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import time |
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import uuid |
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from selenium import webdriver |
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from selenium.webdriver.chrome.options import Options |
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from selenium.webdriver.common.by import By |
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from selenium.webdriver.support import expected_conditions as EC |
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from selenium.webdriver.support.ui import WebDriverWait |
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import click |
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import requests |
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from requests import get |
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from uuid import uuid4 |
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from re import findall |
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from requests.exceptions import RequestException |
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from curl_cffi.requests import get, RequestsError |
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import g4f |
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from random import randint |
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from PIL import Image |
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import io |
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import re |
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import json |
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import yaml |
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from ..AIutel import Optimizers |
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from ..AIutel import Conversation |
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from ..AIutel import AwesomePrompts, sanitize_stream |
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from ..AIbase import Provider, AsyncProvider |
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from Helpingai_T2 import Perplexity |
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from webscout import exceptions |
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from typing import Any, AsyncGenerator, Dict |
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import logging |
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import httpx |
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|
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class YEPCHAT(Provider): |
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def __init__( |
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self, |
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is_conversation: bool = True, |
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max_tokens: int = 600, |
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temperature: float = 0.6, |
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presence_penalty: int = 0, |
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frequency_penalty: int = 0, |
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top_p: float = 0.7, |
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model: str = "Mixtral-8x7B-Instruct-v0.1", |
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timeout: int = 30, |
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intro: str = None, |
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filepath: str = None, |
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update_file: bool = True, |
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proxies: dict = {}, |
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history_offset: int = 10250, |
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act: str = None, |
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): |
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"""Instantiates YEPCHAT |
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|
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Args: |
|
is_conversation (bool, optional): Flag for chatting conversationally. Defaults to True. |
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max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 600. |
|
temperature (float, optional): Charge of the generated text's randomness. Defaults to 0.6. |
|
presence_penalty (int, optional): Chances of topic being repeated. Defaults to 0. |
|
frequency_penalty (int, optional): Chances of word being repeated. Defaults to 0. |
|
top_p (float, optional): Sampling threshold during inference time. Defaults to 0.7. |
|
model (str, optional): LLM model name. Defaults to "gpt-3.5-turbo". |
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timeout (int, optional): Http request timeout. Defaults to 30. |
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intro (str, optional): Conversation introductory prompt. Defaults to None. |
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filepath (str, optional): Path to file containing conversation history. Defaults to None. |
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update_file (bool, optional): Add new prompts and responses to the file. Defaults to True. |
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proxies (dict, optional): Http request proxies. Defaults to {}. |
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history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250. |
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act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None. |
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""" |
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self.session = requests.Session() |
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self.is_conversation = is_conversation |
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self.max_tokens_to_sample = max_tokens |
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self.model = model |
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self.temperature = temperature |
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self.presence_penalty = presence_penalty |
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self.frequency_penalty = frequency_penalty |
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self.top_p = top_p |
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self.chat_endpoint = "https://api.yep.com/v1/chat/completions" |
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self.stream_chunk_size = 64 |
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self.timeout = timeout |
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self.last_response = {} |
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self.headers = { |
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"Accept": "*/*", |
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"Accept-Encoding": "gzip, deflate", |
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"Accept-Language": "en-US,en;q=0.9", |
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"Content-Type": "application/json; charset=utf-8", |
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"Origin": "https://yep.com", |
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"Referer": "https://yep.com/", |
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"User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36", |
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} |
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|
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self.__available_optimizers = ( |
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method |
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for method in dir(Optimizers) |
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if callable(getattr(Optimizers, method)) and not method.startswith("__") |
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) |
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self.session.headers.update(self.headers) |
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Conversation.intro = ( |
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AwesomePrompts().get_act( |
