diff --git "a/mind2web clean notebook.ipynb" "b/mind2web clean notebook.ipynb"
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+++ "b/mind2web clean notebook.ipynb"
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Benchmarking LaVague on Mind2Web"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will be benchmarking LaVague on Mind2Web, a dataset for agents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/daniel/miniconda3/envs/lavague/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "from lavague.utils import load_action_engine\n",
+    "import inspect\n",
+    "\n",
+    "config_path = \"./examples/api/openai.py\"\n",
+    "\n",
+    "action_engine, get_driver = load_action_engine(config_path, streaming=False)\n",
+    "\n",
+    "driver = get_driver()\n",
+    "    \n",
+    "# Gets the original source code of the get_driver method\n",
+    "source_code = inspect.getsource(get_driver)\n",
+    "\n",
+    "# Split the source code into lines and remove the first line (method definition)\n",
+    "source_code_lines = source_code.splitlines()[1:]\n",
+    "source_code_lines = [line.strip() for line in source_code_lines[:-1]]\n",
+    "import_lines = [line for line in source_code_lines if line.startswith(\"from\") or line.startswith(\"import\")] \n",
+    "exec(\"\\n\".join(import_lines))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will use a GPT-3.5 from Azure OpenAI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from llama_index.llms.azure_openai import AzureOpenAI\n",
+    "from lavague.prompts import DEFAULT_PROMPT\n",
+    "\n",
+    "api_key=os.getenv(\"AZURE_OPENAI_KEY\")\n",
+    "api_version=\"2024-02-15-preview\"\n",
+    "azure_endpoint = os.getenv(\"AZURE_OPENAI_ENDPOINT\")\n",
+    "model = \"gpt-35-turbo\"\n",
+    "deployment_name = \"gpt-35-turbo\"\n",
+    "\n",
+    "class LLM(AzureOpenAI):\n",
+    "    def __init__(self):\n",
+    "        super().__init__(\n",
+    "            model=deployment_name,\n",
+    "            deployment_name=deployment_name,\n",
+    "            api_key=api_key,\n",
+    "            azure_endpoint=azure_endpoint,\n",
+    "            api_version=api_version,\n",
+    "            temperature=0.0\n",
+    "        )\n",
+    "llm = LLM()\n",
+    "\n",
+    "action_engine.llm = llm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "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>Unnamed: 0</th>\n",
+       "      <th>split</th>\n",
+       "      <th>annotation_uid</th>\n",
+       "      <th>confirmed_task</th>\n",
+       "      <th>raw_html</th>\n",
+       "      <th>cleaned_html</th>\n",
+       "      <th>action_uid</th>\n",
+       "      <th>operation</th>\n",
+       "      <th>code</th>\n",
+       "      <th>cur_actions_desc</th>\n",
+       "      <th>cur_actions_reprs</th>\n",
+       "      <th>pos_candidates</th>\n",
+       "      <th>prev_actions_desc</th>\n",
+       "      <th>prev_actions_reprs</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6033</td>\n",
+       "      <td>test_task</td>\n",
+       "      <td>640e0425-bceb-45ff-ba4d-dbc5b62e31d5</td>\n",
+       "      <td>Find the \"Rock And Roll Over\" reviews</td>\n",
+       "      <td>&lt;!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.0 Tr...</td>\n",
+       "      <td>&lt;html backend_node_id=\"338\"&gt;\\n  &lt;body&gt;\\n    &lt;d...</td>\n",
+       "      <td>96238fb3-bc46-4dff-95c0-4b2d4ecc70ea</td>\n",
+       "      <td>{'op': 'TYPE', 'original_op': 'TYPE', 'value':...</td>\n",
+       "      <td>```python\\nelement = driver.find_element(By.XP...</td>\n",
+       "      <td>Enter \"Rock And Roll Over\" in the text box to ...</td>\n",
+       "      <td>[textbox]  Enter artist name or song title -&gt; ...</td>\n",
+       "      <td>[{'attributes': '{\"backend_node_id\": \"248\", \"b...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3394</td>\n",
+       "      <td>test_domain</td>\n",
+       "      <td>34e0bf85-6441-40cb-b7f6-d107e5bcb049</td>\n",
+       "      <td>Look up the visitors trend for Apple stock</td>\n",
+       "      <td>&lt;!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.0 Tr...</td>\n",
+       "      <td>&lt;html backend_node_id=\"14307\"&gt;\\n  &lt;div backend...</td>\n",
+       "      <td>8043e8e8-bb7a-4521-80bf-246bc16e883c</td>\n",
+       "      <td>{'op': 'CLICK', 'original_op': 'CLICK', 'value...</td>\n",
+       "      <td>```python\\nelement = driver.find_element(By.XP...</td>\n",
+       "      <td>Click on \"AAPL\" to look up the visitors trend ...</td>\n",
+       "      <td>[div]  AAPL -&gt; CLICK</td>\n",
+       "      <td>[{'attributes': '{\"backend_node_id\": \"14603\", ...</td>\n",
+       "      <td>['Enter \"apple\" in the search box to look up t...</td>\n",
+       "      <td>['[textbox]  Search for news, symbols or compa...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>320</td>\n",
+       "      <td>test_domain</td>\n",
+       "      <td>77269ea5-70a4-4cfa-a2f9-9937a1c55096</td>\n",
+       "      <td>Search for early care and education programs f...</td>\n",
+       "      <td>&lt;!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.0 Tr...</td>\n",
+       "      <td>&lt;html backend_node_id=\"44287\"&gt;\\n  &lt;body&gt;\\n    ...</td>\n",
+       "      <td>b416d08b-90c4-43e1-b3d6-9d4a05e56a06</td>\n",
+       "      <td>{'op': 'CLICK', 'original_op': 'CLICK', 'value...</td>\n",
