{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "9BLqOGQPkxqb" }, "source": [ "# Chapter 11 - Advanced Topics and Cutting-Edge Research\n" ] }, { "cell_type": "markdown", "source": [ "These are the libraries we will use for the code examples of Chapter 11." ], "metadata": { "id": "ryoWa3OMSYjF" } }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "hNOpZd8ik3pm", "outputId": "3a125fad-5f49-4a63-ec3d-54a96c1eda54" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/155.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m155.7/155.7 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/43.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.7/43.7 kB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.1/4.1 MB\u001b[0m \u001b[31m81.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m566.4/566.4 kB\u001b[0m \u001b[31m54.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "transformers 5.0.0 requires huggingface-hub<2.0,>=1.3.0, but you have huggingface-hub 0.36.2 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.4/10.4 MB\u001b[0m \u001b[31m129.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m612.9/612.9 kB\u001b[0m \u001b[31m55.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "smolagents 1.24.0 requires huggingface-hub<1.0.0,>=0.31.2, but you have huggingface-hub 1.6.0 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0m" ] } ], "source": [ "!pip install -qU smolagents ddgs\n", "!pip install -qU transformers==5.2.0\n", "!pip install -qU huggingface_hub>=1.0.0" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "bei21V4BDGJX" }, "outputs": [], "source": [ "# Very important to be logged in!\n", "from huggingface_hub import login\n", "login()\n" ] }, { "cell_type": "markdown", "metadata": { "id": "_tTqzNc5DE7g" }, "source": [ "## Agentic Vision Language Models\n", "### Introduction to Smolagents\n", "\n", "Let’s build a tiny agent and watch what happens inside the loop. Smolagents comes with two agent classes, both based on a common MultiStepAgent that contains the ReAct loop.\n", "\n", "CodeAgent writes and executes Python to perform actions\n", "\n", "ToolCallingAgent outputs JSON to call tools instead, which is the more traditional approach you will find in other frameworks.\n", "\n", "Both need tools: smolagents ships with WebSearchTool, PythonInterpreterTool, and a Tool base class for building your own.\n", "\n", "Let’s write a trivial tool and run it. Pay attention to the description field: this is what the LLM reads to decide when and how to call your tool." ] }, { "cell_type": "code", "source": [ "from smolagents import Tool, CodeAgent, InferenceClientModel, DuckDuckGoSearchTool\n", "import torch\n", "\n", "class AdditionTool(Tool):\n", " name = \"addition_tool\"\n", " description = \"Adds two numbers together\"\n", " inputs = {\n", " \"first_number\": {\n", " \"type\": \"integer\",\n", " \"description\": \"first number to add\"\n", " },\n", " \"second_number\": {\n", " \"type\": \"integer\",\n", " \"description\": \"second number to add\"\n", " }\n", " }\n", " output_type = \"integer\"\n", "\n", " def forward(self, first_number, second_number):\n", " return first_number + second_number\n", "\n", "model = InferenceClientModel()\n", "agent = CodeAgent(tools=[AdditionTool()], model=model)\n", "result = agent.run(\"What is 41 plus 1?\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 215 }, "id": "oUjhywMVVFI4", "outputId": "4ac8c96c-2571-49af-acae-71f7889e02fe" }, "execution_count": 3, "outputs": [ { "output_type": "display_data", "data": { "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\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[1mWhat is 41 plus 1?\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 InferenceClientModel - Qwen/Qwen3-Next-80B-A3B-Thinking \u001b[0m\u001b[38;2;212;183;2m──────────────────────────────────────────────────────\u001b[0m\u001b[38;2;212;183;2m─╯\u001b[0m\n" ], "text/html": [ "
╭──────────────────────────────────────────────────── New run ────────────────────────────────────────────────────╮\n",
              "│                                                                                                                 │\n",
              "│ What is 41 plus 1?                                                                                              │\n",
              "│                                                                                                                 │\n",
              "╰─ InferenceClientModel - Qwen/Qwen3-Next-80B-A3B-Thinking ───────────────────────────────────────────────────────╯\n",
              "
\n" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "\u001b[38;2;212;183;2m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ \u001b[0m\u001b[1;37mStep 1\u001b[0m\u001b[38;2;212;183;2m ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\n" ], "text/html": [ "
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\n" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ " ─ \u001b[1mExecuting parsed code:\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;174;129;255;48;2;39;40;34m41\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;34m1\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;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" ], "text/html": [ "
 ─ Executing parsed code: ──────────────────────────────────────────────────────────────────────────────────────── \n",
              "  result = 41 + 1                                                                                                  \n",
              "  final_answer(result)                                                                                             \n",
              " ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n",
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            "text/plain": [
              "\u001b[1;38;2;212;183;2mFinal answer: 42\u001b[0m\n"
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Final answer: 42\n",
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              "\u001b[2m[Step 1: Duration 3.39 seconds| Input tokens: 2,081 | Output tokens: 670]\u001b[0m\n"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "The output reveals the full lifecycle of an agent run:"
      ],
      "metadata": {
        "id": "QYzGJTgLVBZi"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R_V7z0N4klta",
        "outputId": "3c9e7555-edea-489c-e99e-1318276695a2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TaskStep: TaskStep(task='What is 41 plus 1?', task_images=None)\n",
            "ActionStep: ActionStep(step_number=1, timing=Timing(start_time=1773324476.1104407, end_time=1773324479.499726, duration=3.3892853260040283), model_input_messages=[ChatMessage(role=, content=[{'type': 'text', 'text': 'You are an expert assistant who can solve any task using code blobs. You will be given a task to solve as best you can.\\nTo do so, you have been given access to a list of tools: these tools are basically Python functions which you can call with code.\\nTo solve the task, you must plan forward to proceed in a series of steps, in a cycle of Thought, Code, and Observation sequences.\\n\\nAt each step, in the \\'Thought:\\' sequence, you should first explain your reasoning towards solving the task and the tools that you want to use.\\nThen in the Code sequence you should write the code in simple Python. The code sequence must be opened with \\'\\', and closed with \\'\\'.\\nDuring each intermediate step, you can use \\'print()\\' to save whatever important information you will then need.\\nThese print outputs will then appear in the \\'Observation:\\' field, which will be available as input for the next step.\\nIn the end you have to return a final answer using the `final_answer` tool.\\n\\nHere are a few examples using notional tools:\\n---\\nTask: \"Generate an image of the oldest person in this document.\"\\n\\nThought: I will proceed step by step and use the following tools: `document_qa` to find the oldest person in the document, then `image_generator` to generate an image according to the answer.\\n\\nanswer = document_qa(document=document, question=\"Who is the oldest person mentioned?\")\\nprint(answer)\\n\\nObservation: \"The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland.\"\\n\\nThought: I will now generate an image showcasing the oldest person.\\n\\nimage = image_generator(\"A portrait of John Doe, a 55-year-old man living in Canada.\")\\nfinal_answer(image)\\n\\n\\n---\\nTask: \"What is the result of the following operation: 5 + 3 + 1294.678?\"\\n\\nThought: I will use Python code to compute the result of the operation and then return the final answer using the `final_answer` tool.\\n\\nresult = 5 + 3 + 1294.678\\nfinal_answer(result)\\n\\n\\n---\\nTask:\\n\"Answer the question in the variable `question` about the image stored in the variable `image`. The question is in French.