TestGPT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import openai\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "openai.api_key = os.getenv(\"OPENAI_KEY\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "messages=[\n",
    "        {\"role\": \"user\", \"content\": \"\"\"You are a player in a two dimensional block maze. \n",
    "The maze has open spaces, wall spaces, spaces with the user, and a goal. \n",
    "You can only see the blocks above you, below you, to your left, and to your right. \n",
    "Your available responses are U, D, L, R to decide which direction to move in, do not send any other text. \n",
    "For example: \n",
    "user: Available moves: U, D, L \n",
    "assistant: D \n",
    "Please send confirm to acknowledge these rules.\"\"\".replace('\\n', '')},\n",
    "        {\"role\": \"assistant\", \"content\": \"confirm\"}\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"You see the goal is one block beneath you. Available Moves: U D\"\n",
    "new_message = {\"role\":\"user\", \"content\":text}\n",
    "messages.append(new_message)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "response = openai.ChatCompletion.create(\n",
    "  model=\"gpt-3.5-turbo\",\n",
    "  messages=messages\n",
    ")[\"choices\"][0][\"message\"][\"content\"]\n",
    "messages.append({\"role\":\"assistant\", \"content\":response})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'You see the goal is one block beneath you. Available Moves: U D'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages[-1]['content']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "moves = []\n",
    "moves.append([3, 2])\n",
    "moves.append([4, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[3, 2], [4, 5]]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3, 2], [4, 5]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(moves)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import ast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "games = pd.read_csv('games.csv')\n",
    "games.loc[len(games)+1] = [moves, thoughts, mental_models, maze, goal]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "games = pd.DataFrame(columns=['moves', 'thoughts', 'mental_models', 'maze', 'goal'])\n",
    "moves, thoughts, mental_models, maze, goal = [], [], [], [], []\n",
    "new_game = pd.DataFrame({'moves':[moves], 'thoughts':[thoughts], 'mental_models':[mental_models], 'maze':[maze], 'goal':[goal]})\n",
    "games = pd.concat([games, new_game], ignore_index=True)\n",
    "games.to_csv('games.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "moves, thoughts, mental_models, maze, goal = [], [], [], [], []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "games = pd.read_csv('TripleThinker/games.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot set a row with mismatched columns",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\Owner\\Documents\\Repos\\MazeGPT\\TestGPT.ipynb Cell 16\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Owner/Documents/Repos/MazeGPT/TestGPT.ipynb#X35sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m games\u001b[39m.\u001b[39mloc[\u001b[39mlen\u001b[39m(games)\u001b[39m+\u001b[39m\u001b[39m1\u001b[39m] \u001b[39m=\u001b[39m [moves, thoughts, mental_models, maze, goal]\n",
      "File \u001b[1;32mc:\\Users\\Owner\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:716\u001b[0m, in \u001b[0;36m_LocationIndexer.__setitem__\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m    713\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_has_valid_setitem_indexer(key)\n\u001b[0;32m    715\u001b[0m iloc \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mname \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39miloc\u001b[39m\u001b[39m\"\u001b[39m \u001b[39melse\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mobj\u001b[39m.\u001b[39miloc\n\u001b[1;32m--> 716\u001b[0m iloc\u001b[39m.\u001b[39;49m_setitem_with_indexer(indexer, value, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mname)\n",
