [],
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**kwargs
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) -> Response:
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"""
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Create a new conversation or continue an existing one
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Args:
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prompt: The user input to start or continue the conversation
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model: The name of the model to use for generating responses
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messages: The list of previous messages in the conversation
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history_disabled: A flag indicating if the history and training should be disabled
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action: The type of action to perform, either "next", "continue", or "variant"
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conversation_id: The ID of the existing conversation, if any
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parent_id: The ID of the parent message, if any
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image: The image to include in the user input, if any
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**kwargs: Additional keyword arguments to pass to the generator
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Returns:
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A Response object that contains the generator, action, messages, and options
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"""
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# Add the user input to the messages list
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if prompt is not None:
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messages.append({
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"role": "user",
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"content": prompt
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})
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generator = cls.create_async_generator(
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model,
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messages,
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return_conversation=True,
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**kwargs
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)
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return Response(
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generator,
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action,
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messages,
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kwargs
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)
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@classmethod
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async def upload_image(
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cls,
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session: StreamSession,
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headers: dict,
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image: ImageType,
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image_name: str = None
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) -> ImageRequest:
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"""
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Upload an image to the service and get the download URL
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Args:
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session: The StreamSession object to use for requests
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headers: The headers to include in the requests
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image: The image to upload, either a PIL Image object or a bytes object
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Returns:
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An ImageRequest object that contains the download URL, file name, and other data
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"""
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# Convert the image to a PIL Image object and get the extension
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image = to_image(image)
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extension = image.format.lower()
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# Convert the image to a bytes object and get the size
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data_bytes = to_bytes(image)
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data = {
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"file_name": image_name if image_name else f"{image.width}x{image.height}.{extension}",
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"file_size": len(data_bytes),
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"use_case": "multimodal"
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}
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# Post the image data to the service and get the image data
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async with session.post(f"{cls.url}/backend-api/files", json=data, headers=headers) as response:
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cls._update_request_args()
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await raise_for_status(response)
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image_data = {
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**data,
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**await response.json(),
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"mime_type": f"image/{extension}",
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"extension": extension,
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"height": image.height,
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"width": image.width
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}
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# Put the image bytes to the upload URL and check the status
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async with session.put(
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image_data["upload_url"],
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data=data_bytes,
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headers={
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"Content-Type": image_data["mime_type"],
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"x-ms-blob-type": "BlockBlob"
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}
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) as response:
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await raise_for_status(response)
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# Post the file ID to the service and get the download URL
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async with session.post(
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f"{cls.url}/backend-api/files/{image_data['file_id']}/uploaded",
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json={},
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headers=headers
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) as response:
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cls._update_request_args(session)
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await raise_for_status(response)
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image_data["download_url"] = (await response.json())["download_url"]
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return ImageRequest(image_data)
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@classmethod
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async def get_default_model(cls, session: StreamSession, headers: dict):
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"""
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Get the default model name from the service
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Args:
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session: The StreamSession object to use for requests
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headers: The headers to include in the requests
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Returns:
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The default model name as a string
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"""
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if not cls.default_model:
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async with session.get(f"{cls.url}/backend-api/models", headers=headers) as response:
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cls._update_request_args(session)
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await raise_for_status(response)
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data = await response.json()
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if "categories" in data:
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cls.default_model = data["categories"][-1]["default_model"]
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return cls.default_model
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raise RuntimeError(f"Response: {data}")
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return cls.default_model
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@classmethod
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def create_messages(cls, messages: Messages, image_request: ImageRequest = None):
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"""
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Create a list of messages for the user input
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Args:
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prompt: The user input as a string
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image_response: The image response object, if any
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Returns:
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A list of messages with the user input and the image, if any
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"""
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# Create a message object with the user role and the content
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messages = [{
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"id": str(uuid.uuid4()),
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"author": {"role": message["role"]},
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"content": {"content_type": "text", "parts": [message["content"]]},
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} for message in messages]
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# Check if there is an image response
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if image_request is not None:
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# Change content in last user message
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messages[-1]["content"] = {
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"content_type": "multimodal_text",
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"parts": [{
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"asset_pointer": f"file-service://{image_request.get('file_id')}",
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"height": image_request.get("height"),
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"size_bytes": image_request.get("file_size"),
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"width": image_request.get("width"),
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}, messages[-1]["content"]["parts"][0]]
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}
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# Add the metadata object with the attachments
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messages[-1]["metadata"] = {
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"attachments": [{
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"height": image_request.get("height"),
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"id": image_request.get("file_id"),
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"mimeType": image_request.get("mime_type"),
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"name": image_request.get("file_name"),
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"size": image_request.get("file_size"),
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"width": image_request.get("width"),
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}]
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}
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return messages
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@classmethod
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async def get_generated_image(cls, session: StreamSession, headers: dict, line: dict) -> ImageResponse:
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"""
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Retrieves the image response based on the message content.