\\nYou have been provided with these additional arguments, that you can access using the keys as variables in your Python code:\\n{\\'question\\': \\'Quel est l\\'animal sur l\\'image?\\', \\'image\\': \\'path/to/image.jpg\\'}\"\\n\\nThought: I will use the following tools: `translator` to translate the question into English and then `image_qa` to answer the question on the input image.\\n\\ntranslated_question = translator(question=question, src_lang=\"French\", tgt_lang=\"English\")\\nprint(f\"The translated question is {translated_question}.\")\\nanswer = image_qa(image=image, question=translated_question)\\nfinal_answer(f\"The answer is {answer}\")\\n\\n\\n---\\nTask:\\nIn a 1979 interview, Stanislaus Ulam discusses with Martin Sherwin about other great physicists of his time, including Oppenheimer.\\nWhat does he say was the consequence of Einstein learning too much math on his creativity, in one word?\\n\\nThought: I need to find and read the 1979 interview of Stanislaus Ulam with Martin Sherwin.\\n\\npages = web_search(query=\"1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein\")\\nprint(pages)\\n\\nObservation:\\nNo result found for query \"1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein\".\\n\\nThought: The query was maybe too restrictive and did not find any results. Let\\'s try again with a broader query.\\n\\npages = web_search(query=\"1979 interview Stanislaus Ulam\")\\nprint(pages)\\n\\nObservation:\\nFound 6 pages:\\n[Stanislaus Ulam 1979 interview](https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/)\\n\\n[Ulam discusses Manhattan Project](https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/)\\n\\n(truncated)\\n\\nThought: I will read the first 2 pages to know more.\\n\\nfor url in [\"https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/\", \"https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/\"]:\\n    whole_page = visit_webpage(url)\\n    print(whole_page)\\n    print(\"\\\\n\" + \"=\"*80 + \"\\\\n\")  # Print separator between pages\\n\\nObservation:\\nManhattan Project Locations:\\nLos Alamos, NM\\nStanislaus Ulam was a Polish-American mathematician. He worked on the Manhattan Project at Los Alamos and later helped design the hydrogen bomb. In this interview, he discusses his work at\\n(truncated)\\n\\nThought: I now have the final answer: from the webpages visited, Stanislaus Ulam says of Einstein: \"He learned too much mathematics and sort of diminished, it seems to me personally, it seems to me his purely physics creativity.\" Let\\'s answer in one word.\\n\\nfinal_answer(\"diminished\")\\n\\n\\n---\\nTask: \"Which city has the highest population: Guangzhou or Shanghai?\"\\n\\nThought: I need to get the populations for both cities and compare them: I will use the tool `web_search` to get the population of both cities.\\n\\nfor city in [\"Guangzhou\", \"Shanghai\"]:\\n    print(f\"Population {city}:\", web_search(f\"{city} population\"))\\n\\nObservation:\\nPopulation Guangzhou: [\\'Guangzhou has a population of 15 million inhabitants as of 2021.\\']\\nPopulation Shanghai: \\'26 million (2019)\\'\\n\\nThought: Now I know that Shanghai has the highest population.\\n\\nfinal_answer(\"Shanghai\")\\n\\n\\n---\\nTask: \"What is the current age of the pope, raised to the power 0.36?\"\\n\\nThought: I will use the tool `wikipedia_search` to get the age of the pope, and confirm that with a web search.\\n\\npope_age_wiki = wikipedia_search(query=\"current pope age\")\\nprint(\"Pope age as per wikipedia:\", pope_age_wiki)\\npope_age_search = web_search(query=\"current pope age\")\\nprint(\"Pope age as per google search:\", pope_age_search)\\n\\nObservation:\\nPope age: \"The pope Francis is currently 88 years old.\"\\n\\nThought: I know that the pope is 88 years old. Let\\'s compute the result using Python code.\\n\\npope_current_age = 88 ** 0.36\\nfinal_answer(pope_current_age)\\n\\n\\nAbove examples were using notional tools that might not exist for you. On top of performing computations in the Python code snippets that you create, you only have access to these tools, behaving like regular python functions:\\n\\ndef addition_tool(first_number: integer, second_number: integer) -> integer:\\n    \"\"\"Adds two numbers together\\n\\n    Args:\\n        first_number: first number to add\\n        second_number: second number to add\\n    \"\"\"\\n\\ndef final_answer(answer: any) -> any:\\n    \"\"\"Provides a final answer to the given problem.\\n\\n    Args:\\n        answer: The final answer to the problem\\n    \"\"\"\\n\\n\\n\\nHere are the rules you should always follow to solve your task:\\n1. Always provide a \\'Thought:\\' sequence, and a \\'\\' sequence ending with \\'\\', else you will fail.\\n2. Use only variables that you have defined!\\n3. Always use the right arguments for the tools. DO NOT pass the arguments as a dict as in \\'answer = wikipedia_search({\\'query\\': \"What is the place where James Bond lives?\"})\\', but use the arguments directly as in \\'answer = wikipedia_search(query=\"What is the place where James Bond lives?\")\\'.\\n4. For tools WITHOUT JSON output schema: Take care to not chain too many sequential tool calls in the same code block, as their output format is unpredictable. For instance, a call to wikipedia_search without a JSON output schema has an unpredictable return format, so do not have another tool call that depends on its output in the same block: rather output results with print() to use them in the next block.\\n5. For tools WITH JSON output schema: You can confidently chain multiple tool calls and directly access structured output fields in the same code block! When a tool has a JSON output schema, you know exactly what fields and data types to expect, allowing you to write robust code that directly accesses the structured response (e.g., result[\\'field_name\\']) without needing intermediate print() statements.\\n6. Call a tool only when needed, and never re-do a tool call that you previously did with the exact same parameters.\\n7. Don\\'t name any new variable with the same name as a tool: for instance don\\'t name a variable \\'final_answer\\'.\\n8. Never create any notional variables in our code, as having these in your logs will derail you from the true variables.\\n9. You can use imports in your code, but only from the following list of modules: [\\'collections\\', \\'datetime\\', \\'itertools\\', \\'math\\', \\'queue\\', \\'random\\', \\'re\\', \\'stat\\', \\'statistics\\', \\'time\\', \\'unicodedata\\']\\n10. The state persists between code executions: so if in one step you\\'ve created variables or imported modules, these will all persist.\\n11. Don\\'t give up! You\\'re in charge of solving the task, not providing directions to solve it.\\n\\nNow Begin!'}], tool_calls=None, raw=None, token_usage=None), ChatMessage(role=, content=[{'type': 'text', 'text': 'New task:\\nWhat is 41 plus 1?'}], tool_calls=None, raw=None, token_usage=None)], tool_calls=[ToolCall(name='python_interpreter', arguments='result = 41 + 1\\nfinal_answer(result)', id='call_1')], error=None, model_output_message=ChatMessage(role='assistant', content='Okay, let\\'s see. The task is \"What is 41 plus 1?\" Hmm, that\\'s straightforward. I need to add 41 and 1 together.\\n\\nWait, but there\\'s a tool called addition_tool that takes two integers. So maybe I should use that. But actually, since it\\'s a simple math problem, I could just compute it directly in Python code. Let me check the tools provided.\\n\\nLooking back at the tools: there\\'s addition_tool which adds two numbers, and final_answer. So the correct approach would be to use the addition_tool with first_number=41 and second_number=1. Then pass the result to final_answer.\\n\\nAlternatively, since it\\'s so simple, maybe just compute 41 + 1 directly. But the problem says to use the tools provided. The tools include addition_tool, so perhaps that\\'s the intended way.\\n\\nWait, the example tasks show that sometimes they just compute directly. Like in the second example: \"What is the result of the following operation: 5 + 3 + 1294.678?\" where they just did result = 5 + 3 + 1294.678 and then final_answer(result). So maybe for this simple addition, I can just do 41 +1 in code.\\n\\nBut the addition_tool is available. But using it might be better. However, the problem is so simple that maybe just adding them directly is acceptable. Let me check the rules. Rule 3 says to use the right arguments for the tools. If I use addition_tool, I need to call it with first_number and second_number.\\n\\nAlternatively, since 41 +1 is basic arithmetic, maybe the code can just compute it. Let me see. The task is to find 41 plus 1. The simplest way is to compute 41 +1. So code would be:\\n\\nresult = 41 + 1\\nfinal_answer(result)\\n\\nBut the addition_tool is there. Maybe the problem expects to use that tool. But the example with 5+3+1294.678 didn\\'t use a tool but just computed directly. So probably it\\'s okay to just do the addition directly.