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act, raise_not_found=True, default=None, case_insensitive=True |
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) |
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if act |
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else intro or Conversation.intro |
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) |
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self.conversation = Conversation( |
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is_conversation, self.max_tokens_to_sample, filepath, update_file |
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) |
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self.conversation.history_offset = history_offset |
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self.session.proxies = proxies |
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|
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def ask( |
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self, |
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prompt: str, |
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stream: bool = False, |
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raw: bool = False, |
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optimizer: str = None, |
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conversationally: bool = False, |
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) -> dict: |
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"""Chat with AI |
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|
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Args: |
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prompt (str): Prompt to be send. |
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stream (bool, optional): Flag for streaming response. Defaults to False. |
|
raw (bool, optional): Stream back raw response as received. Defaults to False. |
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optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None. |
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conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False. |
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Returns: |
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dict : {} |
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```json |
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{ |
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"id": "cmpl-c61c1c88de4e4ad3a79134775d17ea0c", |
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"object": "chat.completion.chunk", |
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"created": 1713876886, |
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"model": "Mixtral-8x7B-Instruct-v0.1", |
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"choices": [ |
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{ |
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"index": 0, |
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"delta": { |
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"role": null, |
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"content": " Sure, I can help with that. Are you looking for information on how to start coding, or do you need help with a specific coding problem? We can discuss various programming languages like Python, JavaScript, Java, C++, or others. Please provide more details so I can assist you better." |
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}, |
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"finish_reason": null |
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} |
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] |
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} |
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``` |
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""" |
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conversation_prompt = self.conversation.gen_complete_prompt(prompt) |
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if optimizer: |
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if optimizer in self.__available_optimizers: |
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conversation_prompt = getattr(Optimizers, optimizer)( |
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conversation_prompt if conversationally else prompt |
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) |
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else: |
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raise Exception( |
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f"Optimizer is not one of {self.__available_optimizers}" |
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) |
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self.session.headers.update(self.headers) |
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payload = { |
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"stream": True, |
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"max_tokens": 1280, |
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"top_p": self.top_p, |
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"temperature": self.temperature, |
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"messages": [{"content": conversation_prompt, "role": "user"}], |
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"model": self.model, |
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} |
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|
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def for_stream(): |
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response = self.session.post( |
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self.chat_endpoint, json=payload, stream=True, timeout=self.timeout |
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) |
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if not response.ok: |
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raise exceptions.FailedToGenerateResponseError( |
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f"Failed to generate response - ({response.status_code}, {response.reason}) - {response.text}" |
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) |
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|
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message_load = "" |
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for value in response.iter_lines( |
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decode_unicode=True, |
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delimiter="" if raw else "data:", |
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chunk_size=self.stream_chunk_size, |
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): |
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try: |
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resp = json.loads(value) |
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incomplete_message = self.get_message(resp) |
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if incomplete_message: |
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message_load += incomplete_message |
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resp["choices"][0]["delta"]["content"] = message_load |
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self.last_response.update(resp) |
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yield value if raw else resp |
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elif raw: |
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yield value |
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except json.decoder.JSONDecodeError: |
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pass |
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self.conversation.update_chat_history( |
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prompt, self.get_message(self.last_response) |
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) |
|
|
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def for_non_stream(): |
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for _ in for_stream(): |
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pass |
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return self.last_response |
|
|
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return for_stream() if stream else for_non_stream() |
