{ "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": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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": 72, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "" ] }, "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 }