{ "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": [ "
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Unnamed: 0splitannotation_uidconfirmed_taskraw_htmlcleaned_htmlaction_uidoperationcodecur_actions_desccur_actions_reprspos_candidatesprev_actions_descprev_actions_reprs
06033test_task640e0425-bceb-45ff-ba4d-dbc5b62e31d5Find the \"Rock And Roll Over\" reviews<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.0 Tr...<html backend_node_id=\"338\">\\n <body>\\n <d...96238fb3-bc46-4dff-95c0-4b2d4ecc70ea{'op': 'TYPE', 'original_op': 'TYPE', 'value':...```python\\nelement = driver.find_element(By.XP...Enter \"Rock And Roll Over\" in the text box to ...[textbox] Enter artist name or song title -> ...[{'attributes': '{\"backend_node_id\": \"248\", \"b...NoneNone
13394test_domain34e0bf85-6441-40cb-b7f6-d107e5bcb049Look up the visitors trend for Apple stock<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.0 Tr...<html backend_node_id=\"14307\">\\n <div backend...8043e8e8-bb7a-4521-80bf-246bc16e883c{'op': 'CLICK', 'original_op': 'CLICK', 'value...```python\\nelement = driver.find_element(By.XP...Click on \"AAPL\" to look up the visitors trend ...[div] AAPL -> CLICK[{'attributes': '{\"backend_node_id\": \"14603\", ...['Enter \"apple\" in the search box to look up t...['[textbox] Search for news, symbols or compa...
2320test_domain77269ea5-70a4-4cfa-a2f9-9937a1c55096Search for early care and education programs f...<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.0 Tr...<html backend_node_id=\"44287\">\\n <body>\\n ...b416d08b-90c4-43e1-b3d6-9d4a05e56a06{'op': 'CLICK', 'original_op': 'CLICK', 'value...```python\\nelement = driver.find_element(By.XP...Select the After School Care checkbox.[checkbox] After School Care -> CLICK[{'attributes': '{\"backend_node_id\": \"44285\", ...['Click on the \"services for RESIDENTS\" link.'...['[link] services for RESIDENTS -> CLICK', '[...
36669test_task7f90a191-9dbe-478a-8ae2-8aa45b790158Find more films from the director of Smile.<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.0 Tr...<html backend_node_id=\"23177\">\\n <body>\\n ...fd713700-3876-44a8-80ab-4da898beab42{'op': 'CLICK', 'original_op': 'CLICK', 'value...```python\\nelement = driver.find_element(By.XP...Click on \"Smile.\"[div] Smile -> CLICK[{'attributes': '{\"backend_node_id\": \"23642\", ...['Search for \"Smile\" in the TV Shows and Movie...['[textbox] Search TV Shows and Movies... -> ...
4226test_domain332ed50d-4772-4eb3-9de9-27ff39abc161Create a Fitness board.<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.0 Tr...<html backend_node_id=\"123\">\\n <body backend_...01342a5e-5cc3-45e7-bd9d-b522611fc7bf{'op': 'CLICK', 'original_op': 'CLICK', 'value...```python\\nelement = driver.find_element(By.XP...Click on James Smith's profile image.[img] James Smith -> CLICK[{'attributes': '{\"backend_node_id\": \"117\", \"b...NoneNone
\n", "
" ], "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 \\n \\n \\n
\\n \\n ... \n", "3 \\n \\n ... \n", "4 \\n ... \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": [ "
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queryrecall_retrieverprecision_retrieverrecall_llmprecision_llmhtmlaction_uidsource_nodesground_truth_codeground_truth_outer_htmlgenerated_codetarget_outer_htmlexecution_errorretrievermodel
0Enter \"Rock And Roll Over\" in the text box to ...0.00.0000000.00.000000<html backend_node_id=\"338\">\\n <body>\\n <d...96238fb3-bc46-4dff-95c0-4b2d4ecc70ea<div backend_node_id=\"2662\">\\n ...driver.find_element(By.XPATH, f'//*[@backend_n...<input backend_node_id=\"248\" type=\"text\" name=...# Let's proceed step by step.\\n# First we need...<text backend_node_id=\"2663\">Hi ! I have a Iba...get_retriever_recursivegpt-35-turbo
1Click on \"AAPL\" to look up the visitors trend ...1.00.1149431.00.100000<html backend_node_id=\"14307\">\\n <div backend...8043e8e8-bb7a-4521-80bf-246bc16e883c<div backend_node_id=\"14708\">\\n ...driver.find_element(By.XPATH, f'//*[@backend_n...<li backend_node_id=\"14603\" role=\"option\" titl...# Let's proceed step by step.\\n# First we need...<text backend_node_id=\"14606\">AAPL</text>get_retriever_recursivegpt-35-turbo
2Select the After School Care checkbox.1.00.0087721.01.000000<html backend_node_id=\"44287\">\\n <body>\\n ...b416d08b-90c4-43e1-b3d6-9d4a05e56a06<span backend_node_id=\"44871\">\\n ...driver.find_element(By.XPATH, f'//*[@backend_n...<input backend_node_id=\"44285\" type=\"checkbox\"...# Let's proceed step by step.\\n# First we need...<input backend_node_id=\"44285\" type=\"checkbox\"...get_retriever_recursivegpt-35-turbo
3Click on \"Smile.\"1.00.0500001.00.333333<html backend_node_id=\"23177\">\\n <body>\\n ...fd713700-3876-44a8-80ab-4da898beab42<div backend_node_id=\"23101\">\\n ...driver.find_element(By.XPATH, f'//*[@backend_n...<li backend_node_id=\"23642\" role=\"option\">\\n ...# Let's proceed step by step.\\n# First we need...<text backend_node_id=\"23648\">Smile</text>get_retriever_recursivegpt-35-turbo
4Click on James Smith's profile image.1.00.0112361.01.000000<html backend_node_id=\"123\">\\n <body backend_...01342a5e-5cc3-45e7-bd9d-b522611fc7bf<body backend_node_id=\"187\">\\n <div backend...driver.find_element(By.XPATH, f'//*[@backend_n...<img backend_node_id=\"117\" alt=\"James Smith\"># Let's proceed step by step.\\n# First we need...<img backend_node_id=\"117\" alt=\"James Smith\">get_retriever_recursivegpt-35-turbo
\n", "
" ], "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 \\n \\n \\n
\\n \\n ... \n", "3 \\n \\n ... \n", "4 \\n \\n ... \n", "1
\\n ... \n", "2 \\n ... \n", "3
\\n ... \n", "4 \\n
\\n ... \n", "4 \"James \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 Hi ! I have a Iba... \n", "1 AAPL \n", "2 Smile \n", "4 \"James \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": 96, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Retriever performance: 0.8177083333333334\n", "LLM performance: 0.5572916666666666\n", "Deperdition rate: 0.6815286624203821\n" ] } ], "source": [ "print(\"Retriever performance: \", (results.recall_retriever == 1.0).mean())\n", "print(\"LLM performance: \", (results.recall_llm == 1.0).mean())\n", "print(\"Deperdition rate: \", (results.recall_llm == 1.0).mean() / (results.recall_retriever == 1.0).mean())" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.6242038216560509" ] }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ "perfect_retriever = results.loc[results.recall_retriever == 1.0]\n", "(perfect_retriever.recall_llm == 1.0).mean()\n", "# perfect_retriever = perfect_retriever.loc[~perfect_retriever.recall_llm.isna()]" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "row = results.iloc[0]\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": { 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