mirror of
https://github.com/xtekky/gpt4free.git
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596 lines
23 KiB
Python
596 lines
23 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 os
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try:
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from py_arkose_generator.arkose import get_values_for_request
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from async_property import async_cached_property
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has_requirements = True
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except ImportError:
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async_cached_property = property
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has_requirements = 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 ..helper import format_prompt, get_cookies
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from ...webdriver import get_browser, get_driver_cookies
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from ...typing import AsyncResult, Messages, Cookies, ImageType
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from ...requests import StreamSession
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from ...image import to_image, to_bytes, ImageResponse, ImageRequest
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from ...errors import MissingRequirementsError, MissingAuthError
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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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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"}
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_cookies: dict = {}
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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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history_disabled: bool = False,
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action: str = "next",
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conversation_id: str = None,
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parent_id: str = None,
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image: ImageType = None,
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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:
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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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history_disabled=history_disabled,
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action=action,
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conversation_id=conversation_id,
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parent_id=parent_id,
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image=image,
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response_fields=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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response.raise_for_status()
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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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response.raise_for_status()
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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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response.raise_for_status()
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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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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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else:
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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, prompt: str, 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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# Check if there is an image response
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if not image_request:
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# Create a content object with the text type and the prompt
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content = {"content_type": "text", "parts": [prompt]}
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else:
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# Create a content object with the multimodal text type and the image and the prompt
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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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}, prompt]
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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": "user"},
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"content": content,
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}]
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# Check if there is an image response
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if image_request:
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# Add the metadata object with the attachments
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messages[0]["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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file_id = first_part["asset_pointer"].split("file-service://", 1)[1]
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prompt = first_part["metadata"]["dalle"]["prompt"]
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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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response.raise_for_status()
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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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response.raise_for_status()
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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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access_token: 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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parent_id: str = None,
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image: ImageType = None,
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response_fields: 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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access_token (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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response_fields (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 not has_requirements:
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raise MissingRequirementsError('Install "py-arkose-generator" and "async_property" package')
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if not parent_id:
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parent_id = str(uuid.uuid4())
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if not cookies:
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cookies = cls._cookies or get_cookies("chat.openai.com", False)
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if not access_token and "access_token" in cookies:
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access_token = cookies["access_token"]
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if not access_token:
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login_url = os.environ.get("G4F_LOGIN_URL")
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if login_url:
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yield f"Please login: [ChatGPT]({login_url})\n\n"
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try:
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access_token, cookies = cls.browse_access_token(proxy)
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except MissingRequirementsError:
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raise MissingAuthError(f'Missing "access_token"')
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cls._cookies = cookies
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auth_headers = {"Authorization": f"Bearer {access_token}"}
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async with StreamSession(
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proxies={"https": proxy},
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impersonate="chrome110",
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timeout=timeout,
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headers={"Cookie": "; ".join(f"{k}={v}" for k, v in cookies.items())}
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) as session:
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try:
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image_response = None
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if image:
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image_response = await cls.upload_image(session, auth_headers, image, kwargs.get("image_name"))
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except Exception as e:
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yield e
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end_turn = EndTurn()
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model = cls.get_model(model or await cls.get_default_model(session, auth_headers))
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model = "text-davinci-002-render-sha" if model == "gpt-3.5-turbo" else model
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while not end_turn.is_end:
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arkose_token = await cls.get_arkose_token(session)
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data = {
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"action": action,
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"arkose_token": arkose_token,
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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,
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}
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if action != "continue":
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prompt = format_prompt(messages) if not conversation_id else messages[-1]["content"]
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data["messages"] = cls.create_messages(prompt, image_response)
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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={
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"Accept": "text/event-stream",
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"OpenAI-Sentinel-Arkose-Token": arkose_token,
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**auth_headers
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}
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) as response:
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if not response.ok:
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raise RuntimeError(f"Response {response.status}: {await response.text()}")
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last_message: int = 0
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async for line in response.iter_lines():
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if not line.startswith(b"data: "):
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continue
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elif line.startswith(b"data: [DONE]"):
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break
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try:
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line = json.loads(line[6:])
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except:
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continue
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if "message" not in line:
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continue
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if "error" in line and line["error"]:
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raise RuntimeError(line["error"])
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if "message_type" not in line["message"]["metadata"]:
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continue
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try:
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image_response = await cls.get_generated_image(session, auth_headers, line)
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if image_response:
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yield image_response
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except Exception as e:
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yield e
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if line["message"]["author"]["role"] != "assistant":
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continue
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if line["message"]["content"]["content_type"] != "text":
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continue
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if line["message"]["metadata"]["message_type"] not in ("next", "continue", "variant"):
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continue
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conversation_id = line["conversation_id"]
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parent_id = line["message"]["id"]
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if response_fields:
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response_fields = False
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yield ResponseFields(conversation_id, parent_id, end_turn)
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if "parts" in line["message"]["content"]:
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new_message = line["message"]["content"]["parts"][0]
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if len(new_message) > last_message:
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yield new_message[last_message:]
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last_message = len(new_message)
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if "finish_details" in line["message"]["metadata"]:
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if line["message"]["metadata"]["finish_details"]["type"] == "stop":
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end_turn.end()
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if not auto_continue:
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break
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action = "continue"
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await asyncio.sleep(5)
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if history_disabled and auto_continue:
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await cls.delete_conversation(session, auth_headers, conversation_id)
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@classmethod
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def browse_access_token(cls, proxy: str = None, timeout: int = 1200) -> tuple[str, dict]:
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"""
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Browse to obtain an access token.