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This method processes the message content to extract image information and retrieves the
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corresponding image from the backend API. It then returns an ImageResponse object containing
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the image URL and the prompt used to generate the image.
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Args:
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session (StreamSession): The StreamSession object used for making HTTP requests.
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headers (dict): HTTP headers to be used for the request.
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line (dict): A dictionary representing the line of response that contains image information.
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Returns:
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ImageResponse: An object containing the image URL and the prompt, or None if no image is found.
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Raises:
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RuntimeError: If there'san error in downloading the image, including issues with the HTTP request or response.
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"""
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if "parts" not in line["message"]["content"]:
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return
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first_part = line["message"]["content"]["parts"][0]
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if "asset_pointer" not in first_part or "metadata" not in first_part:
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return
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if first_part["metadata"] is None:
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return
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prompt = first_part["metadata"]["dalle"]["prompt"]
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file_id = first_part["asset_pointer"].split("file-service://", 1)[1]
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try:
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async with session.get(f"{cls.url}/backend-api/files/{file_id}/download", headers=headers) as response:
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cls._update_request_args(session)
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await raise_for_status(response)
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download_url = (await response.json())["download_url"]
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return ImageResponse(download_url, prompt)
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except Exception as e:
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raise RuntimeError(f"Error in downloading image: {e}")
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@classmethod
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async def delete_conversation(cls, session: StreamSession, headers: dict, conversation_id: str):
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"""
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Deletes a conversation by setting its visibility to False.
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This method sends an HTTP PATCH request to update the visibility of a conversation.
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It's used to effectively delete a conversation from being accessed or displayed in the future.
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Args:
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session (StreamSession): The StreamSession object used for making HTTP requests.
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headers (dict): HTTP headers to be used for the request.
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conversation_id (str): The unique identifier of the conversation to be deleted.
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Raises:
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HTTPError: If the HTTP request fails or returns an unsuccessful status code.
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"""
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async with session.patch(
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f"{cls.url}/backend-api/conversation/{conversation_id}",
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json={"is_visible": False},
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headers=headers
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) as response:
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cls._update_request_args(session)
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...
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@classmethod
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async def create_async_generator(
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cls,
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model: str,
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messages: Messages,
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proxy: str = None,
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timeout: int = 120,
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api_key: str = None,
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cookies: Cookies = None,
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auto_continue: bool = False,
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history_disabled: bool = True,
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action: str = "next",
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conversation_id: str = None,
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conversation: Conversation = None,
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parent_id: str = None,
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image: ImageType = None,
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image_name: str = None,
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return_conversation: bool = False,
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**kwargs
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) -> AsyncResult:
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"""
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Create an asynchronous generator for the conversation.
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Args:
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model (str): The model name.
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messages (Messages): The list of previous messages.
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proxy (str): Proxy to use for requests.
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timeout (int): Timeout for requests.
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api_key (str): Access token for authentication.
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cookies (dict): Cookies to use for authentication.
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auto_continue (bool): Flag to automatically continue the conversation.
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history_disabled (bool): Flag to disable history and training.
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action (str): Type of action ('next', 'continue', 'variant').
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conversation_id (str): ID of the conversation.
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parent_id (str): ID of the parent message.
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image (ImageType): Image to include in the conversation.
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return_conversation (bool): Flag to include response fields in the output.
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**kwargs: Additional keyword arguments.