      "File \u001b[1;32mc:\\Users\\Owner\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1682\u001b[0m, in \u001b[0;36m_iLocIndexer._setitem_with_indexer\u001b[1;34m(self, indexer, value, name)\u001b[0m\n\u001b[0;32m   1679\u001b[0m     indexer, missing \u001b[39m=\u001b[39m convert_missing_indexer(indexer)\n\u001b[0;32m   1681\u001b[0m     \u001b[39mif\u001b[39;00m missing:\n\u001b[1;32m-> 1682\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_setitem_with_indexer_missing(indexer, value)\n\u001b[0;32m   1683\u001b[0m         \u001b[39mreturn\u001b[39;00m\n\u001b[0;32m   1685\u001b[0m \u001b[39m# align and set the values\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\Owner\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1998\u001b[0m, in \u001b[0;36m_iLocIndexer._setitem_with_indexer_missing\u001b[1;34m(self, indexer, value)\u001b[0m\n\u001b[0;32m   1995\u001b[0m     \u001b[39mif\u001b[39;00m is_list_like_indexer(value):\n\u001b[0;32m   1996\u001b[0m         \u001b[39m# must have conforming columns\u001b[39;00m\n\u001b[0;32m   1997\u001b[0m         \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(value) \u001b[39m!=\u001b[39m \u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mobj\u001b[39m.\u001b[39mcolumns):\n\u001b[1;32m-> 1998\u001b[0m             \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mcannot set a row with mismatched columns\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m   2000\u001b[0m     value \u001b[39m=\u001b[39m Series(value, index\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mobj\u001b[39m.\u001b[39mcolumns, name\u001b[39m=\u001b[39mindexer)\n\u001b[0;32m   2002\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mobj):\n\u001b[0;32m   2003\u001b[0m     \u001b[39m# We will ignore the existing dtypes instead of using\u001b[39;00m\n\u001b[0;32m   2004\u001b[0m     \u001b[39m#  internals.concat logic\u001b[39;00m\n",
      "\u001b[1;31mValueError\u001b[0m: cannot set a row with mismatched columns"
     ]
    }
   ],
   "source": [
    "games.loc[len(games)+1] = [moves, thoughts, mental_models, maze, goal]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "games.to_csv('TripleThinker/games.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>moves</th>\n",
       "      <th>thoughts</th>\n",
       "      <th>mental_models</th>\n",
       "      <th>maze</th>\n",
       "      <th>goal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[[3, 3], [0, 1], [1, 1], [1, 2], [1, 3], [2, 3...</td>\n",
       "      <td>['Since the only available move is right, go r...</td>\n",
       "      <td>['test', 'test']</td>\n",
       "      <td>[[0, 0, 1, 1], [1, 0, 0, 0], [1, 0, 1, 0], [1,...</td>\n",
       "      <td>[3, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[[0, 0], [0, 1], [1, 1], [1, 2], [1, 3], [2, 3...</td>\n",
       "      <td>['Move right.', 'Move down.', 'Move right.', '...</td>\n",
       "      <td>['test', 'test']</td>\n",
       "      <td>[[0, 0, 1, 1], [1, 0, 0, 0], [1, 0, 1, 0], [1,...</td>\n",
       "      <td>[3, 3]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               moves  \\\n",
       "1  [[3, 3], [0, 1], [1, 1], [1, 2], [1, 3], [2, 3...   \n",
       "2  [[0, 0], [0, 1], [1, 1], [1, 2], [1, 3], [2, 3...   \n",
       "\n",
       "                                            thoughts     mental_models  \\\n",
       "1  ['Since the only available move is right, go r...  ['test', 'test']   \n",
       "2  ['Move right.', 'Move down.', 'Move right.', '...  ['test', 'test']   \n",
       "\n",
       "                                                maze    goal  \n",
       "1  [[0, 0, 1, 1], [1, 0, 0, 0], [1, 0, 1, 0], [1,...  [3, 3]  \n",