|
|
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def chat( |
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self, |
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prompt: str, |
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stream: bool = False, |
|
optimizer: str = None, |
|
conversationally: bool = False, |
|
) -> str: |
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"""Generate response `str` |
|
Args: |
|
prompt (str): Prompt to be send. |
|
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: |
|
str: Response generated |
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""" |
|
|
|
def for_stream(): |
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for response in self.ask( |
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prompt, True, optimizer=optimizer, conversationally=conversationally |
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): |
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yield self.get_message(response) |
|
|
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def for_non_stream(): |
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return self.get_message( |
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self.ask( |
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prompt, |
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False, |
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optimizer=optimizer, |
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conversationally=conversationally, |
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) |
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) |
|
|
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return for_stream() if stream else for_non_stream() |
|
|
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def get_message(self, response: dict) -> str: |
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"""Retrieves message only from response |
|
|
|
Args: |
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response (dict): Response generated by `self.ask` |
|
|
|
Returns: |
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str: Message extracted |
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""" |
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assert isinstance(response, dict), "Response should be of dict data-type only" |
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try: |
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if response["choices"][0].get("delta"): |
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return response["choices"][0]["delta"]["content"] |
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return response["choices"][0]["message"]["content"] |
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except KeyError: |
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return "" |
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class AsyncYEPCHAT(AsyncProvider): |
|
def __init__( |
|
self, |
|
is_conversation: bool = True, |
|
max_tokens: int = 600, |
|
temperature: float = 0.6, |
|
presence_penalty: int = 0, |
|
frequency_penalty: int = 0, |
|
top_p: float = 0.7, |
|
model: str = "Mixtral-8x7B-Instruct-v0.1", |
|
timeout: int = 30, |
|
intro: str = None, |
|
filepath: str = None, |
|
update_file: bool = True, |
|
proxies: dict = {}, |
|
history_offset: int = 10250, |
|
act: str = None, |
|
): |
|
"""Instantiates YEPCHAT |
|
|
|
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): Charge of the generated text's randomness. Defaults to 0.6. |
|
presence_penalty (int, optional): Chances of topic being repeated. Defaults to 0. |
|
frequency_penalty (int, optional): Chances of word being repeated. Defaults to 0. |
|
top_p (float, optional): Sampling threshold during inference time. Defaults to 0.7. |
|
model (str, optional): LLM model name. Defaults to "gpt-3.5-turbo". |
|
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.model = model |
|
self.temperature = temperature |
|
self.presence_penalty = presence_penalty |
|
self.frequency_penalty = frequency_penalty |
|
self.top_p = top_p |
|
self.chat_endpoint = "https://api.yep.com/v1/chat/completions" |
|
self.stream_chunk_size = 64 |
|
self.timeout = timeout |
|
self.last_response = {} |
|
self.headers = { |
|
"Accept": "*/*", |
|
"Accept-Encoding": "gzip, deflate", |
|
"Accept-Language": "en-US,en;q=0.9", |
|
"Content-Type": "application/json; charset=utf-8", |
|
"Origin": "https://yep.com", |
|
"Referer": "https://yep.com/", |
|
"User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36", |
|
} |
|
|
|
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 = httpx.AsyncClient( |
|
headers=self.headers, |
|
proxies=proxies, |
|
) |
|
|
|
async def ask( |
|
self, |
|
prompt: str, |
|
stream: bool = False, |
|
raw: bool = False, |
|
optimizer: str = None, |
|
conversationally: bool = False, |
|
) -> dict: |
|
"""Chat with AI asynchronously. |
|
|
|
Args: |
|
prompt (str): Prompt to be send. |
|
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: |
|
dict : {} |
|
```json |
|
{ |
|
"id": "cmpl-c61c1c88de4e4ad3a79134775d17ea0c", |
|
"object": "chat.completion.chunk", |
|
"created": 1713876886, |
|
"model": "Mixtral-8x7B-Instruct-v0.1", |
|
"choices": [ |
|
{ |
|
"index": 0, |
|
"delta": { |
|
"role": null, |
|
"content": " Sure, I can help with that. Are you looking for information on how to start coding, or do you need help with a specific coding problem? We can discuss various programming languages like Python, JavaScript, Java, C++, or others. Please provide more details so I can assist you better." |
|
}, |
|
"finish_reason": null |
|
} |
|
] |
|
} |
|
``` |
|
""" |
|
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}" |
|
) |
|
payload = { |
|
"stream": True, |
|
"max_tokens": 1280, |
|
"top_p": self.top_p, |
|
"temperature": self.temperature, |
|
"messages": [{"content": conversation_prompt, "role": "user"}], |
|
"model": self.model, |
|
} |
|
|
|
async def for_stream(): |
|
async with self.session.stream( |
|
"POST", self.chat_endpoint, json=payload, timeout=self.timeout |
|
) as response: |
|
if not response.is_success: |
|
raise exceptions.FailedToGenerateResponseError( |
|
f"Failed to generate response - ({response.status_code}, {response.reason_phrase}) - {response.text}" |
|
) |
|
|
|
message_load = "" |
|
async for value in response.aiter_lines(): |
|
try: |
|
resp = sanitize_stream(value) |
|
incomplete_message = await self.get_message(resp) |
|
if incomplete_message: |
|
message_load += incomplete_message |
|
resp["choices"][0]["delta"]["content"] = message_load |
|
self.last_response.update(resp) |
|
yield value if raw else resp |
|
elif raw: |
|
yield value |
|
except json.decoder.JSONDecodeError: |
|
pass |
|
|
|
self.conversation.update_chat_history( |
|
prompt, await self.get_message(self.last_response) |
|
) |
|
|
|
async def for_non_stream(): |
|
async for _ in for_stream(): |
|
pass |
|
return self.last_response |
|
|
|
return for_stream() if stream else await for_non_stream() |
|
|
|
async def chat( |
|
self, |
|
prompt: str, |
|
stream: bool = False, |
|
optimizer: str = None, |
|
conversationally: bool = False, |
|
) -> str: |
|
"""Generate response `str` asynchronously. |
|
Args: |
|
prompt (str): Prompt to be send. |
|
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: |
|
str: Response generated |
|
""" |
|
|
|
async def for_stream(): |
|
async_ask = await self.ask( |
|
prompt, True, optimizer=optimizer, conversationally=conversationally |
|
) |
|
|
|
async for response in async_ask: |
|
yield await self.get_message(response) |
|
|
|
async def for_non_stream(): |
|
return await self.get_message( |
|
await self.ask( |
|
prompt, |
|
False, |
|
optimizer=optimizer, |
|
conversationally=conversationally, |
|
) |
|
) |
|
|
|
return for_stream() if stream else await for_non_stream() |
|
|
|
async def get_message(self, response: dict) -> str: |
|
"""Retrieves message only from response |
|
|
|
Args: |
|
response (dict): Response generated by `self.ask` |
|
|
|
Returns: |
|
str: Message extracted |
|
""" |
|
assert isinstance(response, dict), "Response should be of dict data-type only" |
|
try: |
|
if response["choices"][0].get("delta"): |
|
return response["choices"][0]["delta"]["content"] |
|
return response["choices"][0]["message"]["content"] |
|
except KeyError: |
|
return "" |