+       "      <td>```python\\nelement = driver.find_element(By.XP...</td>\n",
+       "      <td>Select the After School Care checkbox.</td>\n",
+       "      <td>[checkbox]  After School Care -&gt; CLICK</td>\n",
+       "      <td>[{'attributes': '{\"backend_node_id\": \"44285\", ...</td>\n",
+       "      <td>['Click on the \"services for RESIDENTS\" link.'...</td>\n",
+       "      <td>['[link]  services for RESIDENTS -&gt; CLICK', '[...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>6669</td>\n",
+       "      <td>test_task</td>\n",
+       "      <td>7f90a191-9dbe-478a-8ae2-8aa45b790158</td>\n",
+       "      <td>Find more films from the director of Smile.</td>\n",
+       "      <td>&lt;!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.0 Tr...</td>\n",
+       "      <td>&lt;html backend_node_id=\"23177\"&gt;\\n  &lt;body&gt;\\n    ...</td>\n",
+       "      <td>fd713700-3876-44a8-80ab-4da898beab42</td>\n",
+       "      <td>{'op': 'CLICK', 'original_op': 'CLICK', 'value...</td>\n",
+       "      <td>```python\\nelement = driver.find_element(By.XP...</td>\n",
+       "      <td>Click on \"Smile.\"</td>\n",
+       "      <td>[div]  Smile -&gt; CLICK</td>\n",
+       "      <td>[{'attributes': '{\"backend_node_id\": \"23642\", ...</td>\n",
+       "      <td>['Search for \"Smile\" in the TV Shows and Movie...</td>\n",
+       "      <td>['[textbox]  Search TV Shows and Movies... -&gt; ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>226</td>\n",
+       "      <td>test_domain</td>\n",
+       "      <td>332ed50d-4772-4eb3-9de9-27ff39abc161</td>\n",
+       "      <td>Create a Fitness board.</td>\n",
+       "      <td>&lt;!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.0 Tr...</td>\n",
+       "      <td>&lt;html backend_node_id=\"123\"&gt;\\n  &lt;body backend_...</td>\n",
+       "      <td>01342a5e-5cc3-45e7-bd9d-b522611fc7bf</td>\n",
+       "      <td>{'op': 'CLICK', 'original_op': 'CLICK', 'value...</td>\n",
+       "      <td>```python\\nelement = driver.find_element(By.XP...</td>\n",
+       "      <td>Click on James Smith's profile image.</td>\n",
+       "      <td>[img]  James Smith -&gt; CLICK</td>\n",
+       "      <td>[{'attributes': '{\"backend_node_id\": \"117\", \"b...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0        split                        annotation_uid  \\\n",
+       "0        6033    test_task  640e0425-bceb-45ff-ba4d-dbc5b62e31d5   \n",
+       "1        3394  test_domain  34e0bf85-6441-40cb-b7f6-d107e5bcb049   \n",
+       "2         320  test_domain  77269ea5-70a4-4cfa-a2f9-9937a1c55096   \n",
+       "3        6669    test_task  7f90a191-9dbe-478a-8ae2-8aa45b790158   \n",
+       "4         226  test_domain  332ed50d-4772-4eb3-9de9-27ff39abc161   \n",
+       "\n",
+       "                                      confirmed_task  \\\n",
+       "0              Find the \"Rock And Roll Over\" reviews   \n",
+       "1         Look up the visitors trend for Apple stock   \n",
+       "2  Search for early care and education programs f...   \n",
+       "3        Find more films from the director of Smile.   \n",
+       "4                            Create a Fitness board.   \n",
+       "\n",
+       "                                            raw_html  \\\n",
+       "0  <!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.0 Tr...   \n",
+       "1  <!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.0 Tr...   \n",
+       "2  <!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.0 Tr...   \n",
+       "3  <!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.0 Tr...   \n",
+       "4  <!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.0 Tr...   \n",
+       "\n",
+       "                                        cleaned_html  \\\n",
+       "0  <html backend_node_id=\"338\">\\n  <body>\\n    <d...   \n",
+       "1  <html backend_node_id=\"14307\">\\n  <div backend...   \n",
+       "2  <html backend_node_id=\"44287\">\\n  <body>\\n    ...   \n",
+       "3  <html backend_node_id=\"23177\">\\n  <body>\\n    ...   \n",
+       "4  <html backend_node_id=\"123\">\\n  <body backend_...   \n",
+       "\n",
+       "                             action_uid  \\\n",
+       "0  96238fb3-bc46-4dff-95c0-4b2d4ecc70ea   \n",
+       "1  8043e8e8-bb7a-4521-80bf-246bc16e883c   \n",
+       "2  b416d08b-90c4-43e1-b3d6-9d4a05e56a06   \n",
+       "3  fd713700-3876-44a8-80ab-4da898beab42   \n",
+       "4  01342a5e-5cc3-45e7-bd9d-b522611fc7bf   \n",
+       "\n",
+       "                                           operation  \\\n",
+       "0  {'op': 'TYPE', 'original_op': 'TYPE', 'value':...   \n",
+       "1  {'op': 'CLICK', 'original_op': 'CLICK', 'value...   \n",
+       "2  {'op': 'CLICK', 'original_op': 'CLICK', 'value...   \n",
+       "3  {'op': 'CLICK', 'original_op': 'CLICK', 'value...   \n",
+       "4  {'op': 'CLICK', 'original_op': 'CLICK', 'value...   \n",
+       "\n",
+       "                                                code  \\\n",
+       "0  ```python\\nelement = driver.find_element(By.XP...   \n",
+       "1  ```python\\nelement = driver.find_element(By.XP...   \n",
+       "2  ```python\\nelement = driver.find_element(By.XP...   \n",
+       "3  ```python\\nelement = driver.find_element(By.XP...   \n",
+       "4  ```python\\nelement = driver.find_element(By.XP...   \n",
+       "\n",
+       "                                    cur_actions_desc  \\\n",