\\n\\nYes, because the addition_tool is just a function that does addition, but for such a simple case, using Python\\'s built-in addition is fine. So the code would be:\\n\\nresult = 41 + 1\\nfinal_answer(result)\\n\\nBut to check, in the example where they had 5+3+1294.678, they computed it directly. So that\\'s acceptable. So for this problem, same thing.\\n\\nSo Thought: I\\'ll compute 41 +1 directly and return the result.\\n\\nCode:\\n\\nresult = 41 + 1\\nfinal_answer(result)\\n\\nYes, that\\'s correct. No need for the addition_tool here since the operation is simple and Python can handle it directly.\\n\\n\\nThought: This is a simple arithmetic problem. I can directly compute the sum of 41 and 1 using Python\\'s built-in addition operator and then return the result using the `final_answer` tool.\\n\\n\\nresult = 41 + 1\\nfinal_answer(result)\\n', tool_calls=None, raw=ChatCompletionOutput(choices=[ChatCompletionOutputComplete(finish_reason='stop', index=0, message=ChatCompletionOutputMessage(role='assistant', content='Okay, let\\'s see. The task is \"What is 41 plus 1?\" Hmm, that\\'s straightforward. I need to add 41 and 1 together.\\n\\nWait, but there\\'s a tool called addition_tool that takes two integers. So maybe I should use that. But actually, since it\\'s a simple math problem, I could just compute it directly in Python code. Let me check the tools provided.\\n\\nLooking back at the tools: there\\'s addition_tool which adds two numbers, and final_answer. So the correct approach would be to use the addition_tool with first_number=41 and second_number=1. Then pass the result to final_answer.\\n\\nAlternatively, since it\\'s so simple, maybe just compute 41 + 1 directly. But the problem says to use the tools provided. The tools include addition_tool, so perhaps that\\'s the intended way.\\n\\nWait, the example tasks show that sometimes they just compute directly. Like in the second example: \"What is the result of the following operation: 5 + 3 + 1294.678?\" where they just did result = 5 + 3 + 1294.678 and then final_answer(result). So maybe for this simple addition, I can just do 41 +1 in code.\\n\\nBut the addition_tool is available. But using it might be better. However, the problem is so simple that maybe just adding them directly is acceptable. Let me check the rules. Rule 3 says to use the right arguments for the tools. If I use addition_tool, I need to call it with first_number and second_number.\\n\\nAlternatively, since 41 +1 is basic arithmetic, maybe the code can just compute it. Let me see. The task is to find 41 plus 1. The simplest way is to compute 41 +1. So code would be:\\n\\nresult = 41 + 1\\nfinal_answer(result)\\n\\nBut the addition_tool is there. Maybe the problem expects to use that tool. But the example with 5+3+1294.678 didn\\'t use a tool but just computed directly. So probably it\\'s okay to just do the addition directly.\\n\\nYes, because the addition_tool is just a function that does addition, but for such a simple case, using Python\\'s built-in addition is fine. So the code would be:\\n\\nresult = 41 + 1\\nfinal_answer(result)\\n\\nBut to check, in the example where they had 5+3+1294.678, they computed it directly. So that\\'s acceptable. So for this problem, same thing.\\n\\nSo Thought: I\\'ll compute 41 +1 directly and return the result.\\n\\nCode:\\n\\nresult = 41 + 1\\nfinal_answer(result)\\n\\nYes, that\\'s correct. No need for the addition_tool here since the operation is simple and Python can handle it directly.\\n\\n\\nThought: This is a simple arithmetic problem. I can directly compute the sum of 41 and 1 using Python\\'s built-in addition operator and then return the result using the `final_answer` tool.\\n\\n\\nresult = 41 + 1\\nfinal_answer(result)\\n', reasoning=None, tool_call_id=None, tool_calls=None, reasoning_content=None), logprobs=None, matched_stop='')], created=1773324479, id='76bf45bd450247d18284d4ee71f68060', model='Qwen/Qwen3-Next-80B-A3B-Thinking', system_fingerprint=None, usage=ChatCompletionOutputUsage(completion_tokens=670, prompt_tokens=2081, total_tokens=2751, prompt_tokens_details=None, reasoning_tokens=0), object='chat.completion', metadata={'weight_version': 'default'}), token_usage=TokenUsage(input_tokens=2081, output_tokens=670, total_tokens=2751)), model_output='Okay, let\\'s see. The task is \"What is 41 plus 1?\" Hmm, that\\'s straightforward. I need to add 41 and 1 together.\\n\\nWait, but there\\'s a tool called addition_tool that takes two integers. So maybe I should use that. But actually, since it\\'s a simple math problem, I could just compute it directly in Python code. Let me check the tools provided.\\n\\nLooking back at the tools: there\\'s addition_tool which adds two numbers, and final_answer. So the correct approach would be to use the addition_tool with first_number=41 and second_number=1. Then pass the result to final_answer.\\n\\nAlternatively, since it\\'s so simple, maybe just compute 41 + 1 directly. But the problem says to use the tools provided. The tools include addition_tool, so perhaps that\\'s the intended way.\\n\\nWait, the example tasks show that sometimes they just compute directly. Like in the second example: \"What is the result of the following operation: 5 + 3 + 1294.678?\" where they just did result = 5 + 3 + 1294.678 and then final_answer(result). So maybe for this simple addition, I can just do 41 +1 in code.\\n\\nBut the addition_tool is available. But using it might be better. However, the problem is so simple that maybe just adding them directly is acceptable. Let me check the rules. Rule 3 says to use the right arguments for the tools. If I use addition_tool, I need to call it with first_number and second_number.\\n\\nAlternatively, since 41 +1 is basic arithmetic, maybe the code can just compute it. Let me see. The task is to find 41 plus 1. The simplest way is to compute 41 +1. So code would be:\\n\\nresult = 41 + 1\\nfinal_answer(result)\\n\\nBut the addition_tool is there. Maybe the problem expects to use that tool. But the example with 5+3+1294.678 didn\\'t use a tool but just computed directly. So probably it\\'s okay to just do the addition directly.\\n\\nYes, because the addition_tool is just a function that does addition, but for such a simple case, using Python\\'s built-in addition is fine. So the code would be:\\n\\nresult = 41 + 1\\nfinal_answer(result)\\n\\nBut to check, in the example where they had 5+3+1294.678, they computed it directly. So that\\'s acceptable. So for this problem, same thing.\\n\\nSo Thought: I\\'ll compute 41 +1 directly and return the result.\\n\\nCode:\\n\\nresult = 41 + 1\\nfinal_answer(result)\\n\\nYes, that\\'s correct. No need for the addition_tool here since the operation is simple and Python can handle it directly.\\n\\n\\nThought: This is a simple arithmetic problem. I can directly compute the sum of 41 and 1 using Python\\'s built-in addition operator and then return the result using the `final_answer` tool.\\n\\n\\nresult = 41 + 1\\nfinal_answer(result)\\n', code_action='result = 41 + 1\\nfinal_answer(result)', observations='Execution logs:\\nLast output from code snippet:\\n42', observations_images=None, action_output=42, token_usage=TokenUsage(input_tokens=2081, output_tokens=670, total_tokens=2751), is_final_answer=True)\n"
          ]
        }
      ],
      "source": [
        "for step in agent.memory.steps:\n",
        "    print(f\"{type(step).__name__}: {step}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Those four classes — TaskStep, PlanningStep, ActionStep, and FinalAnswerStep — are the memory objects that track every agent run. They map directly to the ReAct loop from Figure 11-1:\n",
        "\n",
        "TaskStep stores the user’s initial instruction (and any input images — this is where screenshots enter the loop for computer-use agents).\n",
        "\n",
        "PlanningStep stores a high-level plan before execution, if you enable planning. Our toy example skipped it, but for complex tasks (“research three vendors and draft an email”) planning helps the agent stay on track.\n",
        "\n",
        "ActionStep is the core of the loop. Each cycle of think → act → observe produces one ActionStep containing: the LLM’s reasoning (model_output), which tools were called (tool_calls), what came back as text (observations), and what came back as images (observations_images). That last field is how screenshots flow through the loop in computer-use agents — you will see this in section 11.1.3.\n",
        "\n",
        "FinalAnswerStep stores the return value when the agent decides it is done.\n",
        "\n",
        "Now that you have seen the lifecycle in action, let’s look at the different ways to connect models and external capabilities to your agent."