       "2  [[0, 0, 1, 1], [1, 0, 0, 0], [1, 0, 1, 0], [1,...  [3, 3]  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "games = games.iloc[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "games = games.drop(columns=['0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[]'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games.iloc[-1]['maze']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "malformed node or string: 0",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\Owner\\Documents\\Repos\\MazeGPT\\TestGPT.ipynb Cell 16\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Owner/Documents/Repos/MazeGPT/TestGPT.ipynb#X22sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m games \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mread_csv(\u001b[39m'\u001b[39m\u001b[39mTripleThinker/games.csv\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Owner/Documents/Repos/MazeGPT/TestGPT.ipynb#X22sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m games \u001b[39m=\u001b[39m games\u001b[39m.\u001b[39;49mapplymap(\u001b[39mlambda\u001b[39;49;00m x: ast\u001b[39m.\u001b[39;49mliteral_eval(x))\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Owner/Documents/Repos/MazeGPT/TestGPT.ipynb#X22sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m game_num \u001b[39m=\u001b[39m \u001b[39m-\u001b[39m\u001b[39m1\u001b[39m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Owner/Documents/Repos/MazeGPT/TestGPT.ipynb#X22sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m maze \u001b[39m=\u001b[39m games\u001b[39m.\u001b[39miloc[game_num][\u001b[39m'\u001b[39m\u001b[39mmaze\u001b[39m\u001b[39m'\u001b[39m]\n",
      "File \u001b[1;32mc:\\Users\\Owner\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:8924\u001b[0m, in \u001b[0;36mDataFrame.applymap\u001b[1;34m(self, func, na_action, **kwargs)\u001b[0m\n\u001b[0;32m   8921\u001b[0m         \u001b[39mreturn\u001b[39;00m lib\u001b[39m.\u001b[39mmap_infer(x, func, ignore_na\u001b[39m=\u001b[39mignore_na)\n\u001b[0;32m   8922\u001b[0m     \u001b[39mreturn\u001b[39;00m lib\u001b[39m.\u001b[39mmap_infer(x\u001b[39m.\u001b[39mastype(\u001b[39mobject\u001b[39m)\u001b[39m.\u001b[39m_values, func, ignore_na\u001b[39m=\u001b[39mignore_na)\n\u001b[1;32m-> 8924\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mapply(infer)\u001b[39m.\u001b[39m__finalize__(\u001b[39mself\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mapplymap\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\Owner\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:8839\u001b[0m, in \u001b[0;36mDataFrame.apply\u001b[1;34m(self, func, axis, raw, result_type, args, **kwargs)\u001b[0m\n\u001b[0;32m   8828\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mpandas\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mcore\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mapply\u001b[39;00m \u001b[39mimport\u001b[39;00m frame_apply\n\u001b[0;32m   8830\u001b[0m op \u001b[39m=\u001b[39m frame_apply(\n\u001b[0;32m   8831\u001b[0m     \u001b[39mself\u001b[39m,\n\u001b[0;32m   8832\u001b[0m     func\u001b[39m=\u001b[39mfunc,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   8837\u001b[0m     kwargs\u001b[39m=\u001b[39mkwargs,\n\u001b[0;32m   8838\u001b[0m )\n\u001b[1;32m-> 8839\u001b[0m \u001b[39mreturn\u001b[39;00m op\u001b[39m.\u001b[39;49mapply()\u001b[39m.\u001b[39m__finalize__(\u001b[39mself\u001b[39m, method\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mapply\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\Owner\\anaconda3\\lib\\site-packages\\pandas\\core\\apply.py:727\u001b[0m, in \u001b[0;36mFrameApply.apply\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    724\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mraw:\n\u001b[0;32m    725\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mapply_raw()\n\u001b[1;32m--> 727\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mapply_standard()\n",