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Args:
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proxy (str): Proxy to use for browsing.
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Returns:
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tuple[str, dict]: A tuple containing the access token and cookies.
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"""
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driver = get_browser(proxy=proxy)
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try:
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driver.get(f"{cls.url}/")
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WebDriverWait(driver, timeout).until(EC.presence_of_element_located((By.ID, "prompt-textarea")))
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access_token = driver.execute_script(
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"let session = await fetch('/api/auth/session');"
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"let data = await session.json();"
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"let accessToken = data['accessToken'];"
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"let expires = new Date(); expires.setTime(expires.getTime() + 60 * 60 * 4);"
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"document.cookie = 'access_token=' + accessToken + ';expires=' + expires.toUTCString() + ';path=/';"
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"return accessToken;"
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)
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return access_token, get_driver_cookies(driver)
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finally:
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driver.close()
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@classmethod
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async def get_arkose_token(cls, session: StreamSession) -> str:
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"""
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Obtain an Arkose token for the session.
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Args:
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session (StreamSession): The session object.
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Returns:
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str: The Arkose token.
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Raises:
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RuntimeError: If unable to retrieve the token.
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"""
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config = {
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"pkey": "3D86FBBA-9D22-402A-B512-3420086BA6CC",
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"surl": "https://tcr9i.chat.openai.com",
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"headers": {
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"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'
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},
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"site": cls.url,
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}
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args_for_request = get_values_for_request(config)
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async with session.post(**args_for_request) as response:
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response.raise_for_status()
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decoded_json = await response.json()
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if "token" in decoded_json:
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return decoded_json["token"]
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raise RuntimeError(f"Response: {decoded_json}")
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|
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class EndTurn:
|
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"""
|
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Class to represent the end of a conversation turn.
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"""
|
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def __init__(self):
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self.is_end = False
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|
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def end(self):
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self.is_end = True
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class ResponseFields:
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"""
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Class to encapsulate response fields.
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"""
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def __init__(self, conversation_id: str, message_id: str, end_turn: EndTurn):
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self.conversation_id = conversation_id
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self.message_id = message_id
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self._end_turn = end_turn
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class Response():
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"""
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Class to encapsulate a response from the chat service.
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"""
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def __init__(
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self,
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generator: AsyncResult,
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action: str,
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messages: Messages,
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options: dict
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):
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self._generator = generator
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self.action = action
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self.is_end = False
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self._message = None
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self._messages = messages
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self._options = options
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self._fields = None
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async def generator(self):
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if self._generator:
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self._generator = None
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chunks = []
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async for chunk in self._generator:
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if isinstance(chunk, ResponseFields):
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self._fields = chunk
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else:
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yield chunk
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chunks.append(str(chunk))
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self._message = "".join(chunks)
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if not self._fields:
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raise RuntimeError("Missing response fields")
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self.is_end = self._fields._end_turn.is_end
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def __aiter__(self):
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return self.generator()
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@async_cached_property
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async def message(self) -> str:
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await self.generator()
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return self._message
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async def get_fields(self):
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await self.generator()
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return {"conversation_id": self._fields.conversation_id, "parent_id": self._fields.message_id}
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async def next(self, prompt: str, **kwargs) -> Response:
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return await OpenaiChat.create(
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**self._options,
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prompt=prompt,
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messages=await self.messages,
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action="next",
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**await self.get_fields(),
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**kwargs
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)
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async def do_continue(self, **kwargs) -> Response:
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fields = await self.get_fields()
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if self.is_end:
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raise RuntimeError("Can't continue message. Message already finished.")
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return await OpenaiChat.create(
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**self._options,
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messages=await self.messages,
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action="continue",
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**fields,
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**kwargs
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)
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async def variant(self, **kwargs) -> Response:
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if self.action != "next":
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raise RuntimeError("Can't create variant from continue or variant request.")
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return await OpenaiChat.create(
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**self._options,
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messages=self._messages,
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action="variant",
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**await self.get_fields(),
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**kwargs
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)
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@async_cached_property
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async def messages(self):
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messages = self._messages
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messages.append({"role": "assistant", "content": await self.message})
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return messages |