{
"cells": [
{
"cell_type": "markdown",
"id": "674fa1cc",
"metadata": {},
"source": [
"# Testing Agent Components\n",
"\n",
"I used this notebook to test different tools and components. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "073cfcab",
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "88eccfb3",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/make/projects/learning/Final_Assignment/.venv/lib/python3.11/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"
]
},
{
"data": {
"text/html": [
"CodeAgent | Qwen/Qwen3-30B-A3B\n",
"├── ✅ Authorized imports: ['os', 'pandas', 'numpy', 'PIL', 'tempfile', 'PIL.Image']\n",
"└── 🛠️ Tools:\n",
" ┏━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
" ┃ Name ┃ Description ┃ Arguments ┃\n",
" ┡━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
" │ final_answer │ Used to submit the final answer to the │ answer (`any`): The final answer to the │\n",
" │ │ problem. │ problem │\n",
" │ web_search │ Performs a duckduckgo web search based │ query (`string`): The search query to │\n",
" │ │ on your query (think a Google search) │ perform. │\n",
" │ │ then returns the top search results. │ │\n",
" │ visit_webpage │ Visits a webpage at the given url and │ url (`string`): The url of the webpage │\n",
" │ │ reads its content as a markdown string. │ to visit. │\n",
" │ │ Use this to browse webpages. │ │\n",
" │ wikipedia_search │ Searches Wikipedia and returns a │ query (`string`): The topic to search │\n",
" │ │ summary or full text of the given │ on Wikipedia. │\n",
" │ │ topic, along with the page URL. │ │\n",
" │ transcriber │ This is a tool that transcribes an │ audio (`audio`): The audio to │\n",
" │ │ audio into text. It returns the │ transcribe. Can be a local path, an │\n",
" │ │ transcribed text. │ url, or a tensor. │\n",
" │ youtube_transcribe │ Transcribes a YouTube video. Use when │ url (`string`): Url of the YouTube │\n",
" │ │ you need to process the audio from a │ video │\n",
" │ │ YouTube video into Text. │ │\n",
" │ read_file │ Used to read the content of a file. │ filepath (`string`): The path to the │\n",
" │ │ Returns the content as a string. │ file to be read. │\n",
" │ │ Will only work for text-based files, │ │\n",
" │ │ such as .txt files or code files. │ │\n",
" │ │ Do not use for audio or visual files. │ │\n",
" │ extract_text_from_image │ Extract text from an image using │ image_path (`string`): Path to the │\n",
" │ │ pytesseract (if available). │ image file │\n",
" │ analyze_csv_file │ Analyze a CSV file using pandas and │ file_path (`string`): Path to the CSV │\n",
" │ │ answer a question about it. │ file │\n",
" │ │ To use this file you need to have saved │ query (`string`): Question about the │\n",
" │ │ it in a location and pass that location │ data │\n",
" │ │ to the function. │ │\n",
" │ │ The download_file_from_url tool will │ │\n",
" │ │ save it by name to │ │\n",
" │ │ tempfile.gettempdir() │ │\n",
" │ analyze_excel_file │ Analyze an Excel file using pandas and │ file_path (`string`): Path to the Excel │\n",
" │ │ answer a question about it. │ file │\n",
" │ │ To use this file you need to have saved │ query (`string`): Question about the │\n",
" │ │ it in a location and pass that location │ data │\n",
" │ │ to the function. │ │\n",
" │ │ The download_file_from_url tool will │ │\n",
" │ │ save it by name to │ │\n",
" │ │ tempfile.gettempdir() │ │\n",
" │ transcribe_audio │ Transcribes an audio file. Use when │ audio_file_path (`string`): Filepath to │\n",
" │ │ you need to process audio data. │ the audio file (str) │\n",
" │ │ DO NOT use this tool for YouTube video; │ │\n",
" │ │ use the youtube_transcribe tool to │ │\n",
" │ │ process audio data from YouTube. │ │\n",
" │ │ Use this tool when you have an audio │ │\n",
" │ │ file in .mp3, .wav, .aac, .ogg, .flac, │ │\n",
" │ │ .m4a, .alac or .wma │ │\n",
" └─────────────────────────┴─────────────────────────────────────────┴─────────────────────────────────────────┘\n",
"\n"
],
"text/plain": [
"\u001b[1;38;2;212;183;2mCodeAgent | Qwen/Qwen3-30B-A3B\u001b[0m\n",
"├── ✅ \u001b[3;38;2;30;144;255mAuthorized imports:\u001b[0m ['os', 'pandas', 'numpy', 'PIL', 'tempfile', 'PIL.Image']\n",
"└── 🛠️ \u001b[3;38;2;30;144;255mTools:\u001b[0m\n",
" ┏━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
" ┃\u001b[1m \u001b[0m\u001b[1mName \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mDescription \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mArguments \u001b[0m\u001b[1m \u001b[0m┃\n",
" ┡━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
" │\u001b[38;2;30;144;255m \u001b[0m\u001b[38;2;30;144;255mfinal_answer \u001b[0m\u001b[38;2;30;144;255m \u001b[0m│ Used to submit the final answer to the │ answer (`any`): The final answer to the │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ problem. │ problem │\n",
" │\u001b[38;2;30;144;255m \u001b[0m\u001b[38;2;30;144;255mweb_search \u001b[0m\u001b[38;2;30;144;255m \u001b[0m│ Performs a duckduckgo web search based │ query (`string`): The search query to │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ on your query (think a Google search) │ perform. │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ then returns the top search results. │ │\n",
" │\u001b[38;2;30;144;255m \u001b[0m\u001b[38;2;30;144;255mvisit_webpage \u001b[0m\u001b[38;2;30;144;255m \u001b[0m│ Visits a webpage at the given url and │ url (`string`): The url of the webpage │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ reads its content as a markdown string. │ to visit. │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ Use this to browse webpages. │ │\n",
" │\u001b[38;2;30;144;255m \u001b[0m\u001b[38;2;30;144;255mwikipedia_search \u001b[0m\u001b[38;2;30;144;255m \u001b[0m│ Searches Wikipedia and returns a │ query (`string`): The topic to search │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ summary or full text of the given │ on Wikipedia. │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ topic, along with the page URL. │ │\n",
" │\u001b[38;2;30;144;255m \u001b[0m\u001b[38;2;30;144;255mtranscriber \u001b[0m\u001b[38;2;30;144;255m \u001b[0m│ This is a tool that transcribes an │ audio (`audio`): The audio to │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ audio into text. It returns the │ transcribe. Can be a local path, an │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ transcribed text. │ url, or a tensor. │\n",
" │\u001b[38;2;30;144;255m \u001b[0m\u001b[38;2;30;144;255myoutube_transcribe \u001b[0m\u001b[38;2;30;144;255m \u001b[0m│ Transcribes a YouTube video. Use when │ url (`string`): Url of the YouTube │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ you need to process the audio from a │ video │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ YouTube video into Text. │ │\n",
