{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "297ea6c7-1eae-47fc-8fa3-d49e6d3deb6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import torch\n",
    "from transformers import AutoModelForSequenceClassification, AutoTokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "45ccf708-2a0b-43a1-bf5f-45294ab205d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"twiz-data/all_intents.json\", 'r') as json_in:\n",
    "    data = json.load(json_in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d9875b16-36f8-4289-9ddf-6907f74a975c",
   "metadata": {},
   "outputs": [],
   "source": [
    "id_to_intent, intent_to_id = dict(), dict()\n",
    "for i, intent in enumerate(data):\n",
    "    id_to_intent[i] = intent\n",
    "    intent_to_id[intent] = i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "01a87f85-e4d7-454c-b645-bf252161d458",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = AutoModelForSequenceClassification.from_pretrained(\"roberta-based/checkpoint-925\", num_labels=len(data), id2label=id_to_intent, label2id=intent_to_id)\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"tokenizer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f29489cf-fa4b-453e-8922-6e972db1cc7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NextStepIntent\n"
     ]
    }
   ],
   "source": [
    "model_in = tokenizer(\"I really really wanna go to the next step\", return_tensors='pt')\n",
    "with torch.no_grad():\n",
    "    logits = model(**model_in).logits\n",
    "    predicted_class_id = logits.argmax().item()\n",
    "    print(model.config.id2label[predicted_class_id])\n"
   ]
  }
 ],
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   "language": "python",
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