      "File \u001b[1;32mc:\\Users\\Owner\\anaconda3\\lib\\site-packages\\pandas\\core\\apply.py:851\u001b[0m, in \u001b[0;36mFrameApply.apply_standard\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    850\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mapply_standard\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m--> 851\u001b[0m     results, res_index \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mapply_series_generator()\n\u001b[0;32m    853\u001b[0m     \u001b[39m# wrap results\u001b[39;00m\n\u001b[0;32m    854\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mwrap_results(results, res_index)\n",
      "File \u001b[1;32mc:\\Users\\Owner\\anaconda3\\lib\\site-packages\\pandas\\core\\apply.py:867\u001b[0m, in \u001b[0;36mFrameApply.apply_series_generator\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    864\u001b[0m \u001b[39mwith\u001b[39;00m option_context(\u001b[39m\"\u001b[39m\u001b[39mmode.chained_assignment\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m):\n\u001b[0;32m    865\u001b[0m     \u001b[39mfor\u001b[39;00m i, v \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(series_gen):\n\u001b[0;32m    866\u001b[0m         \u001b[39m# ignore SettingWithCopy here in case the user mutates\u001b[39;00m\n\u001b[1;32m--> 867\u001b[0m         results[i] \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mf(v)\n\u001b[0;32m    868\u001b[0m         \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(results[i], ABCSeries):\n\u001b[0;32m    869\u001b[0m             \u001b[39m# If we have a view on v, we need to make a copy because\u001b[39;00m\n\u001b[0;32m    870\u001b[0m             \u001b[39m#  series_generator will swap out the underlying data\u001b[39;00m\n\u001b[0;32m    871\u001b[0m             results[i] \u001b[39m=\u001b[39m results[i]\u001b[39m.\u001b[39mcopy(deep\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n",
      "File \u001b[1;32mc:\\Users\\Owner\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:8922\u001b[0m, in \u001b[0;36mDataFrame.applymap.<locals>.infer\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m   8920\u001b[0m \u001b[39mif\u001b[39;00m x\u001b[39m.\u001b[39mempty:\n\u001b[0;32m   8921\u001b[0m     \u001b[39mreturn\u001b[39;00m lib\u001b[39m.\u001b[39mmap_infer(x, func, ignore_na\u001b[39m=\u001b[39mignore_na)\n\u001b[1;32m-> 8922\u001b[0m \u001b[39mreturn\u001b[39;00m lib\u001b[39m.\u001b[39;49mmap_infer(x\u001b[39m.\u001b[39;49mastype(\u001b[39mobject\u001b[39;49m)\u001b[39m.\u001b[39;49m_values, func, ignore_na\u001b[39m=\u001b[39;49mignore_na)\n",
      "File \u001b[1;32mc:\\Users\\Owner\\anaconda3\\lib\\site-packages\\pandas\\_libs\\lib.pyx:2870\u001b[0m, in \u001b[0;36mpandas._libs.lib.map_infer\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Owner\\Documents\\Repos\\MazeGPT\\TestGPT.ipynb Cell 16\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Owner/Documents/Repos/MazeGPT/TestGPT.ipynb#X22sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m games \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mread_csv(\u001b[39m'\u001b[39m\u001b[39mTripleThinker/games.csv\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Owner/Documents/Repos/MazeGPT/TestGPT.ipynb#X22sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m games \u001b[39m=\u001b[39m games\u001b[39m.\u001b[39mapplymap(\u001b[39mlambda\u001b[39;00m x: ast\u001b[39m.\u001b[39;49mliteral_eval(x))\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Owner/Documents/Repos/MazeGPT/TestGPT.ipynb#X22sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m game_num \u001b[39m=\u001b[39m \u001b[39m-\u001b[39m\u001b[39m1\u001b[39m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Owner/Documents/Repos/MazeGPT/TestGPT.ipynb#X22sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m maze \u001b[39m=\u001b[39m games\u001b[39m.\u001b[39miloc[game_num][\u001b[39m'\u001b[39m\u001b[39mmaze\u001b[39m\u001b[39m'\u001b[39m]\n",