" │\u001b[38;2;30;144;255m \u001b[0m\u001b[38;2;30;144;255mread_file \u001b[0m\u001b[38;2;30;144;255m \u001b[0m│ Used to read the content of a file. │ filepath (`string`): The path to the │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ Returns the content as a string. │ file to be read. │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ Will only work for text-based files, │ │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ such as .txt files or code files. │ │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ Do not use for audio or visual files. │ │\n",
" │\u001b[38;2;30;144;255m \u001b[0m\u001b[38;2;30;144;255mextract_text_from_image\u001b[0m\u001b[38;2;30;144;255m \u001b[0m│ Extract text from an image using │ image_path (`string`): Path to the │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ pytesseract (if available). │ image file │\n",
" │\u001b[38;2;30;144;255m \u001b[0m\u001b[38;2;30;144;255manalyze_csv_file \u001b[0m\u001b[38;2;30;144;255m \u001b[0m│ Analyze a CSV file using pandas and │ file_path (`string`): Path to the CSV │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ answer a question about it. │ file │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ To use this file you need to have saved │ query (`string`): Question about the │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ it in a location and pass that location │ data │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ to the function. │ │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ The download_file_from_url tool will │ │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ save it by name to │ │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ tempfile.gettempdir() │ │\n",
" │\u001b[38;2;30;144;255m \u001b[0m\u001b[38;2;30;144;255manalyze_excel_file \u001b[0m\u001b[38;2;30;144;255m \u001b[0m│ Analyze an Excel file using pandas and │ file_path (`string`): Path to the Excel │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ answer a question about it. │ file │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ To use this file you need to have saved │ query (`string`): Question about the │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ it in a location and pass that location │ data │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ to the function. │ │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ The download_file_from_url tool will │ │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ save it by name to │ │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ tempfile.gettempdir() │ │\n",
" │\u001b[38;2;30;144;255m \u001b[0m\u001b[38;2;30;144;255mtranscribe_audio \u001b[0m\u001b[38;2;30;144;255m \u001b[0m│ Transcribes an audio file. Use when │ audio_file_path (`string`): Filepath to │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ you need to process audio data. │ the audio file (str) │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ DO NOT use this tool for YouTube video; │ │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ use the youtube_transcribe tool to │ │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ process audio data from YouTube. │ │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ Use this tool when you have an audio │ │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ file in .mp3, .wav, .aac, .ogg, .flac, │ │\n",
" │\u001b[38;2;30;144;255m \u001b[0m│ .m4a, .alac or .wma │ │\n",
" └─────────────────────────┴─────────────────────────────────────────┴─────────────────────────────────────────┘\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from agents import manager_agent\n",
"manager_agent.visualize()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e1925d1f",
"metadata": {},
"outputs": [],
"source": [
"from PIL import Image\n",
"\n",
"#manager_agent.run(\"How many bird species are in this image?\", images=images)\n",
"manager_agent.run(\"In the video https://www.youtube.com/watch?v=L1vXCYZAYYM, what is the highest number of bird species to be on camera simultaneously?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1c904403",
"metadata": {},
"outputs": [],
"source": [
"manager_agent.run('How many studio albums were published by Mercedes Sosa between 2000 and 2009 (included)? You can use the latest 2022 version of english wikipedia.')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ca93462b",
"metadata": {},
"outputs": [],
"source": [
"DEFAULT_API_URL = \"https://agents-course-unit4-scoring.hf.space\"\n",
"question = \"What is the final numeric output from the attached Python code?\"\n",
"task_file_path = \"agent_benchmark_cache/f918266a-b3e0-4914-865d-4faa564f1aef.py\"\n",
"context = f\"\"\"\n",
"Question: {question}\n",
"\n",
"This question has an associated file. File is already downloaded at:\n",
"{task_file_path}\n",
"\n",
"Analyze the file content above to answer the question using tools.\n",
"\"\"\"\n",
"full_prompt = f\"\"\"{context}\n",
"\n",
"When answering, provide ONLY the precise answer requested. \n",
"Do not include explanations, steps, reasoning, or additional text.\n",
"Be direct and specific. GAIA benchmark requires exact matching answers.\n",
"For example, if asked \"What is the capital of France?\", respond simply with \"Paris\".\n",
"\"\"\"\n",
"manager_agent.run(context)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4e9bf1e6",
"metadata": {},
"outputs": [],
"source": [
"question = \"Hi, I was out sick from my classes on Friday, so I'm trying to figure out what I need to study for my Calculus mid-term next week. My friend from class sent me an audio recording of Professor Willowbrook giving out the recommended reading for the test, but my headphones are broken :(Could you please listen to the recording for me and tell me the page numbers I'm supposed to go over? I've attached a file called Homework.mp3 that has the recording. Please provide just the page numbers as a comma-delimited list. And please provide the list in ascending order.\"\n",
"task_file_path = \"agent_benchmark_cache/1f975693-876d-457b-a649-393859e79bf3.mp3\"\n",
"context = f\"\"\"\n",
"Question: {question}\n",
"\n",
"This question has an associated file. File is already downloaded at:\n",
"{task_file_path}\n",
"\n",
"Analyze the file content above to answer the question using tools.\n",
"\"\"\"\n",
"full_prompt = f\"\"\"{context}\n",
"\n",
"When answering, provide ONLY the precise answer requested. \n",
"Do not include explanations, steps, reasoning, or additional text.\n",
"Be direct and specific. GAIA benchmark requires exact matching answers.\n",
"For example, if asked \"What is the capital of France?\", respond simply with \"Paris\".\n",
"\"\"\"\n",
"manager_agent.run(context)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "8bf3f8eb",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"╭─────────────────────────────────────────────── New run - Manager ───────────────────────────────────────────────╮\n",
"│ │\n",
"│ Question: The attached Excel file contains the sales of menu items for a local fast-food chain. What were the │\n",
"│ total sales that the chain made from food (not including drinks)? Express your answer in USD with two decimal │\n",
"│ places. │\n",
"│ │\n",
