humanize.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle, json, subprocess\n",
    "from glob import glob\n",
    "from simulated_web_agent.agent.memory import Thought, Plan, Action, Observation, Reflection\n",
    "\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "processing = 7\n",
    "# find latest agent_{}.pkl, sort by number\n",
    "agent_files = sorted(glob(f\"output/{processing}/agent_*.pkl\"), key=lambda x: int(x.split(\"_\")[1].replace(\".pkl\", \"\")))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "agent = pickle.load(open(agent_files[-1], \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def humanize_memory(memory):\n",
    "    # transform memory into a human-readable string\n",
    "    if isinstance(memory, Observation):\n",
    "        return f\"Before action {memory.timestamp + 1}, I saw: {memory.content}\"\n",
    "    elif isinstance(memory, Thought):\n",
    "        return f\"Before action {memory.timestamp + 1}, I thought: {memory.content}\"\n",
    "    elif isinstance(memory, Reflection):\n",
    "        return f\"Before action {memory.timestamp + 1}, I reflected: {memory.content}\"\n",
    "    # elif isinstance(memory, Plan):\n",
    "    #     return f\"Plan: {memory.content}\"\n",
    "    elif isinstance(memory, Action):\n",
    "        return f\"For action {memory.timestamp + 1}, I will: {memory.content}\"\n",
    "    else:\n",
    "        return f\"Memory: {memory}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(\"\\n\".join([humanize_memory(m) for m in agent.memory.memories]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing 0:\n",
      "  Memory length: 185\n",
      "Processing 1:\n",
      "  Memory length: 291\n",
      "Processing 2:\n",
      "  Memory length: 452\n",
      "Processing 3:\n",
      "  Memory length: 557\n",
      "Processing 4:\n",
      "  Memory length: 378\n",
      "Processing 5:\n",
      "  Memory length: 339\n",
      "Processing 6:\n",
      "  Memory length: 613\n",
      "Processing 7:\n",
      "  Memory length: 145\n",
      "Processing 8:\n",
      "  Memory length: 566\n",
      "Processing 9:\n",
      "  Memory length: 289\n",
      "Processing 10:\n",
      "  Memory length: 346\n",
      "Processing 11:\n",
      "  Memory length: 138\n",
      "Processing 12:\n",
      "  Memory length: 594\n",
      "Processing 13:\n",
      "  Memory length: 1129\n",
      "Processing 14:\n",
      "  Memory length: 113\n",
      "Processing 15:\n",
      "  Memory length: 268\n",
      "Processing 16:\n",
      "  Memory length: 195\n",
      "Processing 17:\n",
      "  Memory length: 244\n",
      "Processing 18:\n",
      "  Memory length: 282\n",
      "Processing 19:\n",
      "  Memory length: 662\n",
      "Processing 20:\n",
      "  Memory length: 558\n",
      "Processing 21:\n",
      "  Memory length: 265\n",
      "Processing 22:\n",
      "  Memory length: 378\n",
      "Processing 23:\n",
      "  Memory length: 146\n",
      "Processing 24:\n",
      "  Memory length: 174\n",
      "Processing 25:\n",
      "  Memory length: 636\n",
      "Processing 26:\n",
      "  Memory length: 115\n",
      "Processing 27:\n",
      "  Memory length: 577\n",
      "Processing 28:\n",
      "  Memory length: 189\n",
      "Processing 29:\n",
      "  Memory length: 263\n",
      "Processing 30:\n",
      "  Memory length: 222\n",
      "Processing 31:\n",
      "  Memory length: 227\n",
      "Processing 32:\n",
      "  Memory length: 446\n",
      "Processing 33:\n",
      "  Memory length: 482\n",
      "Processing 34:\n",
      "  Memory length: 234\n",