      ],
      "metadata": {
        "id": "8Ca6VhIWVSIr"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kfEp_iIPlYhI"
      },
      "source": [
        "### Leveraging Hugging Face Spaces as tools"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Your tools don’t have to be hand-written functions. One of smolagents’ nicest tricks is turning any Hugging Face Space into a callable tool with a single line. Tool.from_space() wraps any of them as a tool your agent can call mid-loop.\n",
        "\n",
        "For example, let’s give our agent access to FLUX.1-schnell, a fast image generation model:\n",
        "\n"
      ],
      "metadata": {
        "id": "98ec57J3VWBn"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1O_2KbkVlW2Q",
        "outputId": "17e7dcc5-b79b-4472-bacd-673a3ee281a6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded as API: https://black-forest-labs-flux-1-dev.hf.space ✔\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/smolagents/tools.py:666: UserWarning: Since `api_name` was not defined, it was automatically set to the first available API: `/infer`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "\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[1mGenerate an image of a futuristic city with flying cars at sunset\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 InferenceClientModel - Qwen/Qwen3-Next-80B-A3B-Thinking \u001b[0m\u001b[38;2;212;183;2m──────────────────────────────────────────────────────\u001b[0m\u001b[38;2;212;183;2m─╯\u001b[0m\n"
            ],
            "text/html": [
              "
╭──────────────────────────────────────────────────── New run ────────────────────────────────────────────────────╮\n",
              "│                                                                                                                 │\n",
              "│ Generate an image of a futuristic city with flying cars at sunset                                               │\n",
              "│                                                                                                                 │\n",
              "╰─ InferenceClientModel - Qwen/Qwen3-Next-80B-A3B-Thinking ───────────────────────────────────────────────────────╯\n",
              "
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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 1 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
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              " ─ \u001b[1mExecuting parsed code:\u001b[0m ──────────────────────────────────────────────────────────────────────────────────────── \n",
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              "  \u001b[38;2;248;248;242;48;2;39;40;34m    \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprompt\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;34mFuturistic cityscape at sunset with flying cars, glowing neon signs, tall skyscrapers, vibrant \u001b[0m\u001b[48;2;39;40;34m    \u001b[0m  \n",
              "  \u001b[38;2;230;219;116;48;2;39;40;34morange and purple sky, cinematic lighting, highly detailed, photorealistic style\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;34m    \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mseed\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;34m42\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;34m    \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mrandomize_seed\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mTrue\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;34m    \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwidth\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;34m512\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;34m    \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mheight\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;34m512\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;34m    \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mguidance_scale\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;34m7.5\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;34m    \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnum_inference_steps\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;34m50\u001b[0m\u001b[48;2;39;40;34m                                                                                     \u001b[0m  \n",
              "  \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;34mimage\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"
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            "text/html": [
              "
 ─ Executing parsed code: ──────────────────────────────────────────────────────────────────────────────────────── \n",
              "  image = image_generator(                                                                                         \n",
              "      prompt=\"Futuristic cityscape at sunset with flying cars, glowing neon signs, tall skyscrapers, vibrant       \n",
              "  orange and purple sky, cinematic lighting, highly detailed, photorealistic style\",                               \n",
              "      seed=42,                                                                                                     \n",
              "      randomize_seed=True,                                                                                         \n",
              "      width=512,                                                                                                   \n",
              "      height=512,                                                                                                  \n",
              "      guidance_scale=7.5,                                                                                          \n",
              "      num_inference_steps=50                                                                                       \n",
              "  )                                                                                                                \n",
              "  final_answer(image)                                                                                              \n",
              " ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n",
              "
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              "\u001b[1;31mCode execution exceeded the maximum execution time of 30 seconds\u001b[0m\n"
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              "\u001b[2m[Step 1: Duration 42.93 seconds| Input tokens: 2,131 | Output tokens: 2,067]\u001b[0m\n"
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            "text/html": [
              "
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 2 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
              "
\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              " ─ \u001b[1mExecuting parsed code:\u001b[0m ──────────────────────────────────────────────────────────────────────────────────────── \n",
              "  \u001b[38;2;248;248;242;48;2;39;40;34mimage\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;34mimage_generator\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;34m    \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprompt\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;34mFuturistic cityscape at sunset with flying cars, glowing neon signs, tall skyscrapers, vibrant \u001b[0m\u001b[48;2;39;40;34m    \u001b[0m  \n",
              "  \u001b[38;2;230;219;116;48;2;39;40;34morange and purple sky, cinematic lighting\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;34m    \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mseed\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;34m42\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;34m    \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mrandomize_seed\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mTrue\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;34m    \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwidth\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;34m256\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;34m    \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mheight\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;34m256\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;34m    \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mguidance_scale\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;34m7.0\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;34m    \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnum_inference_steps\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;34m30\u001b[0m\u001b[48;2;39;40;34m                                                                                     \u001b[0m  \n",
              "  \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;34mimage\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"
            ],
            "text/html": [
              "
 ─ Executing parsed code: ──────────────────────────────────────────────────────────────────────────────────────── \n",
              "  image = image_generator(                                                                                         \n",
              "      prompt=\"Futuristic cityscape at sunset with flying cars, glowing neon signs, tall skyscrapers, vibrant       \n",
              "  orange and purple sky, cinematic lighting\",                                                                      \n",