"│ This question has an associated file. File is already downloaded at: │\n",
"│ agent_benchmark_cache/7bd855d8-463d-4ed5-93ca-5fe35145f733.xlsx │\n",
"│ │\n",
"│ Analyze the file content above to answer the question using tools. │\n",
"│ │\n",
"╰─ OpenAIServerModel - Qwen/Qwen3-30B-A3B ────────────────────────────────────────────────────────────────────────╯\n",
"\n"
],
"text/plain": [
"\u001b[38;2;212;183;2m╭─\u001b[0m\u001b[38;2;212;183;2m──────────────────────────────────────────────\u001b[0m\u001b[38;2;212;183;2m \u001b[0m\u001b[1;38;2;212;183;2mNew run - Manager\u001b[0m\u001b[38;2;212;183;2m \u001b[0m\u001b[38;2;212;183;2m──────────────────────────────────────────────\u001b[0m\u001b[38;2;212;183;2m─╮\u001b[0m\n",
"\u001b[38;2;212;183;2m│\u001b[0m \u001b[38;2;212;183;2m│\u001b[0m\n",
"\u001b[38;2;212;183;2m│\u001b[0m \u001b[1mQuestion: The attached Excel file contains the sales of menu items for a local fast-food chain. What were the \u001b[0m \u001b[38;2;212;183;2m│\u001b[0m\n",
"\u001b[38;2;212;183;2m│\u001b[0m \u001b[1mtotal sales that the chain made from food (not including drinks)? Express your answer in USD with two decimal \u001b[0m \u001b[38;2;212;183;2m│\u001b[0m\n",
"\u001b[38;2;212;183;2m│\u001b[0m \u001b[1mplaces.\u001b[0m \u001b[38;2;212;183;2m│\u001b[0m\n",
"\u001b[38;2;212;183;2m│\u001b[0m \u001b[38;2;212;183;2m│\u001b[0m\n",
"\u001b[38;2;212;183;2m│\u001b[0m \u001b[1mThis question has an associated file. File is already downloaded at:\u001b[0m \u001b[38;2;212;183;2m│\u001b[0m\n",
"\u001b[38;2;212;183;2m│\u001b[0m \u001b[1magent_benchmark_cache/7bd855d8-463d-4ed5-93ca-5fe35145f733.xlsx\u001b[0m \u001b[38;2;212;183;2m│\u001b[0m\n",
"\u001b[38;2;212;183;2m│\u001b[0m \u001b[38;2;212;183;2m│\u001b[0m\n",
"\u001b[38;2;212;183;2m│\u001b[0m \u001b[1mAnalyze the file content above to answer the question using tools.\u001b[0m \u001b[38;2;212;183;2m│\u001b[0m\n",
"\u001b[38;2;212;183;2m│\u001b[0m \u001b[38;2;212;183;2m│\u001b[0m\n",
"\u001b[38;2;212;183;2m╰─\u001b[0m\u001b[38;2;212;183;2m OpenAIServerModel - Qwen/Qwen3-30B-A3B \u001b[0m\u001b[38;2;212;183;2m───────────────────────────────────────────────────────────────────────\u001b[0m\u001b[38;2;212;183;2m─╯\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"────────────────────────────────────────────────── Initial plan ───────────────────────────────────────────────────\n",
"Here are the facts I know and the plan of action that I will follow to solve the task:\n",
"```\n",
"\n",
"\n",
"1. Use the analyze_excel_file tool to process the provided Excel file and calculate the total sales from food \n",
"items, excluding drinks. \n",
"2. Format the resulting value in USD with two decimal places. \n",
"3. Use the final_answer tool to submit the computed total sales. \n",
"\n",
"```\n",
"\n"
],
"text/plain": [
"────────────────────────────────────────────────── \u001b[1mInitial plan\u001b[0m ───────────────────────────────────────────────────\n",
"Here are the facts I know and the plan of action that I will follow to solve the task:\n",
"```\n",
"\n",
"\n",
"1. Use the analyze_excel_file tool to process the provided Excel file and calculate the total sales from food \n",
"items, excluding drinks. \n",
"2. Format the resulting value in USD with two decimal places. \n",
"3. Use the final_answer tool to submit the computed total sales. \n",
"\n",
"```\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 1 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
"\n"
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"text/plain": [
"\u001b[38;2;212;183;2m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ \u001b[0m\u001b[1mStep \u001b[0m\u001b[1;36m1\u001b[0m\u001b[38;2;212;183;2m ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\n"
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{
"data": {
"text/html": [
" ─ Executing parsed code: ──────────────────────────────────────────────────────────────────────────────────────── \n",
" file_path = \"agent_benchmark_cache/7bd855d8-463d-4ed5-93ca-5fe35145f733.xlsx\" \n",
" result = analyze_excel_file(file_path=file_path, query=\"What are the total sales from food items, excluding \n",
" drinks?\") \n",
" print(result) \n",
" ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n",
"\n"
],
"text/plain": [
" ─ \u001b[1mExecuting parsed code:\u001b[0m ──────────────────────────────────────────────────────────────────────────────────────── \n",
" \u001b[38;2;248;248;242;48;2;39;40;34mfile_path\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34magent_benchmark_cache/7bd855d8-463d-4ed5-93ca-5fe35145f733.xlsx\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n",
" \u001b[38;2;248;248;242;48;2;39;40;34mresult\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34manalyze_excel_file\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfile_path\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfile_path\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mquery\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mWhat are the total sales from food items, excluding \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n",
" \u001b[38;2;230;219;116;48;2;39;40;34mdrinks?\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n",
" \u001b[38;2;248;248;242;48;2;39;40;34mprint\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mresult\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n",
" ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Execution logs:\n",
"Excel file loaded with 9 rows and 7 columns.\n",
"Columns: Location, Burgers, Hot Dogs, Salads, Fries, Ice Cream, Soda\n",
"\n",
"Summary statistics:\n",
" Burgers Hot Dogs Salads Fries Ice Cream \\\n",
"count 9.000000 9.000000 9.000000 9.000000 9.000000 \n",
"mean 1952.333333 2000.333333 2006.000000 2005.555556 2003.111111 \n",
"std 136.278208 18.668155 15.483863 12.836580 18.884150 \n",
"min 1594.000000 1969.000000 1982.000000 1989.000000 1969.000000 \n",
"25% 1967.000000 1999.000000 2001.000000 1994.000000 1998.000000 \n",
"50% 2007.000000 2008.000000 2012.000000 2005.000000 2005.000000 \n",
"75% 2015.000000 2010.000000 2018.000000 2015.000000 2017.000000 \n",
"max 2019.000000 2022.000000 2022.000000 2023.000000 2021.000000 \n",
"\n",
" Soda \n",
"count 9.000000 \n",
"mean 2116.444444 \n",
"std 319.201782 \n",
"min 1980.000000 \n",
"25% 2009.000000 \n",
"50% 2016.000000 \n",
"75% 2019.000000 \n",
"max 2967.000000 \n",
"\n",
"Out: None\n",
"\n"
],
"text/plain": [
"\u001b[1mExecution logs:\u001b[0m\n",
"Excel file loaded with 9 rows and 7 columns.\n",
"Columns: Location, Burgers, Hot Dogs, Salads, Fries, Ice Cream, Soda\n",
"\n",
"Summary statistics:\n",
" Burgers Hot Dogs Salads Fries Ice Cream \\\n",
"count 9.000000 9.000000 9.000000 9.000000 9.000000 \n",
"mean 1952.333333 2000.333333 2006.000000 2005.555556 2003.111111 \n",
"std 136.278208 18.668155 15.483863 12.836580 18.884150 \n",
"min 1594.000000 1969.000000 1982.000000 1989.000000 1969.000000 \n",
"25% 1967.000000 1999.000000 2001.000000 1994.000000 1998.000000 \n",