      "Processing 35:\n",
      "  Memory length: 145\n",
      "Processing 36:\n",
      "  Memory length: 136\n",
      "Processing 37:\n",
      "  Memory length: 437\n",
      "Processing 38:\n",
      "  Memory length: 203\n",
      "Processing 39:\n",
      "  Memory length: 147\n",
      "Processing 40:\n",
      "  Memory length: 105\n",
      "Processing 41:\n",
      "  Memory length: 247\n",
      "Processing 42:\n",
      "  Memory length: 363\n",
      "Processing 43:\n",
      "  Memory length: 416\n",
      "Processing 44:\n",
      "  Memory length: 982\n",
      "Processing 45:\n",
      "  Memory length: 947\n",
      "Processing 46:\n",
      "  Memory length: 628\n",
      "Processing 47:\n",
      "  Memory length: 158\n",
      "Processing 48:\n",
      "  Memory length: 154\n",
      "Processing 49:\n",
      "  Memory length: 146\n",
      "Processing 50:\n",
      "  Memory length: 171\n",
      "Processing 51:\n",
      "  Memory length: 151\n",
      "Processing 52:\n",
      "  Memory length: 1828\n",
      "Processing 53:\n",
      "  Memory length: 545\n",
      "Processing 54:\n",
      "  Memory length: 486\n",
      "Processing 55:\n",
      "  Memory length: 211\n",
      "Processing 56:\n",
      "  Memory length: 222\n",
      "Processing 57:\n",
      "  Memory length: 896\n",
      "Processing 58:\n",
      "  Memory length: 297\n",
      "Processing 59:\n",
      "  Memory length: 262\n"
     ]
    }
   ],
   "source": [
    "for processing in range(0, 60):\n",
    "    agent_files = sorted(glob(f\"output/{processing}/agent_*.pkl\"), key=lambda x: int(x.split(\"_\")[1].replace(\".pkl\", \"\")))\n",
    "    agent = pickle.load(open(agent_files[-1], \"rb\"))\n",
    "    print(f\"Processing {processing}:\")\n",
    "    print(f\"  Memory length: {len(agent.memory.memories)}\")\n",
    "    with open(f\"output/{processing}/humanized_memory.txt\", \"w\") as f:\n",
    "        f.write(\"\\n\".join([humanize_memory(m) for m in agent.memory.memories]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# processing = 7\n",
    "for processing in range(0, 60):\n",
    "    # humanize action\n",
    "    # first read jsonl\n",
    "    with open(f\"output/{processing}/action_trace.txt\", \"r\") as f:\n",
    "        actions = [json.loads(line) for line in f.readlines()]\n",
    "    result = []\n",
    "    i = 0\n",
    "    for action in actions:\n",
    "        for a in action:\n",
    "            result.append(f\"Action {i+1}: {a['type']}, description: {a['description']}\")\n",
    "        i += 1\n",
    "    with open(f\"output/{processing}/humanized_action.txt\", \"w\") as f:\n",
    "        f.write(\"\\n\".join(result))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"Action 1: type_and_submit, description: Typing 'unisex oversized denim jacket' into the search input field and submitting the form.\",\n",
       " \"Action 2: click, description: Clicking on the product 'Women's Oversize Jean Jacket Vintage Washed Boyfriend Denim Jacket Long Sleeve Classic Loose Button Down Jean Coats' to check the details.\",\n",
       " \"Action 3: click, description: Clicking on the product 'BAGELISE Winter Coats for Women Fashion, Womens Denim Faux Fur Long Sleeve Coat Sherpa Collar Casual Oversized Jacket Outwear' to check the details.\",\n",
       " \"Action 4: click, description: Clicking on the product 'GUROA Women 90s Hip Hop Baseball Jacket Oversized Letter Icon Print Long Sleeve Outerwear Stand Collar Patchwork Cute Coat' to check the details.\",\n",