              "      seed=42,                                                                                                     \n",
              "      randomize_seed=True,                                                                                         \n",
              "      width=256,                                                                                                   \n",
              "      height=256,                                                                                                  \n",
              "      guidance_scale=7.0,                                                                                          \n",
              "      num_inference_steps=30                                                                                       \n",
              "  )                                                                                                                \n",
              "  final_answer(image)                                                                                              \n",
              " ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n",
              "
\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [],
            "text/html": [
              "
\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1;38;2;212;183;2mFinal answer: /tmp/gradio/bf12ae40151c5b5b849ebd24b75083cda7cff290b1d760d5e3e3bb1734078400/image.webp\u001b[0m\n"
            ],
            "text/html": [
              "
Final answer: /tmp/gradio/bf12ae40151c5b5b849ebd24b75083cda7cff290b1d760d5e3e3bb1734078400/image.webp\n",
              "
\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[2m[Step 2: Duration 20.28 seconds| Input tokens: 6,525 | Output tokens: 3,577]\u001b[0m\n"
            ],
            "text/html": [
              "
[Step 2: Duration 20.28 seconds| Input tokens: 6,525 | Output tokens: 3,577]\n",
              "
\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "Cannot embed the 'webp' image format",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    916\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    917\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 918\u001b[0;31m                 \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    919\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    920\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/smolagents/agent_types.py\u001b[0m in \u001b[0;36m_ipython_display_\u001b[0;34m(self, include, exclude)\u001b[0m\n\u001b[1;32m    115\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m         \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mImage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    118\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mto_raw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/IPython/core/display.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, url, filename, format, embed, width, height, retina, unconfined, metadata)\u001b[0m\n\u001b[1;32m   1221\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1222\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membed\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ACCEPTABLE_EMBEDDINGS\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1223\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Cannot embed the '%s' image format\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1224\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1225\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mimetype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_MIMETYPES\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Cannot embed the 'webp' image format"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ],
            "image/png": 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\n",
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\n"
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "image_gen_tool = Tool.from_space(\n",
        "    space_id=\"black-forest-labs/FLUX.1-dev\",\n",
        "    name=\"image_generator\",\n",
        "    description=\"Generates an image following your prompt. Returns the image.\")\n",
        "\n",
        "agent = CodeAgent(tools=[image_gen_tool], model=model)\n",
        "\n",
        "agent.run(\"Generate an image of a futuristic city with flying cars at sunset\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The agent writes code that calls image_generator(prompt=”...”), receives the generated image as an observations_images entry in its ActionStep, and can then reason about it or return it as the final answer. The same pattern works for any Space: swap in a speech-to-text Space and your agent can transcribe audio, swap in a document OCR Space and it can read PDFs. The key insight is that models become tools, your agent doesn’t need to do everything itself, it just needs to know which specialist to call.\n",
        "\n"
      ],
      "metadata": {
        "id": "O_YDeeMzVZaT"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nr8owXY6pRXX"
      },
      "source": [
        "### Connecting your model"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You may have noticed we have been writing model=model in every snippet without explaining where it comes from. That’s because smolagents is model-agnostic: it doesn’t care whether your LLM runs on your laptop, on a rented GPU, or behind an API. You just need to pick the right wrapper class for your setup.\n",
        "\n",
        "For local models, smolagents offers three options depending on your hardware:\n",
        "\n",
        "TransformersModel: runs models locally via the transformers library, e.g. TransformersModel(model_id=\"Qwen/Qwen2.5-Coder-32B-Instruct”, device=\"cuda”)\n",
        "\n",
        "MLXModel: optimized for Apple Silicon (M1/M2/M3/M4) using the mlx library, e.g. MLXModel(model_id=\"mlx-community/Llama-3.2-3B-Instruct-4bit”)\n",
        "\n",
        "VLLMModel: connects to a vLLM server, useful when you want to serve a model once and share it across multiple agents or applications.\n",
        "\n",
        "For hosted models, you have three more:\n",
        "\n",
        "InferenceClientModel\n",
        "Connects to Hugging Face Inference Providers, a router that gives you access to models served by multiple providers. Hugging Face also offers Inference Endpoints, where you rent a dedicated instance for any model. This class works with both, e.g. InferenceClientModel(model_id=\"moonshotai/Kimi-K2-Thinking”)\n",
        "\n",
        "OpenAIServerModel\n",
        "Connects to OpenAI’s API or any OpenAI-compatible endpoint.\n",
        "\n",
        "LiteLLMModel\n",
        "A wrapper around LiteLLM that lets you call almost any API (Anthropic, Gemini, Bedrock, Mistral, etc.) through a unified interface.\n",
        "\n",
        "In practice, InferenceClientModel is the fastest way to get started: it doesen’t require a beefy computer and third party APIs but just a Hugging Face token. Here is a complete working example:"
      ],
      "metadata": {
        "id": "IgvD74DqVeK_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "L6B9AU6ypDEq",
        "outputId": "f01d5258-f84c-41ff-e9c3-35d2f23d6fc9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "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\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[1mWhat is the current weather in Paris?\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 InferenceClientModel - Qwen/Qwen3-Next-80B-A3B-Thinking \u001b[0m\u001b[38;2;212;183;2m──────────────────────────────────────────────────────\u001b[0m\u001b[38;2;212;183;2m─╯\u001b[0m\n"
            ],
            "text/html": [
              "
╭──────────────────────────────────────────────────── New run ────────────────────────────────────────────────────╮\n",
              "│                                                                                                                 │\n",
              "│ What is the current weather in Paris?                                                                           │\n",
              "│                                                                                                                 │\n",
              "╰─ InferenceClientModel - Qwen/Qwen3-Next-80B-A3B-Thinking ───────────────────────────────────────────────────────╯\n",
              "
\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[38;2;212;183;2m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ \u001b[0m\u001b[1;37mStep 1\u001b[0m\u001b[38;2;212;183;2m ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\n"
            ],
            "text/html": [
              "
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 1 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
              "
\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              " ─ \u001b[1mExecuting parsed code:\u001b[0m ──────────────────────────────────────────────────────────────────────────────────────── \n",
              "  \u001b[38;2;248;248;242;48;2;39;40;34msearch_results\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;34mweb_search\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;34mcurrent weather in Paris\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;34msearch_results\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"
            ],
            "text/html": [
              "