"50% 2007.000000 2008.000000 2012.000000 2005.000000 2005.000000 \n",
"75% 2015.000000 2010.000000 2018.000000 2015.000000 2017.000000 \n",
"max 2019.000000 2022.000000 2022.000000 2023.000000 2021.000000 \n",
"\n",
" Soda \n",
"count 9.000000 \n",
"mean 2116.444444 \n",
"std 319.201782 \n",
"min 1980.000000 \n",
"25% 2009.000000 \n",
"50% 2016.000000 \n",
"75% 2019.000000 \n",
"max 2967.000000 \n",
"\n",
"Out: None\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"[Step 1: Duration 11.52 seconds| Input tokens: 2,100 | Output tokens: 492]\n",
"\n"
],
"text/plain": [
"\u001b[2m[Step 1: Duration 11.52 seconds| Input tokens: 2,100 | Output tokens: 492]\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 2 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
"\n"
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"text/plain": [
"\u001b[38;2;212;183;2m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ \u001b[0m\u001b[1mStep \u001b[0m\u001b[1;36m2\u001b[0m\u001b[38;2;212;183;2m ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\n"
]
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"metadata": {},
"output_type": "display_data"
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{
"data": {
"text/html": [
" ─ Executing parsed code: ──────────────────────────────────────────────────────────────────────────────────────── \n",
" # Summing the mean values of food columns (Burgers, Hot Dogs, Salads, Fries, Ice Cream) \n",
" total_sales = 1952.33 + 2000.33 + 2006.00 + 2005.56 + 2003.11 \n",
" formatted_sales = f\"${total_sales:.2f}\" \n",
" final_answer(formatted_sales) \n",
" ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n",
"\n"
],
"text/plain": [
" ─ \u001b[1mExecuting parsed code:\u001b[0m ──────────────────────────────────────────────────────────────────────────────────────── \n",
" \u001b[38;2;149;144;119;48;2;39;40;34m# Summing the mean values of food columns (Burgers, Hot Dogs, Salads, Fries, Ice Cream) \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n",
" \u001b[38;2;248;248;242;48;2;39;40;34mtotal_sales\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m1952.33\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m+\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m2000.33\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m+\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m2006.00\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m+\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m2005.56\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m+\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m2003.11\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n",
" \u001b[38;2;248;248;242;48;2;39;40;34mformatted_sales\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mf\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m$\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m{\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtotal_sales\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m:\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m.2f\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m}\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n",
" \u001b[38;2;248;248;242;48;2;39;40;34mfinal_answer\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mformatted_sales\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n",
" ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Out - Final answer: $9967.33\n",
"\n"
],
"text/plain": [
"\u001b[1;38;2;212;183;2mOut - Final answer: $9967.33\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Feedback: \n",
"\n",
"The assistant's answer of $9967.33 is incorrect. Here's the reasoning:\n",
"\n",
"1. **Data Structure Analysis**: The Excel file contains 9 rows (locations) with columns for food items (Burgers, Hot Dogs, Salads, Fries, Ice Cream) and drinks (Soda). The summary statistics show **means** for each column, not totals. For example, the mean for Burgers is 1952.33, implying the total for Burgers across all 9 locations would be 1952.33 × 9 = 17,570.97.\n",
"\n",
"2. **Incorrect Interpretation**: The assistant's answer ($9967.33) matches the **sum of the means** of the food categories (1952.33 + 2000.33 + 2006 + 2005.56 + 2003.11 = 9967.33). However, this represents the **average total food sales per location**, not the **total across all locations**. The correct total should be the sum of all individual food sales across all rows, which would be **9 × 9967.33 = 89,705.97**.\n",
"\n",
"3. **Error in Tool Usage**: The `analyze_excel_file` tool was queried to calculate \"total sales from food items, excluding drinks,\" but the result returned appears to be the sum of column means rather than the actual total of all food sales. This suggests a misinterpretation of the data structure or a flaw in the tool's processing.\n",
"\n",
"**Final Decision**: FAIL \n",
"The answer does not correctly compute the total sales across all locations. The correct total should be **$89,705.97**, not $9967.33.\n"
]
},
{
"data": {
"text/html": [
"Check check_reasoning failed with error: \n",
"\n",
"The assistant's answer of $9967.33 is incorrect. Here's the reasoning:\n",
"\n",
"1. **Data Structure Analysis**: The Excel file contains 9 rows (locations) with columns for food items (Burgers, \n",
"Hot Dogs, Salads, Fries, Ice Cream) and drinks (Soda). The summary statistics show **means** for each column, not \n",
"totals. For example, the mean for Burgers is 1952.33, implying the total for Burgers across all 9 locations would \n",
"be 1952.33 × 9 = 17,570.97.\n",
"\n",
"2. **Incorrect Interpretation**: The assistant's answer ($9967.33) matches the **sum of the means** of the food \n",
"categories (1952.33 + 2000.33 + 2006 + 2005.56 + 2003.11 = 9967.33). However, this represents the **average total \n",
"food sales per location**, not the **total across all locations**. The correct total should be the sum of all \n",
"individual food sales across all rows, which would be **9 × 9967.33 = 89,705.97**.\n",
"\n",
"3. **Error in Tool Usage**: The `analyze_excel_file` tool was queried to calculate \"total sales from food items, \n",
"excluding drinks,\" but the result returned appears to be the sum of column means rather than the actual total of \n",
"all food sales. This suggests a misinterpretation of the data structure or a flaw in the tool's processing.\n",
"\n",
"**Final Decision**: FAIL \n",
"The answer does not correctly compute the total sales across all locations. The correct total should be \n",
"**$89,705.97**, not $9967.33.\n",
"\n"
],
"text/plain": [
"\u001b[1;31mCheck check_reasoning failed with error: \u001b[0m\n",
"\n",
"\u001b[1;31mThe assistant's answer of $\u001b[0m\u001b[1;36m9967.33\u001b[0m\u001b[1;31m is incorrect. Here's the reasoning:\u001b[0m\n",
"\n",
"\u001b[1;36m1\u001b[0m\u001b[1;31m. **Data Structure Analysis**: The Excel file contains \u001b[0m\u001b[1;36m9\u001b[0m\u001b[1;31m rows \u001b[0m\u001b[1;31m(\u001b[0m\u001b[1;31mlocations\u001b[0m\u001b[1;31m)\u001b[0m\u001b[1;31m with columns for food items \u001b[0m\u001b[1;31m(\u001b[0m\u001b[1;31mBurgers, \u001b[0m\n",