      "File \u001b[1;32mc:\\Users\\Owner\\anaconda3\\lib\\ast.py:105\u001b[0m, in \u001b[0;36mliteral_eval\u001b[1;34m(node_or_string)\u001b[0m\n\u001b[0;32m    103\u001b[0m                 \u001b[39mreturn\u001b[39;00m left \u001b[39m-\u001b[39m right\n\u001b[0;32m    104\u001b[0m     \u001b[39mreturn\u001b[39;00m _convert_signed_num(node)\n\u001b[1;32m--> 105\u001b[0m \u001b[39mreturn\u001b[39;00m _convert(node_or_string)\n",
      "File \u001b[1;32mc:\\Users\\Owner\\anaconda3\\lib\\ast.py:104\u001b[0m, in \u001b[0;36mliteral_eval.<locals>._convert\u001b[1;34m(node)\u001b[0m\n\u001b[0;32m    102\u001b[0m         \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    103\u001b[0m             \u001b[39mreturn\u001b[39;00m left \u001b[39m-\u001b[39m right\n\u001b[1;32m--> 104\u001b[0m \u001b[39mreturn\u001b[39;00m _convert_signed_num(node)\n",
      "File \u001b[1;32mc:\\Users\\Owner\\anaconda3\\lib\\ast.py:78\u001b[0m, in \u001b[0;36mliteral_eval.<locals>._convert_signed_num\u001b[1;34m(node)\u001b[0m\n\u001b[0;32m     76\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m     77\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39m-\u001b[39m operand\n\u001b[1;32m---> 78\u001b[0m \u001b[39mreturn\u001b[39;00m _convert_num(node)\n",
      "File \u001b[1;32mc:\\Users\\Owner\\anaconda3\\lib\\ast.py:69\u001b[0m, in \u001b[0;36mliteral_eval.<locals>._convert_num\u001b[1;34m(node)\u001b[0m\n\u001b[0;32m     67\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_convert_num\u001b[39m(node):\n\u001b[0;32m     68\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(node, Constant) \u001b[39mor\u001b[39;00m \u001b[39mtype\u001b[39m(node\u001b[39m.\u001b[39mvalue) \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m (\u001b[39mint\u001b[39m, \u001b[39mfloat\u001b[39m, \u001b[39mcomplex\u001b[39m):\n\u001b[1;32m---> 69\u001b[0m         _raise_malformed_node(node)\n\u001b[0;32m     70\u001b[0m     \u001b[39mreturn\u001b[39;00m node\u001b[39m.\u001b[39mvalue\n",
      "File \u001b[1;32mc:\\Users\\Owner\\anaconda3\\lib\\ast.py:66\u001b[0m, in \u001b[0;36mliteral_eval.<locals>._raise_malformed_node\u001b[1;34m(node)\u001b[0m\n\u001b[0;32m     65\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_raise_malformed_node\u001b[39m(node):\n\u001b[1;32m---> 66\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mmalformed node or string: \u001b[39m\u001b[39m{\u001b[39;00mnode\u001b[39m!r}\u001b[39;00m\u001b[39m'\u001b[39m)\n",
      "\u001b[1;31mValueError\u001b[0m: malformed node or string: 0"
     ]
    }
   ],
   "source": [
    "games = pd.read_csv('TripleThinker/games.csv')\n",
    "games = games.applymap(lambda x: ast.literal_eval(x))\n",
    "game_num = -1\n",
    "\n",
    "maze = games.iloc[game_num]['maze']\n",
    "moves = games.iloc[game_num]['moves']\n",
    "user_location = moves[0]\n",
    "goal_location = games.iloc[game_num]['moves']\n",
    "thoughts = games.iloc[game_num]['thoughts']\n",
    "index = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[3, 3], [0, 1], [1, 1], [1, 2], [1, 3], [2, 3], [3, 3]]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "goal_location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "games = games.drop(columns=['Unnamed: 0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>moves</th>\n",
       "      <th>thoughts</th>\n",
       "      <th>mental_models</th>\n",
       "      <th>maze</th>\n",
       "      <th>goal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>[[3, 3], [0, 1], [1, 1], [1, 2], [1, 3], [2, 3...</td>\n",
       "      <td>['Since the only available move is right, go r...</td>\n",
       "      <td>['test', 'test']</td>\n",
