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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import pipeline, Conversation\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading\n",
    "tok = AutoTokenizer.from_pretrained(\"saved_model\")\n",
    "mod = AutoModelForCausalLM.from_pretrained(\"saved_model\")\n",
    "\n",
    "chatbot = pipeline(\"conversational\", model = mod, tokenizer = tok)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set `padding_side='left'` when initializing the tokenizer.\n",
      "A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set `padding_side='left'` when initializing the tokenizer.\n",
      "A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set `padding_side='left'` when initializing the tokenizer.\n",
      "A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set `padding_side='left'` when initializing the tokenizer.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Conversation id: 20e0c3eb-e549-4c61-96d5-831eb3af1933 \n",
       "user >> Hello \n",
       "bot >> Hi, I'm here to talk to you. \n",
       "user >> How are you? \n",
       "bot >> I'm fine. How are you? \n",
       "user >> I'm good, do you want to watch a movie today? \n",
       "bot >> Sure, I'll watch it. What movie? \n",
       "user >> What about Lalaland? \n",
       "bot >> That's a good one. I'll watch it. "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_input = \"Hello\"\n",
    "conversation = Conversation(user_input)\n",
    "conversation = chatbot(conversation, pad_token_id=chatbot.tokenizer.eos_token_id)\n",
    "reply = conversation.generated_responses\n",
    "reply = reply[0].split(\"  \")[0]\n",
    "conversation.generated_responses = [reply]\n",
    "\n",
    "conversation.add_user_input(\"How are you?\")\n",
    "conversation = chatbot(conversation, pad_token_id=chatbot.tokenizer.eos_token_id)\n",
    "conversation.add_user_input(\"I'm good, do you want to watch a movie today?\")\n",
    "conversation = chatbot(conversation, pad_token_id=chatbot.tokenizer.eos_token_id)\n",
    "conversation.add_user_input(\"What about Lalaland?\")\n",
    "conversation = chatbot(conversation, pad_token_id=chatbot.tokenizer.eos_token_id)\n",
    "\n",
    "conversation"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}