{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "78b0280b-6461-4fc0-bca3-81fe4d72e92b",
   "metadata": {},
   "source": [
    "# OpenAI API Chat Completions Inference for JAQKET dataset\n",
    "\n",
    "[OpenAI API](https://platform.openai.com/) の [ChatCompletions API](https://platform.openai.com/docs/guides/gpt/chat-completions-api) で [JAQKET](https://www.nlp.ecei.tohoku.ac.jp/projects/jaqket/) のデータセットに回答するコードです。\n",
    "\n",
    "Notebook を動かす前に、 OpenAI API の API KEY と Organization ID の確認が必要です。いずれも Manage Account のメニューから確認できます。取得した API KEY と Organization ID は `os.environ` などを使い事前に設定しておいてください。これらは機微な情報のため、 GitHub などに誤ってコミットしないよう注意してください。\n",
    "\n",
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c16bada-cad8-4514-aa72-072dc4b8965b",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install openai requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8a6a1d6-73e2-4c03-8172-2e143c83de12",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import requests\n",
    "import pandas as pd\n",
    "import openai\n",
    "\n",
    "\n",
    "OPENAI_ORGANIZATION = os.getenv(\"OPENAI_ORGANIZATION\")\n",
    "OPENAI_API_KEY = os.getenv(\"OPENAI_API_KEY\")\n",
    "JAQKET_DEV_DATASET = (\n",
    "    \"https://jaqket.s3.ap-northeast-1.amazonaws.com/data/aio_02/aio_02_dev_v1.0.jsonl\"\n",
    ")\n",
    "\n",
    "\n",
    "if OPENAI_ORGANIZATION is None or OPENAI_API_KEY is None:\n",
    "    raise Exception(\n",
    "        \"Please set the OPENAI_ORGANIZATION and OPENAI_API_KEY environment variables for organization and api key.\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e0c91fd-8064-4f08-bb97-37d655f30f3e",
   "metadata": {},
   "source": [
    "## Prepare dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dadd02d7-704c-426f-a2b1-def45b9d90c6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def read_jaqket_dev() -> pd.DataFrame:\n",
    "    file_name = os.path.basename(JAQKET_DEV_DATASET)\n",
    "    location = os.path.join(f\"data/{file_name}\")\n",
    "    if not os.path.exists(location):\n",
    "        response = requests.get(JAQKET_DEV_DATASET)\n",
    "        with open(location, mode=\"wb\") as f:\n",
    "            f.write(response.content)\n",
    "\n",
    "    return pd.read_json(location, lines=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c224fae-518f-46ed-99e3-98249222515b",
   "metadata": {},
   "source": [
    "## Answer to quiz by Chat Completions API (ChatGPT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "159dad9d-5d2d-4c41-a3e8-ffd438baa1dc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "def answer(model: str, question: str) -> str:\n",
    "    openai.organization = OPENAI_ORGANIZATION\n",
    "    openai.api_key = OPENAI_API_KEY\n",
    "\n",
    "    template = \"日本語のクイズに答えてください。\\n{instruction}\\n答えは「\"\n",
    "    prompt = template.format(instruction=question)\n",
    "\n",
    "    response = openai.ChatCompletion.create(\n",
    "        model=model,\n",
    "        # prompt=prompt,\n",
    "        messages=[\n",
    "            {\"role\": \"user\", \"content\": prompt},\n",
    "        ],\n",
    "        max_tokens=64,\n",
    "        temperature=0,\n",
    "        top_p=1,\n",
    "        n=1,\n",
    "        stop=\"\\n\",\n",
    "    )\n",
    "\n",
    "    _answer = response.choices[0][\"message\"][\"content\"].strip()\n",
    "    _answer = re.findall(\"「(.*?)」\", prompt + _answer)[-1]\n",
    "    return _answer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7263497e-071e-4064-b9b3-41196c10a96c",
   "metadata": {},
   "source": [
    "## Answer to dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bcac4083-091c-48e7-9e19-def56674d168",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "import time\n",
    "\n",
    "def answer_jaqket(\n",
    "    question_df: pd.DataFrame, model: str = \"gpt-3.5-turbo\"\n",
    ") -> pd.DataFrame:\n",
    "    chatgpt_answers = []\n",
    "    matches = []\n",
    "    for idx, row in question_df.iterrows():\n",
    "        while True:\n",
    "            try:\n",
    "                chatgpt_answer = answer(model, row[\"question\"])\n",
    "                time.sleep(0.5)\n",
    "                break\n",
    "            except Exception as e:\n",
    "                print(e)\n",
    "                time.sleep(1)\n",
    "        chatgpt_answers += [chatgpt_answer]\n",
    "        matches += [chatgpt_answer in row[\"answers\"]]\n",
    "\n",
    "    question_df[\"chatgpt_answer\"] = pd.Series(chatgpt_answers)\n",
    "    question_df[\"match\"] = pd.Series(matches)\n",
    "    return question_df\n",
    "\n",
    "\n",
    "answer_file_name = \"jaqket_answers_chat.csv\"\n",
    "answers = answer_jaqket(read_jaqket_dev(), model=\"gpt-3.5-turbo\")\n",
    "answers.to_csv(f\"data/{answer_file_name}\", index=False)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "a016cf95-dff3-4ed3-82b7-f019f013994b",
   "metadata": {},
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   "source": []
  }
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