+       "0  Enter \"Rock And Roll Over\" in the text box to ...   \n",
+       "1  Click on \"AAPL\" to look up the visitors trend ...   \n",
+       "2             Select the After School Care checkbox.   \n",
+       "3                                  Click on \"Smile.\"   \n",
+       "4              Click on James Smith's profile image.   \n",
+       "\n",
+       "                                   cur_actions_reprs  \\\n",
+       "0  [textbox]  Enter artist name or song title -> ...   \n",
+       "1                               [div]  AAPL -> CLICK   \n",
+       "2             [checkbox]  After School Care -> CLICK   \n",
+       "3                              [div]  Smile -> CLICK   \n",
+       "4                        [img]  James Smith -> CLICK   \n",
+       "\n",
+       "                                      pos_candidates  \\\n",
+       "0  [{'attributes': '{\"backend_node_id\": \"248\", \"b...   \n",
+       "1  [{'attributes': '{\"backend_node_id\": \"14603\", ...   \n",
+       "2  [{'attributes': '{\"backend_node_id\": \"44285\", ...   \n",
+       "3  [{'attributes': '{\"backend_node_id\": \"23642\", ...   \n",
+       "4  [{'attributes': '{\"backend_node_id\": \"117\", \"b...   \n",
+       "\n",
+       "                                   prev_actions_desc  \\\n",
+       "0                                               None   \n",
+       "1  ['Enter \"apple\" in the search box to look up t...   \n",
+       "2  ['Click on the \"services for RESIDENTS\" link.'...   \n",
+       "3  ['Search for \"Smile\" in the TV Shows and Movie...   \n",
+       "4                                               None   \n",
+       "\n",
+       "                                  prev_actions_reprs  \n",
+       "0                                               None  \n",
+       "1  ['[textbox]  Search for news, symbols or compa...  \n",
+       "2  ['[link]  services for RESIDENTS -> CLICK', '[...  \n",
+       "3  ['[textbox]  Search TV Shows and Movies... -> ...  \n",
+       "4                                               None  "
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd \n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "dataset = load_dataset(\"BigAction/mind2web_clean\")\n",
+    "\n",
+    "df = dataset[\"train\"].to_pandas()\n",
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "import ast\n",
+    "\n",
+    "def keep_assignments(code_snippet):\n",
+    "    # Regex to match variable assignments. This pattern assumes variable names are valid Python identifiers\n",
+    "    # and captures typical assignment statements, excluding those that might appear in comments or strings.\n",
+    "    pattern = r'^\\s*[a-zA-Z_][a-zA-Z0-9_]*\\s*=\\s*.+'\n",
+    "\n",
+    "    # Filter and keep only lines with variable assignments\n",
+    "    assignments = [line for line in code_snippet.split('\\n') if re.match(pattern, line)]\n",
+    "\n",
+    "    # Join the filtered lines back into a string\n",
+    "    return \"\\n\".join(assignments)\n",
+    "\n",
+    "# This function will be used to visit each node in the AST\n",
+    "class VariableVisitor(ast.NodeVisitor):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "        self.output = []\n",
+    "    \n",
+    "    def visit_Assign(self, node):\n",
+    "        \n",
+    "        # For each assignment, print the targets (variables)\n",
+    "        for target in node.targets:\n",
+    "            if isinstance(target, ast.Name):  # Ensure it's a variable assignment\n",
+    "                self.output.append(target.id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bs4 import BeautifulSoup\n",
+    "\n",
+    "def beautify_html(html_string):\n",
+    "    # Use BeautifulSoup to prettify the HTML\n",
+    "    soup = BeautifulSoup(html_string, 'html.parser')\n",
+    "    pretty_html_string = soup.prettify()\n",
+    "    return pretty_html_string\n",
+    "\n",
+    "# Function to extract all 'backend_node_id' attributes\n",
+    "def extract_backend_node_ids(html_content):\n",
+    "    soup = BeautifulSoup(html_content, 'html.parser')\n",
+    "    return set([tag['backend_node_id'] for tag in soup.find_all(attrs={\"backend_node_id\": True})])\n",
+    "\n",
+    "def intersection_score(set1, set2):\n",
+    "    intersection_set = set1 & set2\n",
+    "    intersection_len = len(intersection_set)\n",
+    "    ratio1 = intersection_len / len(set1)\n",
+    "    ratio2 = intersection_len / len(set2) \n",
+    "    return max(ratio1, ratio2), min(ratio1, ratio2)\n",
+    "\n",
+    "def intersection_backend_node_id(ground_truth_outer_html, context_str):\n",
+    "    ground_truth_ids = extract_backend_node_ids(ground_truth_outer_html)\n",
+    "    context_ids = extract_backend_node_ids(context_str)\n",
+    "    recall, precision = intersection_score(ground_truth_ids, context_ids)\n",
+    "    return recall, precision\n",
+    "\n",
+    "def load_html(html, driver):\n",
+    "    file_path = 'sample_page.html'\n",
+    "\n",
+    "    with open(file_path, 'w', encoding='utf-8') as file:\n",
+    "        file.write(html)\n",
+    "        \n",
+    "    abs_file_path = os.path.abspath(\"sample_page.html\")\n",
+    "\n",
+    "    # Use the file:/// protocol to load the local HTML file\n",
+    "    driver.get(f\"file:///{abs_file_path}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
+    "from llama_index.core.node_parser import LangchainNodeParser, CodeSplitter\n",