       " \"Action 5: click, description: Clicking on the product 'Womens Denim Jackets Ripped Distressed Casual Long Sleeve Button Down Stretch Cropped Jean Trucker Jacket with Pockets' to check the details.\",\n",
       " \"Action 6: click, description: Clicking on the product 'Women Winter Hooded Fuzzy Fleece Lined Jacket Coat Overcoat Long-Sleeved Lapel Thick Wool Trench Oversized Outwear' to check the details.\",\n",
       " \"Action 7: click, description: Clicking on the 'Details' tab to investigate detailed product information of the 'Women's Oversize Jean Jacket Vintage Washed Boyfriend Denim Jacket Long Sleeve Classic Loose Button Down Jean Coats.'\",\n",
       " 'Action 8: analyze, description: Investigate the product details, analyzing features, materials used, sizing options, and color availability, particularly in turquoise. Also, check if it aligns with ethical shopping criteria.',\n",
       " 'Action 9: analyze, description: Review the available sizing options listed, noting their representation as 3X-Large to 5X-Large, and determine if they provide the necessary oversized fit.',\n",
       " \"Action 10: click, description: Clicking on the 'Details' tab to review the detailed product information.\",\n",
       " \"Action 11: click, description: Clicking on the size '5X-Large' to select it as it aligns with Avery's need for an oversized fit.\",\n",
       " \"Action 12: click, description: Clicking on 'Add to Cart' to add the jacket to the shopping cart.\"]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "for processing in range(0, 60):\n",
    "# processing = 0\n",
    "# if True:\n",
    "    # os.path.mak\n",
    "    # mkdir upload/{processing}\n",
    "    # copy replay.mp4 to upload/{processing}\n",
    "    # copy humanized_action.txt to upload/{processing}\n",
    "    # copy humanized_memory.txt to upload/{processing}\n",
    "    os.makedirs(f\"upload/{processing+1}\", exist_ok=True)\n",
    "    # shutil.copy(f\"output/{processing}/replay.mp4\", f\"upload/{processing+1}/replay.mp4\")\n",
    "    shutil.copy(f\"output/{processing}/humanized_action.txt\", f\"upload/{processing+1}/actions.txt\")\n",
    "    shutil.copy(f\"output/{processing}/humanized_memory.txt\", f\"upload/{processing+1}/memories.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "\n",
    "for processing in range(0, 60):\n",
    "    # processing = 0\n",
    "    # if True:\n",
    "    # os.path.mak\n",
    "    # mkdir upload/{processing}\n",
    "    # copy replay.mp4 to upload/{processing}\n",
    "    # copy humanized_action.txt to upload/{processing}\n",
    "    # copy humanized_memory.txt to upload/{processing}\n",
    "    os.makedirs(f\"new_upload/{processing+1}\", exist_ok=True)\n",
    "    # shutil.copy(f\"output/{processing}/replay.mp4\", f\"upload/{processing+1}/replay.mp4\")\n",
    "    shutil.copy(\n",
    "        f\"output/{processing}/humanized_action.txt\",\n",
    "        f\"new_upload/{processing+1}/actions.txt\",\n",
    "    )\n",
    "    # shutil.copy(\n",
    "    #     f\"output/{processing}/humanized_memory.txt\",\n",
    "    #     f\"upload/{processing+1}/memories.txt\",\n",
    "    # )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
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  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " '53': '15UGW9vl1QnJL8SdbAkaRuAU4ImVA7Eqc',\n",
       " '54': '1bjCccTYzKtEv9kvIZ8c1oST2zREEnsCC',\n",
       " '1': '1jWhxiT7-FkI61xvLi4Xr-fHg6xrcDy_8',\n",
       " '10': '1nkHVDaoAKAhN25_zDlhwM0532nCXY0VQ',\n",
       " '17': '1Q8HetOjtVsp5wIqM94iYlh9WvbZ3a6Oe',\n",
       " '19': '1BQq1spylceJyHv_J0BEEqmPvpyVIeeVh',\n",