 ─ Executing parsed code: ──────────────────────────────────────────────────────────────────────────────────────── \n",
              "  search_results = web_search(query=\"current weather in Paris\")                                                    \n",
              "  print(search_results)                                                                                            \n",
              " ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n",
              "
\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mExecution logs:\u001b[0m\n",
              "## Search Results\n",
              "\n",
              "[Daily Forecast Paris, France· as of 4:00 AM \n",
              "PDT](https://weather.com/weather/tenday/l/1a8af5b9d8971c46dd5a52547f9221e22cd895d8d8639267a87df614d0912830)\n",
              "Be prepared with the most accurate 10-day forecast for Paris, France with highs, lows, chance of precipitation from\n",
              "The Weather Channel and Weather.com\n",
              "\n",
              "[Paris, Ville de Paris, France Weather Forecast | \n",
              "AccuWeather](https://www.accuweather.com/en/fr/paris/623/weather-forecast/623)\n",
              "Paris, Ville de Paris, France Weather Forecast, with current conditions, wind, air quality, and what to expect for \n",
              "the next 3 days.\n",
              "\n",
              "[Weather \n",
              "Today](https://weather.com/weather/today/l/1a8af5b9d8971c46dd5a52547f9221e22cd895d8d8639267a87df614d0912830)\n",
              "January 8, 2026 -Today’s and tonight’s Paris, France weather forecast, weather conditions and Doppler radar from \n",
              "The Weather Channel and weather.com\n",
              "\n",
              "[Hourly Weather Paris, France · as of 4:00 PM \n",
              "PST](https://weather.com/weather/hourbyhour/l/1a8af5b9d8971c46dd5a52547f9221e22cd895d8d8639267a87df614d0912830)\n",
              "Hourly Local Weather Forecast, weather conditions, precipitation, dew point, humidity, wind from weather.com and \n",
              "The Weather Channel\n",
              "\n",
              "[Paris, France 10-Day Weather Forecast | Weather Underground](https://www.wunderground.com/forecast/fr/paris)\n",
              "Paris Weather Forecasts. Weather Underground provides local & long-range weather forecasts, weatherreports, maps & \n",
              "tropical weather conditions for the Paris area.\n",
              "\n",
              "[Paris (France) weather - Met Office](https://weather.metoffice.gov.uk/forecast/u09tvnxyj)\n",
              "January 8, 2026 -Paris 7 day weather forecast including weather warnings, temperature, rain, wind, visibility, \n",
              "humidity and UV\n",
              "\n",
              "[Weather – WHIO TV 7 and WHIO Radio](https://www.whio.com/weather/)\n",
              "1 day ago -24/7 Dayton weather with forecasts, radar, weather alerts and how to keep your family safe during severe\n",
              "storms.\n",
              "\n",
              "[Paris, IDF, FR Current Weather - The Weather \n",
              "Network](https://www.theweathernetwork.com/en/city/fr/ile-de-france/paris/current)\n",
              "Get Paris, IDF, FR current weather report with temperature, feels like, wind, humidity, pressure, UV and more from \n",
              "TheWeatherNetwork.com.\n",
              "\n",
              "[Weather \n",
              "Tenday](https://weather.com/weather/tenday/l/d2a540efb4e9604b3c1d01b7851a1d9d2ab4c7b3ba428e5799936ac54404c035)\n",
              "Be prepared with the most accurate 10-day forecast for Paris, Île-de-France, France with highs, lows, chance of \n",
              "precipitation from The Weather Channel and Weather.com\n",
              "\n",
              "[Yr - Paris - Hourly weather \n",
              "forecast](https://www.yr.no/en/forecast/hourly-table/2-2988507/France/Île-de-France+Region/Paris+Department/Paris?i\n",
              "=0)\n",
              "Today's weather forecast for Paris by the hour.\n",
              "\n",
              "Out: None\n"
            ],
            "text/html": [
              "
Execution logs:\n",
              "## Search Results\n",
              "\n",
              "[Daily Forecast Paris, France· as of 4:00 AM \n",
              "PDT](https://weather.com/weather/tenday/l/1a8af5b9d8971c46dd5a52547f9221e22cd895d8d8639267a87df614d0912830)\n",
              "Be prepared with the most accurate 10-day forecast for Paris, France with highs, lows, chance of precipitation from\n",
              "The Weather Channel and Weather.com\n",
              "\n",
              "[Paris, Ville de Paris, France Weather Forecast | \n",
              "AccuWeather](https://www.accuweather.com/en/fr/paris/623/weather-forecast/623)\n",
              "Paris, Ville de Paris, France Weather Forecast, with current conditions, wind, air quality, and what to expect for \n",
              "the next 3 days.\n",
              "\n",
              "[Weather \n",
              "Today](https://weather.com/weather/today/l/1a8af5b9d8971c46dd5a52547f9221e22cd895d8d8639267a87df614d0912830)\n",
              "January 8, 2026 -Today’s and tonight’s Paris, France weather forecast, weather conditions and Doppler radar from \n",
              "The Weather Channel and weather.com\n",
              "\n",
              "[Hourly Weather Paris, France · as of 4:00 PM \n",
              "PST](https://weather.com/weather/hourbyhour/l/1a8af5b9d8971c46dd5a52547f9221e22cd895d8d8639267a87df614d0912830)\n",
              "Hourly Local Weather Forecast, weather conditions, precipitation, dew point, humidity, wind from weather.com and \n",
              "The Weather Channel\n",
              "\n",
              "[Paris, France 10-Day Weather Forecast | Weather Underground](https://www.wunderground.com/forecast/fr/paris)\n",
              "Paris Weather Forecasts. Weather Underground provides local & long-range weather forecasts, weatherreports, maps & \n",
              "tropical weather conditions for the Paris area.\n",
              "\n",
              "[Paris (France) weather - Met Office](https://weather.metoffice.gov.uk/forecast/u09tvnxyj)\n",
              "January 8, 2026 -Paris 7 day weather forecast including weather warnings, temperature, rain, wind, visibility, \n",
              "humidity and UV\n",
              "\n",
              "[Weather – WHIO TV 7 and WHIO Radio](https://www.whio.com/weather/)\n",
              "1 day ago -24/7 Dayton weather with forecasts, radar, weather alerts and how to keep your family safe during severe\n",
              "storms.\n",
              "\n",
              "[Paris, IDF, FR Current Weather - The Weather \n",
              "Network](https://www.theweathernetwork.com/en/city/fr/ile-de-france/paris/current)\n",
              "Get Paris, IDF, FR current weather report with temperature, feels like, wind, humidity, pressure, UV and more from \n",
              "TheWeatherNetwork.com.\n",
              "\n",
              "[Weather \n",
              "Tenday](https://weather.com/weather/tenday/l/d2a540efb4e9604b3c1d01b7851a1d9d2ab4c7b3ba428e5799936ac54404c035)\n",
              "Be prepared with the most accurate 10-day forecast for Paris, Île-de-France, France with highs, lows, chance of \n",
              "precipitation from The Weather Channel and Weather.com\n",
              "\n",
              "[Yr - Paris - Hourly weather \n",
              "forecast](https://www.yr.no/en/forecast/hourly-table/2-2988507/France/Île-de-France+Region/Paris+Department/Paris?i\n",
              "=0)\n",
              "Today's weather forecast for Paris by the hour.\n",
              "\n",
              "Out: None\n",
              "
\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[2m[Step 1: Duration 8.24 seconds| Input tokens: 2,085 | Output tokens: 1,768]\u001b[0m\n"
            ],
            "text/html": [
              "
[Step 1: Duration 8.24 seconds| Input tokens: 2,085 | Output tokens: 1,768]\n",
              "
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[38;2;212;183;2m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ \u001b[0m\u001b[1;37mStep 2\u001b[0m\u001b[38;2;212;183;2m ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\n"
            ],
            "text/html": [
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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 2 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
              "
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1;31mError in code parsing:\u001b[0m\n",
              "\u001b[1;31mYour code snippet is invalid, because the regex pattern (.*?) was not found in it.\u001b[0m\n",
              "\u001b[1;31m                Here is your code snippet:\u001b[0m\n",
              "\u001b[1;31m                Okay, let's see. The user asked for the current weather in Paris. I did a web search for \"current \u001b[0m\n",
              "\u001b[1;31mweather in Paris\" and got a bunch of results from different weather sites. Now I need to extract the current \u001b[0m\n",
              "\u001b[1;31mweather information from these results.\u001b[0m\n",
              "\n",
              "\u001b[1;31mLooking at the observation, the search results include links from Weather.com, AccuWeather, Weather Underground, \u001b[0m\n",
              "\u001b[1;31mMet Office, The Weather Network, Yr, etc. But the problem is that the observation just lists the URLs and titles, \u001b[0m\n",
