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| import logging | |
| from telegram import Update | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| import torch | |
| 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 predict(self, input, history=[]): | |
| instruction = "Instruction: given a dialog context and related knowledge, you need to answer the question based on the knowledge." | |
| knowledge = '[KNOWLEDGE] ' | |
| s = list(sum(history, ())) | |
| s.append(input) | |
| dialog = ' EOS '.join(s) | |
| query = f"{instruction} [CONTEXT] {dialog} {knowledge}" | |
| input_ids = self.tokenizer.encode(f"{query}", return_tensors='pt') | |
| print(input, s) | |
| output = self.model.generate(input_ids, max_length=128, min_length=8, top_p=0.9, do_sample=True).tolist() | |
| response = self.tokenizer.decode(output[0], skip_special_tokens=True) | |
| history.append((input, response)) | |
| return response | |
| # 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.predict(message) | |
| await update.message.reply_text(f'{text}') | |