"\u001b[1;31mHot Dogs, Salads, Fries, Ice Cream\u001b[0m\u001b[1;31m)\u001b[0m\u001b[1;31m and drinks \u001b[0m\u001b[1;31m(\u001b[0m\u001b[1;31mSoda\u001b[0m\u001b[1;31m)\u001b[0m\u001b[1;31m. The summary statistics show **means** for each column, not \u001b[0m\n",
"\u001b[1;31mtotals. For example, the mean for Burgers is \u001b[0m\u001b[1;36m1952.33\u001b[0m\u001b[1;31m, implying the total for Burgers across all \u001b[0m\u001b[1;36m9\u001b[0m\u001b[1;31m locations would \u001b[0m\n",
"\u001b[1;31mbe \u001b[0m\u001b[1;36m1952.33\u001b[0m\u001b[1;31m × \u001b[0m\u001b[1;36m9\u001b[0m\u001b[1;31m = \u001b[0m\u001b[1;36m17\u001b[0m\u001b[1;31m,\u001b[0m\u001b[1;36m570.97\u001b[0m\u001b[1;31m.\u001b[0m\n",
"\n",
"\u001b[1;36m2\u001b[0m\u001b[1;31m. **Incorrect Interpretation**: The assistant's answer \u001b[0m\u001b[1;31m(\u001b[0m\u001b[1;31m$\u001b[0m\u001b[1;36m9967.33\u001b[0m\u001b[1;31m)\u001b[0m\u001b[1;31m matches the **sum of the means** of the food \u001b[0m\n",
"\u001b[1;31mcategories \u001b[0m\u001b[1;31m(\u001b[0m\u001b[1;36m1952.33\u001b[0m\u001b[1;31m + \u001b[0m\u001b[1;36m2000.33\u001b[0m\u001b[1;31m + \u001b[0m\u001b[1;36m2006\u001b[0m\u001b[1;31m + \u001b[0m\u001b[1;36m2005.56\u001b[0m\u001b[1;31m + \u001b[0m\u001b[1;36m2003.11\u001b[0m\u001b[1;31m = \u001b[0m\u001b[1;36m9967.33\u001b[0m\u001b[1;31m)\u001b[0m\u001b[1;31m. However, this represents the **average total \u001b[0m\n",
"\u001b[1;31mfood sales per location**, not the **total across all locations**. The correct total should be the sum of all \u001b[0m\n",
"\u001b[1;31mindividual food sales across all rows, which would be **\u001b[0m\u001b[1;36m9\u001b[0m\u001b[1;31m × \u001b[0m\u001b[1;36m9967.33\u001b[0m\u001b[1;31m = \u001b[0m\u001b[1;36m89\u001b[0m\u001b[1;31m,\u001b[0m\u001b[1;36m705.97\u001b[0m\u001b[1;31m**.\u001b[0m\n",
"\n",
"\u001b[1;36m3\u001b[0m\u001b[1;31m. **Error in Tool Usage**: The `analyze_excel_file` tool was queried to calculate \u001b[0m\u001b[32m\"total sales from food items, \u001b[0m\n",
"\u001b[32mexcluding drinks,\"\u001b[0m\u001b[1;31m but the result returned appears to be the sum of column means rather than the actual total of \u001b[0m\n",
"\u001b[1;31mall food sales. This suggests a misinterpretation of the data structure or a flaw in the tool's processing.\u001b[0m\n",
"\n",
"\u001b[1;31m**Final Decision**: FAIL \u001b[0m\n",
"\u001b[1;31mThe answer does not correctly compute the total sales across all locations. The correct total should be \u001b[0m\n",
"\u001b[1;31m**$\u001b[0m\u001b[1;36m89\u001b[0m\u001b[1;31m,\u001b[0m\u001b[1;36m705.97\u001b[0m\u001b[1;31m**, not $\u001b[0m\u001b[1;36m9967.33\u001b[0m\u001b[1;31m.\u001b[0m\n"
]
},
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{
"data": {
"text/html": [
"[Step 2: Duration 59.27 seconds| Input tokens: 5,144 | Output tokens: 1,597]\n",
"\n"
],
"text/plain": [
"\u001b[2m[Step 2: Duration 59.27 seconds| Input tokens: 5,144 | Output tokens: 1,597]\u001b[0m\n"
]
},
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"output_type": "display_data"
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{
"data": {
"text/html": [
"━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 3 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
"\n"
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"\u001b[38;2;212;183;2m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ \u001b[0m\u001b[1mStep \u001b[0m\u001b[1;36m3\u001b[0m\u001b[38;2;212;183;2m ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\n"
]
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{
"data": {
"text/html": [
" ─ Executing parsed code: ──────────────────────────────────────────────────────────────────────────────────────── \n",
" # Extract mean values from the summary statistics \n",
" burgers_mean = 1952.333333 \n",
" hot_dogs_mean = 2000.333333 \n",
" salads_mean = 2006.000000 \n",
" fries_mean = 2005.555556 \n",
" ice_cream_mean = 2003.111111 \n",
" \n",
" # Calculate total food sales across all locations \n",
" total_food_sales = (burgers_mean + hot_dogs_mean + salads_mean + fries_mean + ice_cream_mean) * 9 \n",
" formatted_sales = f\"${total_food_sales:.2f}\" \n",
" final_answer(formatted_sales) \n",
" ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n",
"\n"
],
"text/plain": [
" ─ \u001b[1mExecuting parsed code:\u001b[0m ──────────────────────────────────────────────────────────────────────────────────────── \n",
" \u001b[38;2;149;144;119;48;2;39;40;34m# Extract mean values from the summary statistics \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n",
" \u001b[38;2;248;248;242;48;2;39;40;34mburgers_mean\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m1952.333333\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n",
" \u001b[38;2;248;248;242;48;2;39;40;34mhot_dogs_mean\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m2000.333333\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n",
" \u001b[38;2;248;248;242;48;2;39;40;34msalads_mean\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m2006.000000\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n",
" \u001b[38;2;248;248;242;48;2;39;40;34mfries_mean\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m2005.555556\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n",
" \u001b[38;2;248;248;242;48;2;39;40;34mice_cream_mean\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m2003.111111\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n",
" \u001b[48;2;39;40;34m \u001b[0m \n",
" \u001b[38;2;149;144;119;48;2;39;40;34m# Calculate total food sales across all locations \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n",
" \u001b[38;2;248;248;242;48;2;39;40;34mtotal_food_sales\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mburgers_mean\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m+\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mhot_dogs_mean\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m+\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msalads_mean\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m+\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfries_mean\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m+\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mice_cream_mean\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m*\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m9\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n",
" \u001b[38;2;248;248;242;48;2;39;40;34mformatted_sales\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mf\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m$\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m{\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtotal_food_sales\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m:\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m.2f\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m}\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n",
