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import logging
from telegram import Update
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from telegram.ext import (
Updater,
CommandHandler,
ConversationHandler,
CallbackContext,
CallbackQueryHandler,
MessageHandler,
filters
)
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/DialoGPT-medium")
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
async def talk(self, input: str):
logging.info(f"{input}")
new_user_input_ids = self.tokenizer.encode(input(f"{input}") + 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))
async def process_conversation(self, update: Update, context: CallbackContext) -> int:
message = update.message.text
text = await self.talk(message)
await update.message.reply_text(f'{text}')
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