+    "from llama_index.retrievers.bm25 import BM25Retriever\n",
+    "from llama_index.core import VectorStoreIndex, Document\n",
+    "from lavague.defaults import DefaultEmbedder\n",
+    "\n",
+    "def get_retriever_recursive(embed, html):\n",
+    "    \n",
+    "    K = 3\n",
+    "    \n",
+    "    text_list = [html]\n",
+    "    documents = [Document(text=t) for t in text_list]\n",
+    "    \n",
+    "    splitter = LangchainNodeParser(lc_splitter=RecursiveCharacterTextSplitter.from_language(\n",
+    "        language=\"html\",\n",
+    "    ))\n",
+    "    \n",
+    "    nodes = splitter.get_nodes_from_documents(documents)\n",
+    "    nodes = [node for node in nodes if node.text]\n",
+    "\n",
+    "    index = VectorStoreIndex(nodes, embed_model=embed)\n",
+    "    retriever_recursive = BM25Retriever.from_defaults(index = index, similarity_top_k=K)\n",
+    "    return retriever_recursive\n",
+    "\n",
+    "def get_retriever_code(embed, html):\n",
+    "    \n",
+    "    K = 3\n",
+    "    \n",
+    "    text_list = [html]\n",
+    "    documents = [Document(text=t) for t in text_list]\n",
+    "    \n",
+    "    splitter = CodeSplitter(\n",
+    "        language=\"html\",\n",
+    "        chunk_lines=50,  # lines per chunk\n",
+    "        chunk_lines_overlap=15,  # lines overlap between chunks\n",
+    "        max_chars=2000,  # max chars per chunk\n",
+    "    )\n",
+    "    \n",
+    "    nodes = splitter.get_nodes_from_documents(documents)\n",
+    "    nodes = [node for node in nodes if node.text]\n",
+    "\n",
+    "    index = VectorStoreIndex(nodes, embed_model=embed)\n",
+    "    retriever_code = BM25Retriever.from_defaults(index = index, similarity_top_k=K)\n",
+    "    return retriever_code\n",
+    "\n",
+    "def get_nodes(query, html, get_retriever, embedder):    \n",
+    "    retriever = get_retriever(embedder, html)\n",
+    "    source_nodes = retriever.retrieve(query)\n",
+    "    source_nodes = [node.text for node in source_nodes]\n",
+    "    return source_nodes\n",
+    "\n",
+    "embedder = DefaultEmbedder()\n",
+    "\n",
+    "import re\n",
+    "\n",
+    "def extract_first_python_code(markdown_text: str):\n",
+    "    # Pattern to match the first ```python ``` code block\n",
+    "    pattern = r\"```python(.*?)```\"\n",
+    "\n",
+    "    # Using re.DOTALL to make '.' match also newlines\n",
+    "    match = re.search(pattern, markdown_text, re.DOTALL)\n",
+    "    if match:\n",
+    "        # Return the first matched group, which is the code inside the ```python ```\n",
+    "        return match.group(1).strip()\n",
+    "    else:\n",
+    "        # Return None if no match is found\n",
+    "        return None\n",
+    "decontaminate_html = lambda x: re.sub(r' backend_node_id=\"\\d+\"', '', x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "192it [06:13,  1.94s/it]\n"
+     ]
+    },
+    {
+     "ename": "AttributeError",
+     "evalue": "'NoneType' object has no attribute 'split'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[47], line 42\u001b[0m\n\u001b[1;32m     39\u001b[0m generated_code \u001b[38;5;241m=\u001b[39m extract_first_python_code(response)\n\u001b[1;32m     41\u001b[0m \u001b[38;5;66;03m# Keep only the variable assignments in the generated code\u001b[39;00m\n\u001b[0;32m---> 42\u001b[0m code \u001b[38;5;241m=\u001b[39m \u001b[43mkeep_assignments\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgenerated_code\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     44\u001b[0m \u001b[38;5;66;03m# Split the code into lines and keep only the first assignment\u001b[39;00m\n\u001b[1;32m     45\u001b[0m code \u001b[38;5;241m=\u001b[39m code\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;241m0\u001b[39m]\n",
+      "Cell \u001b[0;32mIn[5], line 10\u001b[0m, in \u001b[0;36mkeep_assignments\u001b[0;34m(code_snippet)\u001b[0m\n\u001b[1;32m      7\u001b[0m pattern \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m^\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124ms*[a-zA-Z_][a-zA-Z0-9_]*\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124ms*=\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124ms*.+\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;66;03m# Filter and keep only lines with variable assignments\u001b[39;00m\n\u001b[0;32m---> 10\u001b[0m assignments \u001b[38;5;241m=\u001b[39m [line \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m \u001b[43mcode_snippet\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m re\u001b[38;5;241m.\u001b[39mmatch(pattern, line)]\n\u001b[1;32m     12\u001b[0m \u001b[38;5;66;03m# Join the filtered lines back into a string\u001b[39;00m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(assignments)\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'split'"
+     ]
+    }
+   ],
+   "source": [
+    "from tqdm import tqdm\n",
+    "\n",
+    "outputs = []\n",
+    "\n",
+    "get_retriever = get_retriever_recursive\n",
+    "retriever_name = get_retriever.__name__\n",
+    "\n",
+    "for index, row in tqdm(df.iterrows()):\n",
+    "    \n",
+    "    if row[\"action_uid\"] in results[\"action_uid\"].values:\n",
+    "        continue\n",
+    "    \n",
+    "    ground_truth_code = row[\"code\"]\n",
+    "    html = row[\"cleaned_html\"]\n",
+    "    query = row[\"cur_actions_desc\"]\n",
+    "    action_uid = row[\"action_uid\"]\n",
+    "\n",