       " '21': '1G4lPc82VgdD6477Zb02Blka9vNGoBvBd',\n",
       " '26': '1kzZgFbVnHgGgOSwY7-4ykvvAO9FJxTMf',\n",
       " '28': '1jGUjjoRhZhVwAWCfusLOQY0I1snVL-tq',\n",
       " '45': '1e3bGP8VWvnyu1iMEjGXfYEpJ-dzwlxZX',\n",
       " '6': '1UUm0fB6DwaW82Nv3OnN6OHtd8Xfe7AAG',\n",
       " '8': '1vuXLP2JuxoJIBM0DMxKvT-mZXbFCc-7L',\n",
       " '11': '1JrY_Sjcy70LIG5pEh7iHGg0v35uUHLq-',\n",
       " '16': '1aKGYWXdtHQMY-2inArxNF7ahEjbhIZQ9',\n",
       " '18': '1_IsIcxLE62nRZGoNEtVu-BzxwRcW5NEq',\n",
       " '20': '15Wkj3ZAwpdaTH0Lk8YHrs_IQ3OhMadtc',\n",
       " '27': '1oDyzf1NRpuDItRYaZ9lwiAnrBz5zIuJL',\n",
       " '29': '1YIhrwmLmG7x3LSurADvHYSDk1vk55kie',\n",
       " '33': '1wEtAREAJw5qsc7Q3yPNNaEfXphlJFiVQ',\n",
       " '42': '1vw5YillwMTlDt9XCWPCXPAY7WNGNvILT',\n",
       " '7': '1PccllVYfNjKYO_TpyIAmU1fOBGPMiF4G',\n",
       " '9': '1oew0Q6rCskHenLNLbkGaRGJlqdIVddHZ',\n",
       " '32': '1szCYjzURBJ6EnzO0Hoz6zh99uRzRa1YI',\n",
       " '34': '1pz0XCn788twYC_jj0qUUqiq2l4KStLio',\n",
       " '35': '1hNlh453NJuyzCgbdawcdcRTH7now_rzE',\n",
       " '50': '1ihvBDQ8yKbVg62CIBRDU8LpiZBT13AGH',\n",
       " '51': '1-q7pUzLGTfimEbB9euy7K8gvXRz8lTlm',\n",
       " '56': '1zrosE31x3LC8BNfFHRbDxi2MGoliU3Ra',\n",
       " '57': '1BuS_ATLrEANbPnX-oy0uZag02LI70bgr',\n",
       " '58': '1hJ6nRt5Y_wZCjWwNM0P1Fst99lZJkpdm',\n",
       " '59': '19ET2ZFd4rugcG5xxLMxzXiI0bDKzEayy',\n",
       " '60': '1optblJ206lB597p7KLEgwLAUuutspFdL'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directory_to_id = {}\n",
    "for line in ids.split(\"\\n\"):\n",
    "    a,b, _, _, _ = line.split()\n",
    "    directory_to_id[b] = a\n",
    "directory_to_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3655513827c14bea80db4dbda6997e1c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/60 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for processing in tqdm(range(1, 61)):\n",
    "    # first list gdrive files list --parent 1vuXLP2JuxoJIBM0DMxKvT-mZXbFCc-7L\n",
    "    files = subprocess.check_output(f\"gdrive files list --parent {directory_to_id[str(processing)]}\", shell=True).decode(\"utf-8\")\n",
    "    # now get the file id of the file with name actions.txt\n",
    "    # print(files)\n",
    "    file_id = [line.split()[0] for line in files.split(\"\\n\") if line and line.split()[1] == \"actions.txt\"][0]\n",
    "    # print(file_id)\n",
    "    # break\n",
    "    # gdrive files update id new_upload/{processing+1}/actions.txt\n",
    "    subprocess.check_output(\n",
    "        f\"gdrive files update {file_id} new_upload/{processing}/actions.txt\",\n",
    "        shell=True,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting ipywidgets\n",
      "  Downloading ipywidgets-8.1.5-py3-none-any.whl.metadata (2.3 kB)\n",
      "Requirement already satisfied: comm>=0.1.3 in /Users/leo/micromamba/envs/simulated_web_agent/lib/python3.9/site-packages (from ipywidgets) (0.2.2)\n",
      "Requirement already satisfied: ipython>=6.1.0 in /Users/leo/micromamba/envs/simulated_web_agent/lib/python3.9/site-packages (from ipywidgets) (8.18.1)\n",
      "Requirement already satisfied: traitlets>=4.3.1 in /Users/leo/micromamba/envs/simulated_web_agent/lib/python3.9/site-packages (from ipywidgets) (5.14.3)\n",
      "Collecting widgetsnbextension~=4.0.12 (from ipywidgets)\n",
      "  Downloading widgetsnbextension-4.0.13-py3-none-any.whl.metadata (1.6 kB)\n",
      "Collecting jupyterlab-widgets~=3.0.12 (from ipywidgets)\n",
      "  Downloading jupyterlab_widgets-3.0.13-py3-none-any.whl.metadata (4.1 kB)\n",