" \u001b[38;2;248;248;242;48;2;39;40;34mfinal_answer\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mformatted_sales\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n",
" ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Out - Final answer: $89706.00\n",
"\n"
],
"text/plain": [
"\u001b[1;38;2;212;183;2mOut - Final answer: $89706.00\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Feedback: \n",
"\n",
"The answer provided, **$89,706.00**, is correct. Here's the breakdown:\n",
"\n",
"1. **Data Structure**: The Excel file contains 9 rows (locations) with columns for food items (Burgers, Hot Dogs, Salads, Fries, Ice Cream) and drinks (Soda). Summary statistics show **mean values** for each column, not totals. \n",
" - Example: Burgers mean = 1952.33, implying total sales for Burgers = 1952.33 × 9 = **17,570.97**.\n",
"\n",
"2. **Correct Calculation**: \n",
" - Sum of food column means: \n",
" $1952.33 (Burgers) + 2000.33 (Hot Dogs) + 2006.00 (Salads) + 2005.56 (Fries) + 2003.11 (Ice Cream) = **9,967.33**. \n",
" - Multiply by 9 locations: \n",
" $9,967.33 × 9 = **89,705.97**, rounded to **$89,706.00**.\n",
"\n",
"3. **Error in Initial Steps**: The assistant initially summed the means directly (incorrectly), but the final answer correctly accounts for the 9 locations by multiplying the sum of means by 9. The final answer matches the expected result.\n",
"\n",
"**Reasons for PASS**: \n",
"- The answer correctly computes the total sales by multiplying the sum of food column means by the number of locations (9). \n",
"- The result ($89,706.00) aligns with the mathematical derivation from the provided summary statistics. \n",
"- The final answer adheres to the required format (USD with two decimal places).\n",
"\n",
"**Final Decision**: PASS\n",
"Feedback: \n",
"\n",
"The final answer provided is **$89706.00**. \n",
"\n",
"**Format Evaluation**: \n",
"- The answer includes the USD currency symbol (`$`), which aligns with the task's requirement to express the answer in USD. \n",
"- The value is formatted with two decimal places (`00`), as specified. \n",
"- The structure adheres to the instruction: \"Express your answer in USD with two decimal places.\" \n",
"\n",
"**Final Decision**: PASS\n"
]
},
{
"data": {
"text/html": [
"[Step 3: Duration 70.01 seconds| Input tokens: 8,978 | Output tokens: 4,754]\n",
"\n"
],
"text/plain": [
"\u001b[2m[Step 3: Duration 70.01 seconds| Input tokens: 8,978 | Output tokens: 4,754]\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
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{
"data": {
"text/plain": [
"'$89706.00'"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"question = \"The attached Excel file contains the sales of menu items for a local fast-food chain. What were the total sales that the chain made from food (not including drinks)? Express your answer in USD with two decimal places.\"\n",
"task_file_path = \"agent_benchmark_cache/7bd855d8-463d-4ed5-93ca-5fe35145f733.xlsx\"\n",
"context = f\"\"\"\n",
"Question: {question}\n",
"\n",
"This question has an associated file. File is already downloaded at:\n",
"{task_file_path}\n",
"\n",
"Analyze the file content above to answer the question using tools.\n",
"\"\"\"\n",
"full_prompt = f\"\"\"{context}\n",
"\n",
"When answering, provide ONLY the precise answer requested. \n",
"Do not include explanations, steps, reasoning, or additional text.\n",
"Be direct and specific. GAIA benchmark requires exact matching answers.\n",
"For example, if asked \"What is the capital of France?\", respond simply with \"Paris\".\n",
"\"\"\"\n",
"manager_agent.run(context)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "51a53c63",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"╭─────────────────────────────────────────────── New run - Manager ───────────────────────────────────────────────╮\n",
"│ │\n",
"│ How many r's are in the word Strawberry? │\n",
"│ │\n",
"╰─ OpenAIServerModel - Qwen/Qwen3-30B-A3B ────────────────────────────────────────────────────────────────────────╯\n",
"\n"
],
"text/plain": [
"\u001b[38;2;212;183;2m╭─\u001b[0m\u001b[38;2;212;183;2m──────────────────────────────────────────────\u001b[0m\u001b[38;2;212;183;2m \u001b[0m\u001b[1;38;2;212;183;2mNew run - Manager\u001b[0m\u001b[38;2;212;183;2m \u001b[0m\u001b[38;2;212;183;2m──────────────────────────────────────────────\u001b[0m\u001b[38;2;212;183;2m─╮\u001b[0m\n",
"\u001b[38;2;212;183;2m│\u001b[0m \u001b[38;2;212;183;2m│\u001b[0m\n",
"\u001b[38;2;212;183;2m│\u001b[0m \u001b[1mHow many r's are in the word Strawberry?\u001b[0m \u001b[38;2;212;183;2m│\u001b[0m\n",
"\u001b[38;2;212;183;2m│\u001b[0m \u001b[38;2;212;183;2m│\u001b[0m\n",
"\u001b[38;2;212;183;2m╰─\u001b[0m\u001b[38;2;212;183;2m OpenAIServerModel - Qwen/Qwen3-30B-A3B \u001b[0m\u001b[38;2;212;183;2m───────────────────────────────────────────────────────────────────────\u001b[0m\u001b[38;2;212;183;2m─╯\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
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{
"data": {
"text/html": [
"────────────────────────────────────────────────── Initial plan ───────────────────────────────────────────────────\n",
"Here are the facts I know and the plan of action that I will follow to solve the task:\n",
"```\n",
"\n",
"\n",
"1. Count the number of times the letter 'r' appears in the word \"Strawberry\".\n",
"2. Use the final_answer tool to submit the count. \n",
"\n",
"\n",
"```\n",
"\n"
],
"text/plain": [
"────────────────────────────────────────────────── \u001b[1mInitial plan\u001b[0m ───────────────────────────────────────────────────\n",
"Here are the facts I know and the plan of action that I will follow to solve the task:\n",
"```\n",
"\n",
"\n",
"1. Count the number of times the letter 'r' appears in the word \"Strawberry\".\n",
"2. Use the final_answer tool to submit the count. \n",
"\n",
"\n",
"```\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 1 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
"\n"
],
"text/plain": [
"\u001b[38;2;212;183;2m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ \u001b[0m\u001b[1mStep \u001b[0m\u001b[1;36m1\u001b[0m\u001b[38;2;212;183;2m ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\n"
]
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"output_type": "display_data"
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{
"data": {
"text/html": [
" ─ Executing parsed code: ──────────────────────────────────────────────────────────────────────────────────────── \n",
" word = \"Strawberry\" \n",
" count = word.lower().count('r') \n",
" print(count) \n",
" ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n",
"\n"
],
"text/plain": [
" ─ \u001b[1mExecuting parsed code:\u001b[0m ──────────────────────────────────────────────────────────────────────────────────────── \n",
" \u001b[38;2;248;248;242;48;2;39;40;34mword\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mStrawberry\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n",