+    "    backend_node_id = ground_truth_code.split(\"backend_node_id\")[1].split(\"']\")[0].replace(\"='\",\"\")\n",
+    "\n",
+    "    load_html(html, driver)\n",
+    "\n",
+    "    source_nodes = get_nodes(query, html, get_retriever, embedder)\n",
+    "    backend_node_id = ground_truth_code.split(\"backend_node_id\")[1].split(\"']\")[0].replace(\"='\",\"\")\n",
+    "\n",
+    "    ground_truth_element = driver.find_element(By.XPATH, f'//*[@backend_node_id=\"{backend_node_id}\"]')\n",
+    "    ground_truth_outer_html = driver.execute_script(\"return arguments[0].outerHTML;\", ground_truth_element)\n",
+    "    \n",
+    "    context_str = \"\\n\".join(source_nodes)\n",
+    "    recall_retriever, precision_retriever = intersection_backend_node_id(ground_truth_outer_html, context_str)\n",
+    "    \n",
+    "    # We remove the backend node ids to ensure the LLM does not use them\n",
+    "    decontaminated_context_str = decontaminate_html(context_str)\n",
+    "    prompt = DEFAULT_PROMPT.format(context_str=decontaminated_context_str, query_str=query)\n",
+    "\n",
+    "    response = llm.complete(prompt).text\n",
+    "\n",
+    "    generated_code = extract_first_python_code(response)\n",
+    "\n",
+    "    # Keep only the variable assignments in the generated code\n",
+    "    code = keep_assignments(generated_code)\n",
+    "\n",
+    "    # Split the code into lines and keep only the first assignment\n",
+    "    code = code.split(\"\\n\")[0]\n",
+    "    parsed_code = ast.parse(code)\n",
+    "\n",
+    "    recall_llm, precision_llm = None, None\n",
+    "    execution_error = \"\"\n",
+    "\n",
+    "    try:\n",
+    "    # Create a visitor instance and use it to visit the nodes in the parsed AST\n",
+    "        visitor = VariableVisitor()\n",
+    "        visitor.visit(parsed_code)\n",
+    "        variable_name = visitor.output[0]\n",
+    "\n",
+    "        # Execute the code to define the first variable\n",
+    "        exec(code)\n",
+    "\n",
+    "        # Assign the variable to the target_element variable which will be used afterwards to compute score\n",
+    "        target_element = None\n",
+    "        exec(f\"\"\"target_element = {variable_name}\"\"\")\n",
+    "\n",
+    "        target_outer_html = driver.execute_script(\"return arguments[0].outerHTML;\", target_element)\n",
+    "        recall_llm, precision_llm = intersection_backend_node_id(ground_truth_outer_html, target_outer_html)\n",
+    "    except Exception as e:\n",
+    "        target_outer_html = \"\"\n",
+    "        execution_error = str(e)\n",
+    "\n",
+    "    output = {\n",
+    "        \"query\": query,\n",
+    "        \"recall_retriever\": recall_retriever,\n",
+    "        \"precision_retriever\": precision_retriever,\n",
+    "        \"recall_llm\": recall_llm,\n",
+    "        \"precision_llm\": precision_llm,\n",
+    "        \"html\": html,\n",
+    "        \"action_uid\": action_uid,\n",
+    "        \"source_nodes\": (\"-\"*10).join(source_nodes),\n",
+    "        \"ground_truth_code\": f\"\"\"ground_truth_element = driver.find_element(By.XPATH, f'//*[@backend_node_id=\"{backend_node_id}\"]')\"\"\",\n",
+    "        \"ground_truth_outer_html\": ground_truth_outer_html,\n",
+    "        \"generated_code\": generated_code,\n",
+    "        \"target_outer_html\": target_outer_html,\n",
+    "        \"execution_error\": execution_error,\n",
+    "        \"retriever\": retriever_name,\n",
+    "        \"model\": model,\n",
+    "    }\n",
+    "    outputs.append(output)\n",
+    "    \n",
+    "    results = pd.DataFrame(outputs)\n",
+    "\n",
+    "    results.to_csv(f\"benchmark_retriever_{retriever_name}_model_{model}.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "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>query</th>\n",
+       "      <th>recall_retriever</th>\n",
+       "      <th>precision_retriever</th>\n",
+       "      <th>recall_llm</th>\n",
+       "      <th>precision_llm</th>\n",
+       "      <th>html</th>\n",
+       "      <th>action_uid</th>\n",
+       "      <th>source_nodes</th>\n",
+       "      <th>ground_truth_code</th>\n",
+       "      <th>ground_truth_outer_html</th>\n",
+       "      <th>generated_code</th>\n",
+       "      <th>target_outer_html</th>\n",
+       "      <th>execution_error</th>\n",
+       "      <th>retriever</th>\n",
+       "      <th>model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Enter \"Rock And Roll Over\" in the text box to ...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>&lt;html backend_node_id=\"338\"&gt;\\n  &lt;body&gt;\\n    &lt;d...</td>\n",
+       "      <td>96238fb3-bc46-4dff-95c0-4b2d4ecc70ea</td>\n",
+       "      <td>&lt;div backend_node_id=\"2662\"&gt;\\n                ...</td>\n",
+       "      <td>driver.find_element(By.XPATH, f'//*[@backend_n...</td>\n",
+       "      <td>&lt;input backend_node_id=\"248\" type=\"text\" name=...</td>\n",
+       "      <td># Let's proceed step by step.\\n# First we need...</td>\n",
+       "      <td>&lt;text backend_node_id=\"2663\"&gt;Hi ! I have a Iba...</td>\n",
+       "      <td></td>\n",
+       "      <td>get_retriever_recursive</td>\n",
+       "      <td>gpt-35-turbo</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Click on \"AAPL\" to look up the visitors trend ...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.114943</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>&lt;html backend_node_id=\"14307\"&gt;\\n  &lt;div backend...</td>\n",