              "\u001b[1;31mnot the actual weather data. So I need to visit one of these pages to get the current conditions.\u001b[0m\n",
              "\n",
              "\u001b[1;31mWait, but the tools available are only web_search and final_answer. There's no tool to visit the webpage directly. \u001b[0m\n",
              "\u001b[1;31mWait, in the previous examples, there was a 'visit_webpage' tool, but in the current setup, the allowed tools are \u001b[0m\n",
              "\u001b[1;31monly web_search and final_answer. Wait, let me check the problem statement again.\u001b[0m\n",
              "\n",
              "\u001b[1;31mThe user provided that the available tools are:\u001b[0m\n",
              "\n",
              "\u001b[1;31mdef web_search(query: string) -> string:\u001b[0m\n",
              "\u001b[1;31m    \"\"\"Performs a duckduckgo web search based on your query (think a Google search) then returns the top search \u001b[0m\n",
              "\u001b[1;31mresults.\u001b[0m\n",
              "\n",
              "\u001b[1;31m    Args:\u001b[0m\n",
              "\u001b[1;31m        query: The search query to perform.\u001b[0m\n",
              "\u001b[1;31m    \"\"\"\u001b[0m\n",
              "\n",
              "\u001b[1;31mdef final_answer(answer: any) -> any:\u001b[0m\n",
              "\u001b[1;31m    \"\"\"Provides a final answer to the given problem.\u001b[0m\n",
              "\n",
              "\u001b[1;31m    Args:\u001b[0m\n",
              "\u001b[1;31m        answer: The final answer to the problem\u001b[0m\n",
              "\u001b[1;31m    \"\"\"\u001b[0m\n",
              "\n",
              "\u001b[1;31mOh right, in the current setup, the only tools are web_search and final_answer. There's no 'visit_webpage' tool \u001b[0m\n",
              "\u001b[1;31mavailable. So in previous examples, sometimes they used visit_webpage, but according to the current problem's \u001b[0m\n",
              "\u001b[1;31mrules, that's not present. Wait, looking back at the initial problem description:\u001b[0m\n",
              "\n",
              "\u001b[1;31m\"you only have access to these tools, behaving like regular python functions: [...\\] web_search and final_answer\"\u001b[0m\n",
              "\n",
              "\u001b[1;31mSo the tools are only those two. So the web_search function returns the top search results (as a string, probably a\u001b[0m\n",
              "\u001b[1;31mlist of URLs and titles), but not the actual content of the pages. So to get the current weather, I need to look at\u001b[0m\n",
              "\u001b[1;31mthe search results and see if any of them have the current weather details in their snippets.\u001b[0m\n",
              "\n",
              "\u001b[1;31mWait, in the observation, the search results show things like \"January 8, 2026 -Today’s and tonight’s Paris, France\u001b[0m\n",
              "\u001b[1;31mweather forecast, weather conditions and Doppler radar from The Weather Channel and weather.com\" but that's just \u001b[0m\n",
              "\u001b[1;31mthe title and description of the search result. The actual weather data (like temperature, etc.) isn't in the \u001b[0m\n",
              "\u001b[1;31mobservation.\u001b[0m\n",
              "\n",
              "\u001b[1;31mHmm, so perhaps the web_search returns a list of search results where each result includes a title, URL, and maybe \u001b[0m\n",
              "\u001b[1;31ma snippet. But in the observation provided, it's a list of entries with titles and URLs. For example:\u001b[0m\n",
              "\n",
              "\u001b[1;31m[Daily Forecast Paris, France· as of 4:00 AM \u001b[0m\n",
              "\u001b[1;31mPDT\\](https://weather.com/weather/tenday/l/1a8af5b9d8971c46dd5a52547f9221e22cd895d8d8639267a87df614d0912830)\u001b[0m\n",
              "\u001b[1;31mBe prepared with the most accurate 10-day forecast for Paris, France with highs, lows, chance of precipitation from\u001b[0m\n",
              "\u001b[1;31mThe Weather Channel and Weather.com\u001b[0m\n",
              "\n",
              "\u001b[1;31mSo the \"Be prepared with...\" part is the snippet. Maybe in some of these snippets, there's a mention of current \u001b[0m\n",
              "\u001b[1;31mweather. Let me check each of the search results.\u001b[0m\n",
              "\n",
              "\u001b[1;31mLooking at the observation:\u001b[0m\n",
              "\n",
              "\u001b[1;31m- The first result's snippet says \"Be prepared with the most accurate 10-day forecast for Paris, France with highs,\u001b[0m\n",
              "\u001b[1;31mlows, chance of precipitation from The Weather Channel and Weather.com\" – that's for 10-day forecast, not current.\u001b[0m\n",
              "\n",
              "\u001b[1;31mSecond result: \"Paris, Ville de Paris, France Weather Forecast, with current conditions, wind, air quality, and \u001b[0m\n",
              "\u001b[1;31mwhat to expect for the next 3 days.\" – mentions \"current conditions\" so maybe this one has the current weather.\u001b[0m\n",
              "\n",
              "\u001b[1;31mThird result: \"January 8, 2026 -Today’s and tonight’s Paris, France weather forecast, weather conditions and \u001b[0m\n",
              "\u001b[1;31mDoppler radar from The Weather Channel and weather.com\" – this says \"weather conditions\" so possibly current.\u001b[0m\n",
              "\n",
              "\u001b[1;31mFourth result: \"Hourly Local Weather Forecast, weather conditions, precipitation, dew point, humidity, wind from \u001b[0m\n",
              "\u001b[1;31mweather.com and The Weather Channel\" – again, weather conditions.\u001b[0m\n",
              "\n",
              "\u001b[1;31mBut the problem is that the search results don't give the actual numbers. So perhaps the current weather data isn't\u001b[0m\n",
              "\u001b[1;31min the search results snippets. So maybe I need to use the web_search results to find a specific page and then \u001b[0m\n",
              "\u001b[1;31msomehow get the data from it. But since there's no visit_webpage tool, how can I do that?\u001b[0m\n",
              "\n",
              "\u001b[1;31mWait, the problem states that the available tools are only web_search and final_answer. So perhaps the web_search \u001b[0m\n",
              "\u001b[1;31mfunction, when queried for \"current weather in Paris\", returns the current weather data in the results. But in the \u001b[0m\n",
              "\u001b[1;31mactual implementation of web_search, maybe it returns structured data. However, according to the problem \u001b[0m\n",
              "\u001b[1;31mdescription, the web_search tool returns the top search results as a string. So maybe in reality, when you use \u001b[0m\n",
              "\u001b[1;31mweb_search, it might return the current weather data directly from a service, but in the example given, the \u001b[0m\n",
              "\u001b[1;31mobservation shows URLs and titles.\u001b[0m\n",
              "\n",
              "\u001b[1;31mWait, looking at the previous example where the task was \"Which city has the highest population: Guangzhou or \u001b[0m\n",
              "\u001b[1;31mShanghai?\" the code used web_search for each city and the observation showed 'Guangzhou has a population of 15 \u001b[0m\n",
              "\u001b[1;31mmillion inhabitants as of 2021.' and '26 million (2019)'.\u001b[0m\n",
              "\n",
              "\u001b[1;31mAh, right! So in some cases, the web_search tool might return actual data in the snippets. So maybe when I search \u001b[0m\n",
              "\u001b[1;31mfor \"current weather in Paris\", the web_search result includes the current temperature, conditions, etc., in the \u001b[0m\n",
              "\u001b[1;31msnippets.\u001b[0m\n",
              "\n",
              "\u001b[1;31mLooking at the current observation:\u001b[0m\n",
              "\n",
              "\u001b[1;31mThe search results listed in the observation don't have the actual numbers. For example, the first result's snippet\u001b[0m\n",
              "\u001b[1;31mis about 10-day forecast, the second says \"Paris, Ville de Paris, France Weather Forecast, with current conditions,\u001b[0m\n",
              "\u001b[1;31mwind, air quality, and what to expect for the next 3 days.\" But it doesn't say the actual current temperature.\u001b[0m\n",
              "\n",
              "\u001b[1;31mWait, but in the Guangzhou/Shanghai example, the web_search return included the actual numbers. So perhaps in this \u001b[0m\n",
              "\u001b[1;31mcase, the web_search for \"current weather in Paris\" would return a result that includes the current temperature. \u001b[0m\n",
              "\u001b[1;31mLet me check the observation again.\u001b[0m\n",
              "\n",
              "\u001b[1;31mIn the current observation, the search results list:\u001b[0m\n",
              "\n",
              "\u001b[1;31m[Daily Forecast Paris, France· as of 4:00 AM \u001b[0m\n",
              "\u001b[1;31mPDT\\](https://weather.com/weather/tenday/l/1a8af5b9d8971c46dd5a52547f9221e22cd895d8d8639267a87df614d0912830)\u001b[0m\n",
              "\u001b[1;31mBe prepared with the most accurate 10-day forecast for Paris, France with highs, lows, chance of precipitation from\u001b[0m\n",
              "\u001b[1;31mThe Weather Channel and Weather.com\u001b[0m\n",
              "\n",
              "\u001b[1;31m[Paris, Ville de Paris, France Weather Forecast | \u001b[0m\n",