      "Requirement already satisfied: decorator in /Users/leo/micromamba/envs/simulated_web_agent/lib/python3.9/site-packages (from ipython>=6.1.0->ipywidgets) (5.1.1)\n",
      "Requirement already satisfied: jedi>=0.16 in /Users/leo/micromamba/envs/simulated_web_agent/lib/python3.9/site-packages (from ipython>=6.1.0->ipywidgets) (0.19.1)\n",
      "Requirement already satisfied: matplotlib-inline in /Users/leo/micromamba/envs/simulated_web_agent/lib/python3.9/site-packages (from ipython>=6.1.0->ipywidgets) (0.1.7)\n",
      "Requirement already satisfied: prompt-toolkit<3.1.0,>=3.0.41 in /Users/leo/micromamba/envs/simulated_web_agent/lib/python3.9/site-packages (from ipython>=6.1.0->ipywidgets) (3.0.47)\n",
      "Requirement already satisfied: pygments>=2.4.0 in /Users/leo/micromamba/envs/simulated_web_agent/lib/python3.9/site-packages (from ipython>=6.1.0->ipywidgets) (2.18.0)\n",
      "Requirement already satisfied: stack-data in /Users/leo/micromamba/envs/simulated_web_agent/lib/python3.9/site-packages (from ipython>=6.1.0->ipywidgets) (0.6.2)\n",
      "Requirement already satisfied: typing-extensions in /Users/leo/micromamba/envs/simulated_web_agent/lib/python3.9/site-packages (from ipython>=6.1.0->ipywidgets) (4.12.2)\n",
      "Requirement already satisfied: exceptiongroup in /Users/leo/micromamba/envs/simulated_web_agent/lib/python3.9/site-packages (from ipython>=6.1.0->ipywidgets) (1.2.2)\n",
      "Requirement already satisfied: pexpect>4.3 in /Users/leo/micromamba/envs/simulated_web_agent/lib/python3.9/site-packages (from ipython>=6.1.0->ipywidgets) (4.9.0)\n",
      "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /Users/leo/micromamba/envs/simulated_web_agent/lib/python3.9/site-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets) (0.8.4)\n",
      "Requirement already satisfied: ptyprocess>=0.5 in /Users/leo/micromamba/envs/simulated_web_agent/lib/python3.9/site-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets) (0.7.0)\n",
      "Requirement already satisfied: wcwidth in /Users/leo/micromamba/envs/simulated_web_agent/lib/python3.9/site-packages (from prompt-toolkit<3.1.0,>=3.0.41->ipython>=6.1.0->ipywidgets) (0.2.13)\n",
      "Requirement already satisfied: executing>=1.2.0 in /Users/leo/micromamba/envs/simulated_web_agent/lib/python3.9/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.0.1)\n",
      "Requirement already satisfied: asttokens>=2.1.0 in /Users/leo/micromamba/envs/simulated_web_agent/lib/python3.9/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.4.1)\n",
      "Requirement already satisfied: pure-eval in /Users/leo/micromamba/envs/simulated_web_agent/lib/python3.9/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (0.2.3)\n",
      "Requirement already satisfied: six>=1.12.0 in /Users/leo/micromamba/envs/simulated_web_agent/lib/python3.9/site-packages (from asttokens>=2.1.0->stack-data->ipython>=6.1.0->ipywidgets) (1.16.0)\n",
      "Downloading ipywidgets-8.1.5-py3-none-any.whl (139 kB)\n",
      "Downloading jupyterlab_widgets-3.0.13-py3-none-any.whl (214 kB)\n",
      "Downloading widgetsnbextension-4.0.13-py3-none-any.whl (2.3 MB)\n",
      "\u001b[2K   \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: widgetsnbextension, jupyterlab-widgets, ipywidgets\n",
      "Successfully installed ipywidgets-8.1.5 jupyterlab-widgets-3.0.13 widgetsnbextension-4.0.13\n"
     ]
    }
   ],
   "source": [
    "!pip install ipywidgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "simulated_web_agent",
   "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": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}