" \u001b[38;2;248;248;242;48;2;39;40;34mcount\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mword\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mlower\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcount\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mr\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n",
" \u001b[38;2;248;248;242;48;2;39;40;34mprint\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcount\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n",
" ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Execution logs:\n",
"3\n",
"\n",
"Out: None\n",
"\n"
],
"text/plain": [
"\u001b[1mExecution logs:\u001b[0m\n",
"3\n",
"\n",
"Out: None\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"[Step 1: Duration 15.68 seconds| Input tokens: 1,978 | Output tokens: 316]\n",
"\n"
],
"text/plain": [
"\u001b[2m[Step 1: Duration 15.68 seconds| Input tokens: 1,978 | Output tokens: 316]\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 2 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
"\n"
],
"text/plain": [
"\u001b[38;2;212;183;2m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ \u001b[0m\u001b[1mStep \u001b[0m\u001b[1;36m2\u001b[0m\u001b[38;2;212;183;2m ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Error in code parsing:\n",
"Your code snippet is invalid, because the regex pattern ```(?:py|python)?\\s*\\n(.*?)\\n``` was not found in it.\n",
" Here is your code snippet:\n",
"\n",
"\n",
"The word \"Strawberry\" contains 3 'r's.\n",
"\n",
"final_answer(answer=3)\n",
" It seems like you're trying to return the final answer, you can do it as follows:\n",
" Code:\n",
" ```py\n",
" final_answer(\"YOUR FINAL ANSWER HERE\")\n",
" ```<end_code>\n",
"Make sure to provide correct code blobs.\n",
"\n"
],
"text/plain": [
"\u001b[1;31mError in code parsing:\u001b[0m\n",
"\u001b[1;31mYour code snippet is invalid, because the regex pattern ```\u001b[0m\u001b[1;31m(\u001b[0m\u001b[1;31m?:py|python\u001b[0m\u001b[1;31m)\u001b[0m\u001b[1;31m?\\s*\\\u001b[0m\u001b[1;35mn\u001b[0m\u001b[1;31m(\u001b[0m\u001b[1;31m.*?\u001b[0m\u001b[1;31m)\u001b[0m\u001b[1;31m\\n``` was not found in it.\u001b[0m\n",
"\u001b[1;31m Here is your code snippet:\u001b[0m\n",
"\n",
"\n",
"\u001b[1;31mThe word \u001b[0m\u001b[32m\"Strawberry\"\u001b[0m\u001b[1;31m contains \u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;31m \u001b[0m\u001b[32m'r'\u001b[0m\u001b[1;31ms.\u001b[0m\n",
"\n",
"\u001b[1;35mfinal_answer\u001b[0m\u001b[1;31m(\u001b[0m\u001b[1;33manswer\u001b[0m\u001b[1;31m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;31m)\u001b[0m\n",
"\u001b[1;31m It seems like you're trying to return the final answer, you can do it as follows:\u001b[0m\n",
"\u001b[1;31m Code:\u001b[0m\n",
"\u001b[1;31m ```py\u001b[0m\n",
"\u001b[1;31m \u001b[0m\u001b[1;35mfinal_answer\u001b[0m\u001b[1;31m(\u001b[0m\u001b[32m\"YOUR FINAL ANSWER HERE\"\u001b[0m\u001b[1;31m)\u001b[0m\n",
"\u001b[1;31m ```\u001b[0m\u001b[1;31m<\u001b[0m\u001b[1;95mend_code\u001b[0m\u001b[1;31m>\u001b[0m\n",
"\u001b[1;31mMake sure to provide correct code blobs.\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"[Step 2: Duration 8.36 seconds| Input tokens: 4,093 | Output tokens: 999]\n",
"\n"
],
"text/plain": [
"\u001b[2m[Step 2: Duration 8.36 seconds| Input tokens: 4,093 | Output tokens: 999]\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 3 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
"\n"
],
"text/plain": [
"\u001b[38;2;212;183;2m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ \u001b[0m\u001b[1mStep \u001b[0m\u001b[1;36m3\u001b[0m\u001b[38;2;212;183;2m ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" ─ Executing parsed code: ──────────────────────────────────────────────────────────────────────────────────────── \n",
" word = \"Strawberry\" \n",
" count = word.lower().count('r') \n",
" print(count) \n",
" ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n",
"\n"
],
"text/plain": [
" ─ \u001b[1mExecuting parsed code:\u001b[0m ──────────────────────────────────────────────────────────────────────────────────────── \n",
" \u001b[38;2;248;248;242;48;2;39;40;34mword\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mStrawberry\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n",
" \u001b[38;2;248;248;242;48;2;39;40;34mcount\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mword\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mlower\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcount\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mr\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n",
" \u001b[38;2;248;248;242;48;2;39;40;34mprint\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcount\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n",
" ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Execution logs:\n",
"3\n",
"\n",
"Out: None\n",
"\n"
],
"text/plain": [
"\u001b[1mExecution logs:\u001b[0m\n",
"3\n",
"\n",
"Out: None\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"[Step 3: Duration 6.81 seconds| Input tokens: 6,381 | Output tokens: 1,551]\n",
"\n"
],
"text/plain": [
"\u001b[2m[Step 3: Duration 6.81 seconds| Input tokens: 6,381 | Output tokens: 1,551]\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 4 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
"\n"
],
"text/plain": [
"\u001b[38;2;212;183;2m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ \u001b[0m\u001b[1mStep \u001b[0m\u001b[1;36m4\u001b[0m\u001b[38;2;212;183;2m ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" ─ Executing parsed code: ──────────────────────────────────────────────────────────────────────────────────────── \n",
" word = \"Strawberry\" \n",
" count = word.lower().count('r') \n",
" print(count) \n",
" ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n",
"\n"
],
"text/plain": [
" ─ \u001b[1mExecuting parsed code:\u001b[0m ──────────────────────────────────────────────────────────────────────────────────────── \n",
" \u001b[38;2;248;248;242;48;2;39;40;34mword\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mStrawberry\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n",
" \u001b[38;2;248;248;242;48;2;39;40;34mcount\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mword\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mlower\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcount\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mr\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n",
" \u001b[38;2;248;248;242;48;2;39;40;34mprint\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcount\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n",
" ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Execution logs:\n",
"3\n",
"\n",
"Out: None\n",
"\n"
],
"text/plain": [
"\u001b[1mExecution logs:\u001b[0m\n",
"3\n",
"\n",
"Out: None\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"[Step 4: Duration 7.95 seconds| Input tokens: 8,821 | Output tokens: 2,148]\n",
"\n"
],
"text/plain": [
"\u001b[2m[Step 4: Duration 7.95 seconds| Input tokens: 8,821 | Output tokens: 2,148]\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
