import time import uuid from selenium import webdriver from selenium.webdriver.chrome.options import Options from selenium.webdriver.common.by import By from selenium.webdriver.support import expected_conditions as EC from selenium.webdriver.support.ui import WebDriverWait import click import requests from requests import get from uuid import uuid4 from re import findall from requests.exceptions import RequestException from curl_cffi.requests import get, RequestsError import g4f from random import randint from PIL import Image import io import re import json import yaml from ..AIutel import Optimizers from ..AIutel import Conversation from ..AIutel import AwesomePrompts, sanitize_stream from ..AIbase import Provider, AsyncProvider from webscout import exceptions from typing import Any, AsyncGenerator, Dict import logging import httpx #-----------------------------------------------xjai------------------------------------------- class Xjai(Provider): def __init__( self, is_conversation: bool = True, max_tokens: int = 600, temperature: float = 0.8, top_p: float = 1, timeout: int = 30, intro: str = None, filepath: str = None, update_file: bool = True, proxies: dict = {}, history_offset: int = 10250, act: str = None, ): """ Initializes the Xjai class for interacting with the Xjai AI chat API. Args: is_conversation (bool, optional): Flag for chatting conversationally. Defaults to True. max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 600. temperature (float, optional): The creativity level of the AI's response. Defaults to 0.8. top_p (float, optional): The probability threshold for token selection. Defaults to 1. timeout (int, optional): Http request timeout. Defaults to 30. intro (str, optional): Conversation introductory prompt. Defaults to None. filepath (str, optional): Path to file containing conversation history. Defaults to None. update_file (bool, optional): Add new prompts and responses to the file. Defaults to True. proxies (dict, optional): Http request proxies. Defaults to {}. history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250. act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None. """ self.session = requests.Session() self.is_conversation = is_conversation self.max_tokens_to_sample = max_tokens self.temperature = temperature self.top_p = top_p self.chat_endpoint = "https://p1api.xjai.pro/freeapi/chat-process" self.stream_chunk_size = 1 # Process response line by line self.timeout = timeout self.last_response = {} self.__available_optimizers = ( method for method in dir(Optimizers) if callable(getattr(Optimizers, method)) and not method.startswith("__") ) Conversation.intro = ( AwesomePrompts().get_act( act, raise_not_found=True, default=None, case_insensitive=True ) if act else intro or Conversation.intro ) self.conversation = Conversation( is_conversation, self.max_tokens_to_sample, filepath, update_file ) self.conversation.history_offset = history_offset self.session.proxies = proxies def ask( self, prompt: str, stream: bool = False, raw: bool = False, optimizer: str = None, conversationally: bool = False, ) -> Any: """ Sends a chat request to the Xjai AI chat API and returns the response. Args: prompt (str): The query to send to the AI. stream (bool, optional): Flag for streaming response. Defaults to False. raw (bool, optional): Stream back raw response as received. Defaults to False. optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None. conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False. Returns: Any: The response from the AI, either as a dictionary or a generator depending on the `stream` and `raw` parameters. """ conversation_prompt = self.conversation.gen_complete_prompt(prompt) if optimizer: if optimizer in self.__available_optimizers: conversation_prompt = getattr(Optimizers, optimizer)( conversation_prompt if conversationally else prompt ) else: raise Exception( f"Optimizer is not one of {self.__available_optimizers}" ) headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 " "(KHTML, like Gecko) Chrome/124.0.0.0 Safari/537.36" } payload = { "prompt": conversation_prompt + "\n\nReply in English Only", "systemMessage": "Reply in English Only", "temperature": self.temperature, "top_p": self.top_p } def generate_response(): response = self.session.post( self.chat_endpoint, headers=headers, json=payload, stream=True, timeout=self.timeout ) output = "" print_next = False for line in response.iter_lines(decode_unicode=True, chunk_size=self.stream_chunk_size): line_content = line.decode("utf-8") # Filter out irrelevant content if '[ChatAI](https://srv.aiflarepro.com/#/?cid=4111)' in line_content: continue if '&KFw6loC9Qvy&' in line_content: parts = line_content.split('&KFw6loC9Qvy&') if print_next: output += parts[0] print_next = False else: output += parts[1] print_next = True if len(parts) > 2: print_next = False elif print_next: output += line_content + '\n' # Update chat history self.conversation.update_chat_history(prompt, output) return output def for_stream(): response = generate_response() for line in response.splitlines(): yield line if raw else dict(text=line) def for_non_stream(): response = generate_response() return response if raw else dict(text=response) return for_stream() if stream else for_non_stream() def chat( self, prompt: str, stream: bool = False, optimizer: str = None, conversationally: bool = False, ) -> Any: """ Generates a response from the Xjai AI chat API. Args: prompt (str): The query to send to the AI. stream (bool, optional): Flag for streaming response. Defaults to False. optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None. conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False. Returns: Any: The response from the AI, either as a string or a generator depending on the `stream` parameter. """ def for_stream(): for response in self.ask( prompt, True, optimizer=optimizer, conversationally=conversationally ): yield self.get_message(response) def for_non_stream(): return self.get_message( self.ask( prompt, False, optimizer=optimizer, conversationally=conversationally, ) ) return for_stream() if stream else for_non_stream() def get_message(self, response: Any) -> str: """ Retrieves the message from the AI's response. Args: response (Any): The response from the AI, either a dictionary or a raw string. Returns: str: The extracted message from the AI's response. """ if isinstance(response, dict): return response["text"] else: # Assume raw string return response