+       "      <td>8043e8e8-bb7a-4521-80bf-246bc16e883c</td>\n",
+       "      <td>&lt;div backend_node_id=\"14708\"&gt;\\n               ...</td>\n",
+       "      <td>driver.find_element(By.XPATH, f'//*[@backend_n...</td>\n",
+       "      <td>&lt;li backend_node_id=\"14603\" role=\"option\" titl...</td>\n",
+       "      <td># Let's proceed step by step.\\n# First we need...</td>\n",
+       "      <td>&lt;text backend_node_id=\"14606\"&gt;AAPL&lt;/text&gt;</td>\n",
+       "      <td></td>\n",
+       "      <td>get_retriever_recursive</td>\n",
+       "      <td>gpt-35-turbo</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Select the After School Care checkbox.</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.008772</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>&lt;html backend_node_id=\"44287\"&gt;\\n  &lt;body&gt;\\n    ...</td>\n",
+       "      <td>b416d08b-90c4-43e1-b3d6-9d4a05e56a06</td>\n",
+       "      <td>&lt;span backend_node_id=\"44871\"&gt;\\n              ...</td>\n",
+       "      <td>driver.find_element(By.XPATH, f'//*[@backend_n...</td>\n",
+       "      <td>&lt;input backend_node_id=\"44285\" type=\"checkbox\"...</td>\n",
+       "      <td># Let's proceed step by step.\\n# First we need...</td>\n",
+       "      <td>&lt;input backend_node_id=\"44285\" type=\"checkbox\"...</td>\n",
+       "      <td></td>\n",
+       "      <td>get_retriever_recursive</td>\n",
+       "      <td>gpt-35-turbo</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Click on \"Smile.\"</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>&lt;html backend_node_id=\"23177\"&gt;\\n  &lt;body&gt;\\n    ...</td>\n",
+       "      <td>fd713700-3876-44a8-80ab-4da898beab42</td>\n",
+       "      <td>&lt;div backend_node_id=\"23101\"&gt;\\n               ...</td>\n",
+       "      <td>driver.find_element(By.XPATH, f'//*[@backend_n...</td>\n",
+       "      <td>&lt;li backend_node_id=\"23642\" role=\"option\"&gt;\\n  ...</td>\n",
+       "      <td># Let's proceed step by step.\\n# First we need...</td>\n",
+       "      <td>&lt;text backend_node_id=\"23648\"&gt;Smile&lt;/text&gt;</td>\n",
+       "      <td></td>\n",
+       "      <td>get_retriever_recursive</td>\n",
+       "      <td>gpt-35-turbo</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Click on James Smith's profile image.</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.011236</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>&lt;html backend_node_id=\"123\"&gt;\\n  &lt;body backend_...</td>\n",
+       "      <td>01342a5e-5cc3-45e7-bd9d-b522611fc7bf</td>\n",
+       "      <td>&lt;body backend_node_id=\"187\"&gt;\\n    &lt;div backend...</td>\n",
+       "      <td>driver.find_element(By.XPATH, f'//*[@backend_n...</td>\n",
+       "      <td>&lt;img backend_node_id=\"117\" alt=\"James Smith\"&gt;</td>\n",
+       "      <td># Let's proceed step by step.\\n# First we need...</td>\n",
+       "      <td>&lt;img backend_node_id=\"117\" alt=\"James Smith\"&gt;</td>\n",
+       "      <td></td>\n",
+       "      <td>get_retriever_recursive</td>\n",
+       "      <td>gpt-35-turbo</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                               query  recall_retriever  \\\n",
+       "0  Enter \"Rock And Roll Over\" in the text box to ...               0.0   \n",
+       "1  Click on \"AAPL\" to look up the visitors trend ...               1.0   \n",
+       "2             Select the After School Care checkbox.               1.0   \n",
+       "3                                  Click on \"Smile.\"               1.0   \n",
+       "4              Click on James Smith's profile image.               1.0   \n",
+       "\n",
+       "   precision_retriever  recall_llm  precision_llm  \\\n",
+       "0             0.000000         0.0       0.000000   \n",
+       "1             0.114943         1.0       0.100000   \n",
+       "2             0.008772         1.0       1.000000   \n",
+       "3             0.050000         1.0       0.333333   \n",
+       "4             0.011236         1.0       1.000000   \n",
+       "\n",
+       "                                                html  \\\n",
+       "0  <html backend_node_id=\"338\">\\n  <body>\\n    <d...   \n",
+       "1  <html backend_node_id=\"14307\">\\n  <div backend...   \n",
+       "2  <html backend_node_id=\"44287\">\\n  <body>\\n    ...   \n",
+       "3  <html backend_node_id=\"23177\">\\n  <body>\\n    ...   \n",
+       "4  <html backend_node_id=\"123\">\\n  <body backend_...   \n",
+       "\n",
+       "                             action_uid  \\\n",
+       "0  96238fb3-bc46-4dff-95c0-4b2d4ecc70ea   \n",
+       "1  8043e8e8-bb7a-4521-80bf-246bc16e883c   \n",
+       "2  b416d08b-90c4-43e1-b3d6-9d4a05e56a06   \n",
+       "3  fd713700-3876-44a8-80ab-4da898beab42   \n",
+       "4  01342a5e-5cc3-45e7-bd9d-b522611fc7bf   \n",
+       "\n",
+       "                                        source_nodes  \\\n",
+       "0  <div backend_node_id=\"2662\">\\n                ...   \n",
+       "1  <div backend_node_id=\"14708\">\\n               ...   \n",
+       "2  <span backend_node_id=\"44871\">\\n              ...   \n",
+       "3  <div backend_node_id=\"23101\">\\n               ...   \n",
+       "4  <body backend_node_id=\"187\">\\n    <div backend...   \n",
+       "\n",
