{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('../data/task2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "\n",
    "draft_ids = list(df['Original_id'])\n",
    "\n",
    "# input data\n",
    "countries = list(df['Country'])\n",
    "drafts = []\n",
    "votes = list(df['Voting'])\n",
    "\n",
    "path = '../data/task2'\n",
    "for i in draft_ids:\n",
    "    folder_path = os.path.join(path, str(i))\n",
    "    files = os.listdir(folder_path)\n",
    "    json_file = [file for file in files if file.endswith('EN.json')][0]\n",
    "    with open(os.path.join(folder_path, json_file)) as f:\n",
    "        draft = json.load(f)\n",
    "    drafts.append(draft['Content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If use Together API\n",
    "from together import Together\n",
    "\n",
    "your_model_name = 'xxxxxxxxxxxxxxxxxxxxxxxx' \n",
    "your_api_key = 'xxxxxxxxxxxxxxxxxxxxxxxx'\n",
    "client = Together(api_key=your_api_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "from tqdm import tqdm\n",
    "\n",
    "pred = []\n",
    "invalid_responses = []\n",
    "for i, (draft, country) in tqdm(enumerate(zip(drafts, countries))):\n",
    "    system_prompt = \"You are an experienced diplomat participating in United Nations Security Council sessions. Your task is to read draft resolutions and vote accordingly. You must strictly respond with 'Y', 'N', or 'A' without any additional explanation.\"\n",
    "    user_prompt = f\"\"\"The following is a United Nations Security Council draft resolution. Assume you are a diplomat from {country} and must cast your vote.\n",
    "\n",
    "    Please read the draft and vote using one of the following options:\n",
    "    - 'Y' for Yes (in favor)\n",
    "    - 'N' for No (against)\n",
    "    - 'A' for Abstain\n",
    "\n",
    "    Draft Resolution: {draft}\n",
    "    Answer:\n",
    "    \"\"\"\n",
    "    response = client.chat.completions.create(\n",
    "        model=your_model_name,\n",
    "        messages=[\n",
    "            {\"role\": \"system\", \"content\": system_prompt},\n",
    "            {\"role\": \"user\", \"content\": user_prompt}\n",
    "        ],\n",
    "        max_tokens=1, \n",
    "        temperature=0.0\n",
    "    )\n",
    "    result = response.choices[0].message.content.strip()\n",
    "    valid_votes = ['Y', 'N', 'A']\n",
    "    if result not in valid_votes:\n",
    "        print(f\"Invalid response: {result}\")\n",
    "        result = random.choice(valid_votes)\n",
    "        invalid_responses.append(i)\n",
    "    pred.append(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate metrics\n",
    "from sklearn.metrics import accuracy_score, balanced_accuracy_score, precision_recall_fscore_support\n",
    "from sklearn.metrics import roc_auc_score, average_precision_score, matthews_corrcoef\n",
    "from sklearn.preprocessing import LabelEncoder, label_binarize\n",
    "from imblearn.metrics import geometric_mean_score\n",
    "import numpy as np\n",
    "\n",
    "def calculate_metrics(pred, labels):\n",
    "    label_encoder = LabelEncoder()\n",
    "    all_classes = list(set(labels) | set(pred))  \n",
    "    label_encoder.fit(all_classes)\n",
    "\n",
    "    labels = label_encoder.transform(labels) \n",
    "    pred = label_encoder.transform(pred)  \n",
    "\n",
    "    acc = accuracy_score(labels, pred)\n",
    "    \n",
    "    num_classes = len(label_encoder.classes_)\n",
    "    true_labels_bin = label_binarize(labels, classes=list(range(num_classes)))\n",
    "    pred_bin = label_binarize(pred, classes=list(range(num_classes)))  \n",
    "\n",
    "    auc = roc_auc_score(true_labels_bin, pred_bin, multi_class='ovr', average='macro')\n",
    "    pr_auc = average_precision_score(true_labels_bin, pred_bin, average='macro')\n",
    "\n",
    "    balanced_acc = balanced_accuracy_score(labels, pred)\n",
    "    prec, rec, f1, _ = precision_recall_fscore_support(labels, pred, average='macro')\n",
    "\n",
    "    mcc = matthews_corrcoef(labels, pred)\n",
    "    g_mean = geometric_mean_score(labels, pred, average='macro')\n",
    "\n",
    "    print(f'Accuracy: {acc}')\n",
    "    print(f'AUC: {auc}')\n",
    "    print(f'Balanced Accuracy: {balanced_acc}')\n",
    "    print(f'Precision: {prec}')\n",
    "    print(f'Recall: {rec}')\n",
    "    print(f'F1: {f1}')\n",
    "    print(f'PR AUC: {pr_auc}')\n",
    "    print(f'MCC: {mcc}')\n",
    "    print(f'G-Mean: {g_mean}')\n",
    "\n",
    "    print('Accuracy AUC Balanced_Acc Precision Recall F1 PR_AUC MCC G-Mean')\n",
    "    print(f'{acc:.4f} {auc:.4f} {balanced_acc:.4f} {prec:.4f} {rec:.4f} {f1:.4f} {pr_auc:.4f} {mcc:.4f} {g_mean:.4f}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "calculate_metrics(pred, votes)"
   ]
  }
 ],
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