       "      <td>[[0, 0, 1, 1], [1, 0, 0, 0], [1, 0, 1, 0], [1,...</td>\n",
       "      <td>[3, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>[[0, 0], [0, 1], [1, 1], [1, 2], [1, 3], [2, 3...</td>\n",
       "      <td>['Move right.', 'Move down.', 'Move right.', '...</td>\n",
       "      <td>['test', 'test']</td>\n",
       "      <td>[[0, 0, 1, 1], [1, 0, 0, 0], [1, 0, 1, 0], [1,...</td>\n",
       "      <td>[3, 3]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                              moves  \\\n",
       "0           0                                                 []   \n",
       "1           1  [[3, 3], [0, 1], [1, 1], [1, 2], [1, 3], [2, 3...   \n",
       "2           2  [[0, 0], [0, 1], [1, 1], [1, 2], [1, 3], [2, 3...   \n",
       "\n",
       "                                            thoughts     mental_models  \\\n",
       "0                                                 []                []   \n",
       "1  ['Since the only available move is right, go r...  ['test', 'test']   \n",
       "2  ['Move right.', 'Move down.', 'Move right.', '...  ['test', 'test']   \n",
       "\n",
       "                                                maze    goal  \n",
       "0                                                 []      []  \n",
       "1  [[0, 0, 1, 1], [1, 0, 0, 0], [1, 0, 1, 0], [1,...  [3, 3]  \n",
       "2  [[0, 0, 1, 1], [1, 0, 0, 0], [1, 0, 1, 0], [1,...  [3, 3]  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games = pd.read_csv('TripleThinker/games.csv')\n",
    "games"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "games.to_csv('TripleThinker/games.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[[3, 3], [0, 1], [1, 1], [1, 2], [1, 3], [2, 3], [3, 3]]'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games.iloc[game_num]['moves']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[3, 3], [0, 1], [1, 1], [1, 2], [1, 3], [2, 3], [3, 3]]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>moves</th>\n",
       "      <th>thoughts</th>\n",
       "      <th>mental_models</th>\n",
       "      <th>maze</th>\n",
       "      <th>goal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[[3, 3], [0, 1], [1, 1], [1, 2], [1, 3], [2, 3...</td>\n",
       "      <td>['Since the only available move is right, go r...</td>\n",
       "      <td>['test', 'test']</td>\n",
       "      <td>[[0, 0, 1, 1], [1, 0, 0, 0], [1, 0, 1, 0], [1,...</td>\n",
       "      <td>[3, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[[0, 0], [0, 1], [1, 1], [1, 2], [1, 3], [2, 3...</td>\n",
       "      <td>['Move right.', 'Move down.', 'Move right.', '...</td>\n",
       "      <td>['test', 'test']</td>\n",
       "      <td>[[0, 0, 1, 1], [1, 0, 0, 0], [1, 0, 1, 0], [1,...</td>\n",
       "      <td>[3, 3]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               moves  \\\n",
       "0                                                 []   \n",
       "1  [[3, 3], [0, 1], [1, 1], [1, 2], [1, 3], [2, 3...   \n",
       "2  [[0, 0], [0, 1], [1, 1], [1, 2], [1, 3], [2, 3...   \n",
       "\n",
       "                                            thoughts     mental_models  \\\n",
       "0                                                 []                []   \n",
       "1  ['Since the only available move is right, go r...  ['test', 'test']   \n",
       "2  ['Move right.', 'Move down.', 'Move right.', '...  ['test', 'test']   \n",
       "\n",
       "                                                maze    goal  \n",
       "0                                                 []      []  \n",
       "1  [[0, 0, 1, 1], [1, 0, 0, 0], [1, 0, 1, 0], [1,...  [3, 3]  \n",
       "2  [[0, 0, 1, 1], [1, 0, 0, 0], [1, 0, 1, 0], [1,...  [3, 3]  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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