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Yields:
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AsyncResult: Asynchronous results from the generator.
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Raises:
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RuntimeError: If an error occurs during processing.
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"""
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if parent_id is None:
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parent_id = str(uuid.uuid4())
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async with StreamSession(
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proxies={"https": proxy},
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impersonate="chrome",
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timeout=timeout
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) as session:
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api_key = kwargs["access_token"] if "access_token" in kwargs else api_key
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if api_key is not None:
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cls._create_request_args(cookies)
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cls._set_api_key(api_key)
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if cls.default_model is None and cls._headers is not None:
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try:
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if not model:
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cls.default_model = cls.get_model(await cls.get_default_model(session, cls._headers))
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else:
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cls.default_model = cls.get_model(model)
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except Exception as e:
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if debug.logging:
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print("OpenaiChat: Load default_model failed")
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print(f"{e.__class__.__name__}: {e}")
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arkose_token = None
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if cls.default_model is None:
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arkose_token, api_key, cookies = await getArkoseAndAccessToken(proxy)
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cls._create_request_args(cookies)
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cls._set_api_key(api_key)
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cls.default_model = cls.get_model(await cls.get_default_model(session, cls._headers))
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async with session.post(
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f"{cls.url}/backend-api/sentinel/chat-requirements",
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json={"conversation_mode_kind": "primary_assistant"},
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headers=cls._headers
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) as response:
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cls._update_request_args(session)
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await raise_for_status(response)
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data = await response.json()
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blob = data["arkose"]["dx"]
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need_arkose = data["arkose"]["required"]
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chat_token = data["token"]
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if need_arkose and arkose_token is None:
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arkose_token, api_key, cookies = await getArkoseAndAccessToken(proxy)
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cls._create_request_args(cookies)
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cls._set_api_key(api_key)
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if arkose_token is None:
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raise MissingAuthError("No arkose token found in .har file")
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try:
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image_request = await cls.upload_image(session, cls._headers, image, image_name) if image else None
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except Exception as e:
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if debug.logging:
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print("OpenaiChat: Upload image failed")
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print(f"{e.__class__.__name__}: {e}")
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model = cls.get_model(model).replace("gpt-3.5-turbo", "text-davinci-002-render-sha")
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fields = Conversation() if conversation is None else copy(conversation)
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fields.finish_reason = None
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while fields.finish_reason is None:
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conversation_id = conversation_id if fields.conversation_id is None else fields.conversation_id
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parent_id = parent_id if fields.message_id is None else fields.message_id
|
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websocket_request_id = str(uuid.uuid4())
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data = {
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"action": action,
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"conversation_mode": {"kind": "primary_assistant"},
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"force_paragen": False,
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"force_rate_limit": False,
|
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"conversation_id": conversation_id,
|
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"parent_message_id": parent_id,
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"model": model,
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"history_and_training_disabled": history_disabled and not auto_continue and not return_conversation,
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"websocket_request_id": websocket_request_id
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}
|
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if action != "continue":
|
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messages = messages if conversation_id is None else [messages[-1]]
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data["messages"] = cls.create_messages(messages, image_request)
|
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headers = {
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"Accept": "text/event-stream",
|
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"OpenAI-Sentinel-Chat-Requirements-Token": chat_token,
|
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**cls._headers
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}
|
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if need_arkose:
|
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headers["OpenAI-Sentinel-Arkose-Token"] = arkose_token
|
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async with session.post(
|
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f"{cls.url}/backend-api/conversation",
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json=data,
|
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headers=headers
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) as response:
|
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cls._update_request_args(session)
|
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await raise_for_status(response)
|
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async for chunk in cls.iter_messages_chunk(response.iter_lines(), session, fields):
|
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if return_conversation:
|
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return_conversation = False
|
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yield fields
|
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yield chunk
|
|
if not auto_continue:
|
|
break
|
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action = "continue"
|
|
await asyncio.sleep(5)
|
|
if history_disabled and auto_continue and not return_conversation:
|
|
await cls.delete_conversation(session, cls._headers, fields.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
|
|
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 _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 = {
|
|
"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'
|
|
}
|
|
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 |