+       "                                   ground_truth_code  \\\n",
+       "0  driver.find_element(By.XPATH, f'//*[@backend_n...   \n",
+       "1  driver.find_element(By.XPATH, f'//*[@backend_n...   \n",
+       "2  driver.find_element(By.XPATH, f'//*[@backend_n...   \n",
+       "3  driver.find_element(By.XPATH, f'//*[@backend_n...   \n",
+       "4  driver.find_element(By.XPATH, f'//*[@backend_n...   \n",
+       "\n",
+       "                             ground_truth_outer_html  \\\n",
+       "0  <input backend_node_id=\"248\" type=\"text\" name=...   \n",
+       "1  <li backend_node_id=\"14603\" role=\"option\" titl...   \n",
+       "2  <input backend_node_id=\"44285\" type=\"checkbox\"...   \n",
+       "3  <li backend_node_id=\"23642\" role=\"option\">\\n  ...   \n",
+       "4      <img backend_node_id=\"117\" alt=\"James Smith\">   \n",
+       "\n",
+       "                                      generated_code  \\\n",
+       "0  # Let's proceed step by step.\\n# First we need...   \n",
+       "1  # Let's proceed step by step.\\n# First we need...   \n",
+       "2  # Let's proceed step by step.\\n# First we need...   \n",
+       "3  # Let's proceed step by step.\\n# First we need...   \n",
+       "4  # Let's proceed step by step.\\n# First we need...   \n",
+       "\n",
+       "                                   target_outer_html execution_error  \\\n",
+       "0  <text backend_node_id=\"2663\">Hi ! I have a Iba...                   \n",
+       "1          <text backend_node_id=\"14606\">AAPL</text>                   \n",
+       "2  <input backend_node_id=\"44285\" type=\"checkbox\"...                   \n",
+       "3         <text backend_node_id=\"23648\">Smile</text>                   \n",
+       "4      <img backend_node_id=\"117\" alt=\"James Smith\">                   \n",
+       "\n",
+       "                 retriever         model  \n",
+       "0  get_retriever_recursive  gpt-35-turbo  \n",
+       "1  get_retriever_recursive  gpt-35-turbo  \n",
+       "2  get_retriever_recursive  gpt-35-turbo  \n",
+       "3  get_retriever_recursive  gpt-35-turbo  \n",
+       "4  get_retriever_recursive  gpt-35-turbo  "
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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cBQUFXl5eAwcO9PLyCgwMpAmdHTt2qKmpOTs79+zZU1JSUktLizufwsF/EkWnrq6+cePGp0+f0lkdHR1LS0shIfEckHXr1i1YsODSpUs+Pj7nz59v2W5euHCB9ggcNmyYj48Pm83+aNghISHFxcXc52Lp0qUsFmv79u3h4eE0oSDwYuBoKoHVvXt3IyMjQkhwcLCqqur69es5i44cOeLo6Mjf/09VVVVMTGz58uWVlZW0xMXFRVlZubq6etKkSTk5OZMnT54wYQJPwISQzZs3Ozg4BAYGOjo62tjY8DT7+PHjrl27jh079tmzZ/xx2tnZmZqacu/X27dv//33X3t7e+5qAg84afqmEH7QCCEPHjxwdXUNCgqytrb+5ZdfeJay2WzCMI9f1V19WMX9r6a+kU1Y5TXsN6UNL9/Xv/7QUFbF5j7Ru3btCggI8PX1FRcX37NnD6f86tWr5ubm+vr6gwYNkpGRadu27dKlS9esWdO3b19ZWVk7O7vy8nJaMzg42MvLKzg4uE+fPnl5eYSQ2bNn9+zZMygoKCQkRF5envaoa+qYiHiv8evatWvv3r0zMjIMDQ1DQ0MJITdv3rS0tHR0dNy2bZuFhQX9uYpVq1a5uLh4e3sPHDiwsLCQuwVtbe3evXsLbJwmsAghUVFRlZWV3PnKDRs2REZG8q+irq5eUVGxcuVKzuGlD9uTJ09y36rcARNC6uvrZ8+ePWDAgICAgD59+nB3nKUWL17ctWvXT09VAwAAAADAjw75K4L8FfJXyF9xQf6KIH+F/BUAAAAAwA+KaaXmzJljZGS0atUqQ0PDOXPmiL7i6NGjS0tLGYZZvXq1rKwsnaZo3uTatWuVlZUnTpxgsVi//fbbmzdv3rx5Y2FhMXz4cFpt6dKl9fX1DMMkJCTo6ekxDHP06NHLly8zDFNcXKysrLxw4UJac8aMGU5OTo2NjQzDHDhwQEFB4c2bNwzDLFy40MTEpKSkhM1m//rrr1JSUnV1dTxxPnnyhHYftLOzS0lJqays5CxKSEjQ0dGh02/evFm7di3DMI2Njerq6lu2bGEYZuXKlQEBAbQCJxh7e/vRo0dXVVXV1tb6+flZWVkxDMNms7t167Z69WqGYZ4/fy4hIXHlyhWGYXr37h0dHU1XTE5O1tDQ4D+SNKOXmZlJZ3///XcxMbHXr1/TbTk4OFy6dOmPP/64efPmvXv3CCEPHz5kGKaoqEhJSWn79u10reXLl9+8ebOwsFBGRub27dsMw+zYsUNDQ4N+n+TfC259+vThlE+dOtXS0pJnT2tqajw8PBwcHPjX5T+JDMPcvXuXEFJQUEBn/f39R4wYwb/uvn37xMXFpaSkgoKCjhw5wmazRQmJ+4CMGDHC39+fLmrxbtLL9f379/yrMAyjpKQ0efJkOn358uVNmzbRae5zwTAMIWTChAk5OTljxoxp6mLgV1BQQAhJTk7mXzR16lQpKSl6nTMMM3jw4NzcXIGNTJ8+nRCiqqo6ceLEW7du8TR+9+5d/oAPHz4sIyOTn59PF4WEhNCJLl26rFq1imGYjIyMuXPnCtzcqlWrNm3atHr1akLIxYsXaeHixYu3bt36119/cW+xqQMu8KZo6qBx36SDBg1KTExkGKahoWHx4sU8gW3YkHzl7vMbj6ov5lUI+Xf1YdXt/PcbNvzfMWez2UFBQXTaz8/P2tqau81ff/1VX1//6dOnFRUVixYtIoRkZWWVlpbevXtXTk5u48aNtNqiRYvohL+//+jRo+mKDQ0NDMOcPHmSEHLmzBnhx0SUe43nQbF8+XLOLRYcHDxs2DA6vXDhwg4dOhw5cuSff/6Jj4//66+/tLW1KyoqGIaJiIgYNGgQf8vUpk2buC9pbj179jQ3N6fTtbW15ubm9K7nUVdXZ21tTQjp2rXrkiVLioqKOIu4b1WegGfMmGFubk6fzPX19aGhoQzDbN26VUlJiWGY6upqPz8/zp4CAAAAAPycSpvjWwf7pSB/RaeRv6LTyF/xQP6KB/JXyF8hfwUAAAAA8JU1K3/VOkfAys3NzcjIuHfvXkxMTF5eXmZmpoj9CKurq2tqauhw0EFBQXV1dfy9x/T19eXk5FxcXDQ0NFRVVdXV1dXV1e3t7TldWGJjY+nw3e3bty8qKiKEODo60uF/586d26ZNm5kzZ9Ka69evHzp0KO3O4unpKSsru3XrVrpIVlZWRUWFxWINHjy4rq6O00uJQ0dH5+7du5MmTfr333/Dw8MNDQ2vXbvGv0fq6upRUVGEEDExsXbt2tF43r179/DhQ9pPiLvHEu0ZKSUl5ebmRjvAZWdn371718/PjxCipaWlq6t7/Pjx69evX79+ffz48aIcUurOnTurV68eO3Zsu3btaEmvXr2srKxiYmLMzMy4a27fvl1CQoIzpvewYcO0tbV37dqlpaXVvXt3Qoi9vf3r16+zs7Ob2gsqOzv72rVrw4cPp7OjR4++evVqdnY2955KS0tz9pQH/0kUnbe395UrV7y9vfft2zdw4EA7O7u3b99+NKSmDsin7KZw6enpDg4ODg4Oo0ePFlLNzc3N1NQ0MTFR4MUgyoa4RUZG1tfX05xCYWFhZWWlsbGxwJorV65MT083MTFJSEjo0aNHVFQUwzU+uUBr1651d3fnDME9Z84c7qU3btw4ceIE7VjZlLCwMDk5OdrPr66ubt++fcHBwdwVmjrgTd0Uohy0d+/e0QyduLj4/PnzeZay2WyGIQzDMGyh/xhGoU2bsWPD6Vrnz5/ndO8LCQm5ePHi48ePuZuVl5fX1taWl5enO9ihQwdFRUXad5nzKFuwYAGd4NwFU6dOFRcXb2hoiImJ8ff3d3R0FHJM6OxH7zVq3rx59GqkOUSBNDU1PTw87OzsIiIiNm/e7OrqKi8vTwixt7c/fvy4KN1keURFRd2+ffvq1auEkL1793p7e3N+eYGbpKTkmTNnVq5cyTDMvHnzunTpQi9gIRobG9evXz9q1CjaO1ZCQmLGjBmcpQzDTJw4ccGCBbRjLgAAAAAA/LSQv+JA/orOIn/FD/krfshfIX/FXw35KwAAAACA70HrfAHr5MmT3CPxRkZGnjhxQpQVDx06dPfuXXd3d3d39xEjRigpKXGP4s5DXFyc85WJe3r//v3Dhg2bOHFiWloa/cotLS0tJiZ269atlJSUlStX0q80xcXFZWVlmpqadC0Wi9WpUyeeL5m0ZUKHcebTvn37+Pj4oqKi1NTUiooKnm/a1Lt376ZNmzZ69OiYmJhXr17ReMLCwl68eKGrqxsTE/P8+XP+tcTExOgW6RfOmJiYkJCQkJAQRUVFOTm5Bw8eEEL09PSEHcr/Wrx4sa2t7YQJE6ZNm5aYmMi/dzzfGPPy8rS1tWlSjxCipaWlqqr69OnTkpISGsPkyZNNTU0ZhhG+F48ePSKEcA6vtrY2IUTg4RV4bPlPYrP06tVrz549RUVFsbGxFy9epClLUULiPyCfZTcFCggIOHv27NmzZ9esWfPRyjIyMgIvBlE2xK1r167Ozs4bNmxgGGbjxo3h4eFCKvv7+587d+7evXve3t7r1q3jpHebkpeXp6ury5mlGU/q+fPngwcPrq2tFd6CkpJSSEjInj17SkpKtm/f7u/vLykpyV2hqQPe1E0hykGbNGlSWlqakZHRihUrqqqqeJbSHqhsNqH/GtlMXWPd25qnJTUv6hpqOeWykiy9duKctXbv3r1//376KFu/fr2kpCT3WOXceJ4wnDuisbHxzz//HD58+OTJk8+fP0/vAhr82rVrHz169Mcffwg/JvwbEpJjWrJkCb0ax44d21QdDno1nj9/nh7Vbdu2GRoafvTk8hs6dKiSkhL9TYHU1FQhm5aWlp4+ffrDhw9PnTrVpUuXiIgI4XfZy5cvy8rKmroa58+fv2XLlhbcPgAAAAAA0Mogf8WB/BWdRf6KH/JX/JC/Qv5KIOSvAAAAAAC+OQG9JVoBFxcXf3//mJgYOpuUlJSRkSHKin/99dfZs2dVVVXpbHx8/NSpU1+8eNGxY0cRN339+vWAgID79+/r6emlp6efOnWKs2jy5Mk2NjacNBOLxSKEcCdH2Gw2LRRFTU2NjIwMIURaWnrUqFGSkpLDhw9/+fIl52skNXLkyHbt2qWmphJCOMEYGxs/fPhwx44diYmJW7duzcnJ4VmLR3JysrKyMmc2LS2NEMLJMQm3YMECHx8fEXeKECIjIyMwYdSpU6ddu3bxFArZC57DS785i3h4hZxEUXBOTdu2bZcuXfr69Ws6mnrLQhJ+sj5lNzksLCzat28vYmWei6EFoqOj/fz8Tp48efDgwYsXLwqsw2azGxsbafLIyMgoIyPDyMjowoULYWFhQlpu6uIhhOzfv3/lypXDhw+PiIiwsrIS0khkZGRqampaWtpff/11+PBhnqVNHfC6ujrS9E0h/KANGTKkb9++aWlpK1as2LdvH88xYRiG+e8BKa9/c7/s9NXizWKSLEKIkriWhUqgroK1jpqqYSdZzioNDQ35+fl///03p8TX13f79u383ROFiIuLS0lJuXXrlrS09IQJE/Lz82n5u3fvfvnllxkzZnB6v32Wi5AjKCiobdu2otR0c3OLi4tr2VYoOTm5ESNGpKSkREZGKigoaGlpCaxWV1cnKSlJ98jZ2fnIkSPa2tpXrlwRksSnTwCBV2NZWZmqqmrv3r0nT5585MiRT4kfAAAAAAB+dMhfcSB/RWeRvxIC+StuyF9RyF9xIH8FAAAAAPA9aJ0jYJmYmPj6+hoZGcXHxxsbG/v5+ZmYmHx0rdLSUvrVglPi7e3NZrN37Ngh+qavX7/evn17/i82e/bsOX/+PGd04szMTDU1NWVlZc7Y7AzDFBYW6uvri7ihSZMmcX870tXVZbFYSkpKPNWuXbtGf/2d2/Pnz5WVlaOjo69fvy4mJpaVldXUVjp16kQIuX//Pn8h7Tb02XXt2vXx48c8/ag6depUUFBAcwQcwveCHknO4X327Bm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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "row = results.iloc[3]\n",
+    "\n",
+    "html = row[\"html\"]\n",
+    "load_html(html, driver)\n",
+    "\n",
+    "ground_truth_code = row[\"ground_truth_code\"]\n",
+    "exec(f\"ground_truth_element = {ground_truth_code}\")\n",
+    "generated_code = row[\"generated_code\"]\n",
+    "\n",
+    "code = keep_assignments(generated_code)\n",
+    "\n",
+    "# Split the code into lines and keep only the first assignment\n",
+    "code = code.split(\"\\n\")[0]\n",
+    "parsed_code = ast.parse(code)\n",
+    "\n",
+    "# Create a visitor instance and use it to visit the nodes in the parsed AST\n",
+    "visitor = VariableVisitor()\n",
+    "visitor.visit(parsed_code)\n",
+    "variable_name = visitor.output[0]\n",
+    "\n",
+    "# Execute the code to define the first variable\n",
+    "exec(code)\n",
+    "\n",
+    "# Assign the variable to the target_element variable which will be used afterwards to compute score\n",
+    "target_element = None\n",
+    "exec(f\"\"\"target_element = {variable_name}\"\"\")\n",
+    "\n",
+    "def highlight_element(element, driver):\n",
+    "    driver.execute_script(\"\"\"\n",
+    "    arguments[0].style.outline = \"10px dashed blue\";\n",
+    "    arguments[0].style.backgroundColor = \"rgba(135, 206, 250, 0.3)\";\n",
+    "    arguments[0].style.borderRadius = \"50px\";\"\"\", element)\n",
+    "    \n",
+    "def unhighlight_element(element, driver):\n",
+    "    driver.execute_script(\"\"\"\n",
+    "    arguments[0].style.outline = \"\";\n",
+    "    arguments[0].style.backgroundColor = \"\";\n",
+    "    arguments[0].style.borderRadius = \"\";\"\"\", element)\n",
+    "\n",
+    "highlight_element(ground_truth_element, driver)\n",
+    "driver.save_screenshot(\"screenshot1.png\")\n",
+    "unhighlight_element(ground_truth_element, driver)\n",
+    "\n",
+    "highlight_element(target_element, driver)\n",
+    "driver.save_screenshot(\"screenshot2.png\")\n",
+    "\n",
+    "def combine_screenshots(output_path=\"combined_screenshot.png\"):\n",
+    "    from PIL import Image, ImageDraw, ImageFont\n",
+    "\n",
+    "    # Load the images\n",
+    "    image1 = Image.open(\"screenshot1.png\")\n",
+    "    image2 = Image.open(\"screenshot2.png\")\n",
+    "    \n",
+    "    # Calculate dimensions for the combined image\n",
+    "    total_width = image1.width + image2.width\n",
+    "    max_height = max(image1.height, image2.height)\n",
+    "    \n",
+    "    # Create a new blank image with the correct size\n",
+    "    combined_image = Image.new('RGB', (total_width, max_height))\n",
+    "    \n",
+    "    # Paste the original images into the combined image\n",
+    "    combined_image.paste(image1, (0, 0))\n",
+    "    combined_image.paste(image2, (image1.width, 0))\n",
+    "    \n",
+    "    draw = ImageDraw.Draw(combined_image)\n",
+    "    font = ImageFont.load_default()  # Load default font\n",
+    "    draw.text((10, 10), \"Targeted element\", fill=\"black\", font=font)\n",
+    "    draw.text((image1.width + 10, 10), \"Ground truth\", fill=\"black\", font=font)\n",
+    "\n",
+    "    \n",
+    "    # Save the combined image\n",
+    "    combined_image.save(output_path)\n",
+    "\n",
+    "from IPython.display import Image, display\n",
+    "combine_screenshots()\n",
+    "display(Image(\"combined_screenshot.png\"))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "myenv",
+   "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": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}