rexthecoder commited on
Commit
642e116
·
1 Parent(s): 3e64e0d

chore: fix

Browse files
Files changed (2) hide show
  1. main.py +1 -1
  2. src/agent/tools/conversation.py +32 -18
main.py CHANGED
@@ -43,7 +43,7 @@ class LoggingDisabled:
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  def main():
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  app = Application.builder().token(
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- '6207542226:AAGPOQrKiVdQJuHE0dQ1hKJm64ZXK-6z7-0',).build()
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  run_agent(
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  agent=GirlfriendGPT(
 
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  def main():
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  app = Application.builder().token(
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+ '6207542226:AAEeWfZzrMcGTiCmUkQSp3oXkedQJnrEaXc',).build()
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  run_agent(
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  agent=GirlfriendGPT(
src/agent/tools/conversation.py CHANGED
@@ -1,6 +1,6 @@
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  import logging
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  from telegram import Update
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- from transformers import AutoModelForCausalLM, AutoTokenizer
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  import torch
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  from telegram.ext import (
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  CallbackContext,
@@ -16,25 +16,39 @@ Output: A text
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  GET_CON = range(1)
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- class Conversation():
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- tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
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- model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
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-
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-
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- async def talk(self, message: str):
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- logging.info(f"{message}")
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- chat_history_ids = torch.tensor([], dtype=torch.long)
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- new_user_input_ids = self.tokenizer.encode(message + self.tokenizer.eos_token, return_tensors='pt')
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- bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1)
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- chat_history_ids =self.model.generate(bot_input_ids, max_length=1000, pad_token_id=self.tokenizer.eos_token_id)
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- return "{}".format(self.tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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-
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  async def process_conversation(self, update: Update, context: CallbackContext) -> int:
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  message = update.message.text
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- text = await self.talk(message)
 
 
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  await update.message.reply_text(f'{text}')
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-
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-
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-
 
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  import logging
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  from telegram import Update
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+ from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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  import torch
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  from telegram.ext import (
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  CallbackContext,
 
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  GET_CON = range(1)
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+ class Conversation():
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+ tokenizer = AutoTokenizer.from_pretrained(
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+ "microsoft/GODEL-v1_1-large-seq2seq")
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+ model = AutoModelForSeq2SeqLM.from_pretrained(
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+ "microsoft/GODEL-v1_1-large-seq2seq")
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+
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+ # async def talk(self, message: str):
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+ # logging.info(f"{message}")
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+ # chat_history_ids = torch.tensor([], dtype=torch.long)
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+ # new_user_input_ids = self.tokenizer.encode(message + self.tokenizer.eos_token, return_tensors='pt')
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+ # bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1)
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+ # chat_history_ids =self.model.generate(bot_input_ids, max_length=1000, pad_token_id=self.tokenizer.eos_token_id)
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+ # return "{}".format(self.tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))
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+
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+ def generate(self, instruction, knowledge, dialog):
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+ if knowledge != '':
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+ knowledge = '[KNOWLEDGE] ' + knowledge
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+ dialog = ' EOS '.join(dialog)
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+ query = f"{instruction} [CONTEXT] {dialog} {knowledge}"
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+ input_ids = self.tokenizer(f"{query}", return_tensors="pt").input_ids
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+ outputs = self.model.generate(
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+ input_ids, max_length=128,
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+ min_length=8,
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+ top_p=0.9,
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+ do_sample=True,
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+ )
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+ output = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
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+ return output
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  async def process_conversation(self, update: Update, context: CallbackContext) -> int:
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  message = update.message.text
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+ instruction = f'Instruction: given a dialog context, you need to response empathically.'
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+ knowledge = ''
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+ text = await self.generate(instruction, knowledge,message)
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  await update.message.reply_text(f'{text}')