"\n"
],
"text/plain": [
"\u001b[38;2;212;183;2m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ \u001b[0m\u001b[1mStep \u001b[0m\u001b[1;36m5\u001b[0m\u001b[38;2;212;183;2m ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" ─ Executing parsed code: ──────────────────────────────────────────────────────────────────────────────────────── \n",
" final_answer(answer=3) \n",
" ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n",
"\n"
],
"text/plain": [
" ─ \u001b[1mExecuting parsed code:\u001b[0m ──────────────────────────────────────────────────────────────────────────────────────── \n",
" \u001b[38;2;248;248;242;48;2;39;40;34mfinal_answer\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34manswer\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m3\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n",
" ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Out - Final answer: 3\n",
"\n"
],
"text/plain": [
"\u001b[1;38;2;212;183;2mOut - Final answer: 3\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Feedback: \n",
"\n",
"The answer is correct. Here's the breakdown:\n",
"\n",
"**Reasons why the answer is correct:**\n",
"1. **Manual Counting:** The word \"Strawberry\" is spelled S-T-R-A-W-B-E-R-R-Y. Breaking it down:\n",
" - Letters: S, T, **R**, A, W, B, E, **R**, **R**, Y.\n",
" - The letter 'r' (case-insensitive) appears at positions 3, 8, and 9 (3 total).\n",
"\n",
"2. **Code Validation:** The Python code `word.lower().count('r')` converts the word to lowercase (\"strawberry\") and counts 'r's, which are at positions 2, 7, and 8 (3 total). The code output confirms this.\n",
"\n",
"3. **Case Sensitivity:** The question does not specify case sensitivity. The code accounts for both uppercase and lowercase 'r's by converting the word to lowercase first, ensuring accuracy.\n",
"\n",
"4. **Error Resolution:** The initial formatting errors in the code blocks were unrelated to the correctness of the count. Once fixed, the code consistently returned 3.\n",
"\n",
"**Final Decision:** PASS\n",
"Feedback: \n",
"\n",
"The final answer provided is \"3\", which is a single number in Arabic numerals as required. It does not include commas, units, or unnecessary formatting. The answer directly addresses the task of counting the number of 'r's in \"Strawberry\", which is correctly determined to be 3. \n",
"\n",
"**Reasons for PASS:** \n",
"- The answer is a number (3) without commas or units. \n",
"- It adheres to the instruction to use Arabic numerals. \n",
"- It is concise and directly answers the question. \n",
"\n",
"**Final Decision:** PASS\n"
]
},
{
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"3"
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"metadata": {},
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}
],
"source": [
"manager_agent.run(\"How many r's are in the word Strawberry?\")\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "d45cd981",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" \r"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/make/projects/learning/Final_Assignment/.venv/lib/python3.11/site-packages/imageio/plugins/spe.py:507: UserWarning: The file header of /var/folders/tq/1fyfc__n16j9dmfwvshf9cmm0000gn/T/tmpu6xzfro0/video.mp4 claims there are -166920192 frames, but there are actually 0 frames.\n",
" warnings.warn(\n"
]
}
],
"source": [
"# from tools.tools import youtube_frames_to_images\n",
"# results = youtube_frames_to_images('https://youtube.com/watch?v=L1vXCYZAYYM')"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "9d806dfe",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" \r"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/make/projects/learning/Final_Assignment/.venv/lib/python3.11/site-packages/whisper/transcribe.py:126: UserWarning: FP16 is not supported on CPU; using FP32 instead\n",
" warnings.warn(\"FP16 is not supported on CPU; using FP32 instead\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" Wow, this coffee's great. I was just thinking that. Yeah, is that cinnamon? It's a... it's a checkery. Checkery. Teal? Isn't that hot? Extremely.\n"
]
}
],
"source": [
"from tools.tools import youtube_transcribe\n",
"results = youtube_transcribe('https://www.youtube.com/watch?v=1htKBjuUWec')\n",
"print(results)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b87ee5e6",
"metadata": {},
"outputs": [],
"source": [
"from PIL import Image\n",
"from os import walk, path\n",
"\n",
"# Path to image file\n",
"picture_path = './images'\n",
"\n",
"# List to hold images\n",
"images = []\n",
"\n",
"# Walk through the directory and get all .png files paths\n",
"for root, dirs, files in walk(picture_path):\n",
" for file in files:\n",
" if file.endswith('.png'):\n",
" # Construct full path of each png file\n",
" picture_file = path.join(root, file)\n",
" \n",
" try:\n",
" # Open image from path\n",
" img = Image.open(picture_file)\n",
" \n",
" # Append it to the list of images after converting it to RGB format\n",
" images.append(img.convert('RGB'))\n",
" except IOError as e:\n",
" print(f'Error opening {picture_file}: {e}')\n",
" continue\n",
"\n",
"\n",
"from tools.tools import use_vision_model\n",
"\n",
"query = \"Review these pictures. What is the highest number of bird species to appear in the same photo of these photos?\"\n",
"\n",
"tool_input = {\n",
" \"question\": query,\n",
" \"images\": images\n",
"}\n",
"\n",
"use_vision_model(**tool_input)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5f294f90",
"metadata": {},
"outputs": [],
"source": [
"from tools.tools import download_file_from_url, transcribe_audio\n",
"\n",
"task_file_path = \"1f975693-876d-457b-a649-393859e79bf3\"\n",
"filename = \"1f975693-876d-457b-a649-393859e79bf3.mp3\"\n",
"url = f'{DEFAULT_API_URL}/files/{task_file_path}'\n",
"\n",
"tool_input = {\"url\": url}\n",
"\n",
"filename = download_file_from_url(**tool_input).strip(\"File downloaded to \").strip(\". You can now process this file.'\")\n",
"\n",
"tool_input = {\"audio_file_path\": filename}\n",
"\n",
"transcribe_audio(**tool_input)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b34b7bf2",
"metadata": {},
"outputs": [],
"source": [
"from tools.tools import read_file, download_file_from_url\n",
"\n",
"DEFAULT_API_URL = \"https://agents-course-unit4-scoring.hf.space\"\n",
"taskid = \"f918266a-b3e0-4914-865d-4faa564f1aef\"\n",
"filename = f\"{taskid}.py\"\n",
"url = f'{DEFAULT_API_URL}/files/{taskid}'\n",
"\n",
"tool_input = {\"url\": url, \"filename\": filename}\n",
"\n",
"filepath = download_file_from_url(**tool_input).strip(\"File downloaded to \").strip(\". You can now process this file.'\")\n",
"\n",
"\n",
"tool_input = {\"filepath\": filepath}\n",
"\n",
"output = read_file(**tool_input)\n",
"\n",
"print(output)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
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"language_info": {
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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