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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}')
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