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82 lines
3.6 KiB
82 lines
3.6 KiB
import asyncio
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import os, tempfile
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import logging
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import json
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import requests
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from transformers import AutoTokenizer, AutoConfig
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from huggingface_hub import hf_hub_download
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import io
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import base64
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from PIL import Image, PngImagePlugin
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logger = logging.getLogger(__name__)
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async def get_full_prompt(simple_prompt: str, bot, chat_history):
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# https://github.com/ggerganov/llama.cpp/tree/master/examples
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## prompt = "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n"
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# prompt = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\n"
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# #"BEGINNING OF CONVERSATION:"
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# prompt += "### Human: " + simple_prompt + "\n"
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# prompt += "### Assistant:"
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prompt = f"This is a transcript of a 1000 page, never ending conversation between {bot.user_name} and the cute and helpful AI assistant {bot.name}. {bot.name} is a girl who is an AI running on the users computer.\n"
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prompt += f"{bot.name} can think for herself without the user seeing her thoughts by adding a /think prefix to her output. She uses this to reason about the world and to think about what she should say next.\n"
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prompt += f"{bot.name} is always coherent and makes sense, but if she isn't sure if what she is saying is correct she will ask the user for help.\n"
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prompt += f"{bot.name} is a very helpful AI and will help the user with anything they need, she is also very friendly and will try to make the user feel better if they are sad.\n"
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prompt += f"{bot.name} is also very curious and will ask the user a lot of questions about themselves and their life, she will also try to make the user like her.\n"
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prompt += f"\n"
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#prompt += f"{bot.user_name}: " + simple_prompt + "\n"
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#prompt += f"{bot.name}:"
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MAX_TOKENS = 2048
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max_new_tokens = 200
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total_num_tokens = await num_tokens(prompt)
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total_num_tokens += await num_tokens(f"{bot.user_name}: " + simple_prompt + "\n{bot.name}:")
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visible_history = []
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current_message = True
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for key, chat_item in reversed(chat_history.chat_history.items()):
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if current_message:
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current_message = False
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continue
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if chat_item.message["en"].startswith('!begin'):
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break
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if chat_item.message["en"].startswith('!'):
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continue
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if chat_item.message["en"].startswith('<ERROR>'):
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continue
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#if chat_item.message["en"] == bot.greeting:
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# continue
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if chat_item.num_tokens == None:
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chat_item.num_tokens = await num_tokens("{}: {}".format(chat_item.user_name, chat_item.message["en"]))
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# TODO: is it MAX_TOKENS or MAX_TOKENS - max_new_tokens??
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logger.debug(f"History: " + str(chat_item) + " [" + str(chat_item.num_tokens) + "]")
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if total_num_tokens + chat_item.num_tokens < MAX_TOKENS - max_new_tokens:
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visible_history.append(chat_item)
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total_num_tokens += chat_item.num_tokens
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else:
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break
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visible_history = reversed(visible_history)
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#prompt += bot.name + ": " + bot.greeting + "\n"
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for chat_item in visible_history:
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if chat_item.is_own_message:
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prompt += bot.name + ": " + chat_item.message["en"] + "\n"
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else:
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prompt += f"{bot.user_name}: " + chat_item.message["en"] + "\n"
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prompt += f"{bot.user_name}: " + simple_prompt + "\n"
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prompt += f"{bot.name}:"
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return prompt
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async def num_tokens(input_text: str):
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return await estimate_num_tokens(input_text)
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async def estimate_num_tokens(input_text: str):
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return len(input_text)//4+1
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