src/agent/tools/conversation.py

import logging
from telegram import Update
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from telegram.ext import (
    CallbackContext,
)

NAME = "Conversation"

DESCRIPTION = """
Useful for building up conversation. 
Input: A normal chat text
Output: A text
"""

GET_CON = range(1)


class Conversation():
    tokenizer = AutoTokenizer.from_pretrained(
        "microsoft/GODEL-v1_1-large-seq2seq")
    model = AutoModelForSeq2SeqLM.from_pretrained(
        "microsoft/GODEL-v1_1-large-seq2seq")

    # async def talk(self, message: str):
    #     logging.info(f"{message}")
    #     chat_history_ids = torch.tensor([], dtype=torch.long)
    #     new_user_input_ids = self.tokenizer.encode(message + self.tokenizer.eos_token, return_tensors='pt')
    #     bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1)
    #     chat_history_ids =self.model.generate(bot_input_ids, max_length=1000, pad_token_id=self.tokenizer.eos_token_id)
    #     return "{}".format(self.tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))

    def generate(self, instruction, knowledge, dialog):
        if knowledge != '':
            knowledge = '[KNOWLEDGE] ' + knowledge
        dialog = ' EOS '.join(dialog)
        query = f"{instruction} [CONTEXT] {dialog} {knowledge}"
        input_ids = self.tokenizer(f"{query}", return_tensors="pt").input_ids
        outputs = self.model.generate(
            input_ids, max_length=128,
            min_length=8,
            top_p=0.9,
            do_sample=True,
        )
        output = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        return output

    async def process_conversation(self, update: Update, context: CallbackContext) -> int:
        message = update.message.text
        instruction = f'Instruction: given a dialog context, you need to response empathically.'
        knowledge = ''
        dialog = []
        dialog .append(message)
        text = self.generate(instruction, knowledge, dialog)
        await update.message.reply_text(f'{text}')