              "\u001b[1;31mAccuWeather\\](https://www.accuweather.com/en/fr/paris/623/weather-forecast/623)\u001b[0m\n",
              "\u001b[1;31mParis, Ville de Paris, France Weather Forecast, with current conditions, wind, air quality, and what to expect for \u001b[0m\n",
              "\u001b[1;31mthe next 3 days.\u001b[0m\n",
              "\n",
              "\u001b[1;31m[Weather \u001b[0m\n",
              "\u001b[1;31mToday\\](https://weather.com/weather/today/l/1a8af5b9d8971c46dd5a52547f9221e22cd895d8d8639267a87df614d0912830)\u001b[0m\n",
              "\u001b[1;31mJanuary 8, 2026 -Today’s and tonight’s Paris, France weather forecast, weather conditions and Doppler radar from \u001b[0m\n",
              "\u001b[1;31mThe Weather Channel and weather.com\u001b[0m\n",
              "\n",
              "\u001b[1;31m[Hourly Weather Paris, France · as of 4:00 PM \u001b[0m\n",
              "\u001b[1;31mPST\\](https://weather.com/weather/hourbyhour/l/1a8af5b9d8971c46dd5a52547f9221e22cd895d8d8639267a87df614d0912830)\u001b[0m\n",
              "\u001b[1;31mHourly Local Weather Forecast, weather conditions, precipitation, dew point, humidity, wind from weather.com and \u001b[0m\n",
              "\u001b[1;31mThe Weather Channel\u001b[0m\n",
              "\n",
              "\u001b[1;31m[Paris, France 10-Day Weather Forecast | Weather Underground\\](https://www.wunderground.com/forecast/fr/paris)\u001b[0m\n",
              "\u001b[1;31mParis Weather Forecasts. Weather Underground provides local & long-range weather forecasts, weatherreports, maps & \u001b[0m\n",
              "\u001b[1;31mtropical weather conditions for the Paris area.\u001b[0m\n",
              "\n",
              "\u001b[1;31m[Paris (France) weather - Met Office\\](https://weather.metoffice.gov.uk/forecast/u09tvnxyj)\u001b[0m\n",
              "\u001b[1;31mJanuary 8, 2026 -Paris 7 day weather forecast including weather warnings, temperature, rain, wind, visibility, \u001b[0m\n",
              "\u001b[1;31mhumidity and UV\u001b[0m\n",
              "\n",
              "\u001b[1;31m[Weather – WHIO TV 7 and WHIO Radio\\](https://www.whio.com/weather/)\u001b[0m\n",
              "\u001b[1;31m1 day ago -24/7 Dayton weather with forecasts, radar, weather alerts and how to keep your family safe during severe\u001b[0m\n",
              "\u001b[1;31mstorms.\u001b[0m\n",
              "\n",
              "\u001b[1;31m[Paris, IDF, FR Current Weather - The Weather \u001b[0m\n",
              "\u001b[1;31mNetwork\\](https://www.theweathernetwork.com/en/city/fr/ile-de-france/paris/current)\u001b[0m\n",
              "\u001b[1;31mGet Paris, IDF, FR current weather report with temperature, feels like, wind, humidity, pressure, UV and more from \u001b[0m\n",
              "\u001b[1;31mTheWeatherNetwork.com.\u001b[0m\n",
              "\n",
              "\u001b[1;31m[Weather \u001b[0m\n",
              "\u001b[1;31mTenday\\](https://weather.com/weather/tenday/l/d2a540efb4e9604b3c1d01b7851a1d9d2ab4c7b3ba428e5799936ac54404c035)\u001b[0m\n",
              "\u001b[1;31mBe prepared with the most accurate 10-day forecast for Paris, Île-de-France, France with highs, lows, chance of \u001b[0m\n",
              "\u001b[1;31mprecipitation from The Weather Channel and Weather.com\u001b[0m\n",
              "\n",
              "\u001b[1;31m[Yr - Paris - Hourly weather \u001b[0m\n",
              "\u001b[1;31mforecast\\](https://www.yr.no/en/forecast/hourly-table/2-2988507/France/Île-de-France+Region/Paris+Department/Paris?\u001b[0m\n",
              "\u001b[1;31mi=0)\u001b[0m\n",
              "\u001b[1;31mToday's weather forecast for Paris by the hour.\u001b[0m\n",
              "\n",
              "\u001b[1;31mWait, but the actual current weather data (like temperature, etc.) isn't in the observation. The snippets just \u001b[0m\n",
              "\u001b[1;31mdescribe the content of the pages. So to get the actual current weather, I need to access one of these pages, but \u001b[0m\n",
              "\u001b[1;31mthere's no tool to visit the webpage. Hmm. But in the previous examples, like the \"current age of the pope\" task, \u001b[0m\n",
              "\u001b[1;31mthey used wikipedia_search and web_search and got the age directly. So perhaps in this case, the web_search for \u001b[0m\n",
              "\u001b[1;31m\"current weather in Paris\" returns something like \"Current temperature in Paris is 10°C, partly cloudy\" in the \u001b[0m\n",
              "\u001b[1;31msearch results.\u001b[0m\n",
              "\n",
              "\u001b[1;31mWait, maybe the actual implementation of the web_search tool in this environment is such that when you search for \u001b[0m\n",
              "\u001b[1;31m\"current weather in Paris\", it returns the current weather data in the search results. For example, maybe the \u001b[0m\n",
              "\u001b[1;31msearch results include a featured snippet with the current weather.\u001b[0m\n",
              "\n",
              "\u001b[1;31mLooking at the observation again, the current search results don't have that. But perhaps in reality, when the \u001b[0m\n",
              "\u001b[1;31mweb_search function is called, it might return structured data. However, in the given observation, the output is a \u001b[0m\n",
              "\u001b[1;31mlist of URLs and snippets. But maybe in this specific case, one of the search results has a snippet with the \u001b[0m\n",
              "\u001b[1;31mcurrent weather.\u001b[0m\n",
              "\n",
              "\u001b[1;31mFor example, maybe the \"Paris, IDF, FR Current Weather - The Weather Network\" snippet says something like \"Current \u001b[0m\n",
              "\u001b[1;31mweather: 5°C, cloudy\" but in the observation provided, the snippets are just general descriptions.\u001b[0m\n",
              "\n",
              "\u001b[1;31mWait, the user's observation shows:\u001b[0m\n",
              "\n",
              "\u001b[1;31m[Paris, IDF, FR Current Weather - The Weather \u001b[0m\n",
              "\u001b[1;31mNetwork\\](https://www.theweathernetwork.com/en/city/fr/ile-de-france/paris/current)\u001b[0m\n",
              "\u001b[1;31mGet Paris, IDF, FR current weather report with temperature, feels like, wind, humidity, pressure, UV and more from \u001b[0m\n",
              "\u001b[1;31mTheWeatherNetwork.com.\u001b[0m\n",
              "\n",
              "\u001b[1;31mSo the snippet here says \"Get Paris, IDF, FR current weather report with temperature, feels like, wind, humidity, \u001b[0m\n",
              "\u001b[1;31mpressure, UV and more...\" but doesn't actually give the numbers. So perhaps the web_search tool isn't returning the\u001b[0m\n",
              "\u001b[1;31mactual current data but just the page titles and descriptions. Then how to get the actual data?\u001b[0m\n",
              "\n",
              "\u001b[1;31mThis is a problem. Since there's no tool to visit the webpages and get their content, and the current web_search \u001b[0m\n",
              "\u001b[1;31mreturns only the list of search results without the actual data, then perhaps this task can't be solved with the \u001b[0m\n",
              "\u001b[1;31mavailable tools. But that can't be the case. Maybe the web_search function actually returns the current weather \u001b[0m\n",
              "\u001b[1;31mdata when searched for \"current weather in Paris\" in some specific way.\u001b[0m\n",
              "\n",
              "\u001b[1;31mWait, perhaps the web_search tool is actually integrated with a weather API and returns the current weather \u001b[0m\n",
              "\u001b[1;31mdirectly. For example, in some systems, when you search for \"current weather Paris\", it might return the \u001b[0m\n",
              "\u001b[1;31mtemperature, etc., as part of the search results.\u001b[0m\n",
              "\n",
              "\u001b[1;31mLooking back at the Guangzhou/Shanghai example: when they did web_search for \"Guangzhou population\", the \u001b[0m\n",
              "\u001b[1;31mobservation showed 'Guangzhou has a population of 15 million inhabitants as of 2021.' So the web_search tool in \u001b[0m\n",
              "\u001b[1;31mthis context does return actual data in the search results, not just URLs and titles.\u001b[0m\n",
              "\n",
              "\u001b[1;31mSo perhaps in the current case, the search results for \"current weather in Paris\" would include a line like \u001b[0m\n",
              "\u001b[1;31m\"Current temperature in Paris: 10°C, cloudy\" in the search results.\u001b[0m\n",
              "\n",
              "\u001b[1;31mBut in the observation provided for this task, the search results are listed as URLs and snippets that don't have \u001b[0m\n",
              "\u001b[1;31mthe actual numbers, but maybe in reality, when the web_search is run, it does return the current data. However, in \u001b[0m\n",
              "\u001b[1;31mthe observation given here, the snippets are the ones shown, which don't include the numbers. But perhaps the \u001b[0m\n",
              "\u001b[1;31mactual implementation of web_search in the system would return the data in the search results.\u001b[0m\n",
              "\n",
              "\u001b[1;31mWait, looking at the user's observation:\u001b[0m\n",
              "\n",
              "\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                \u001b[0m\n",
              "\u001b[1;31m                final_answer(\"YOUR FINAL ANSWER HERE\")\u001b[0m\n",
              "\u001b[1;31m                \u001b[0m\n",
              "\u001b[1;31mMake sure to provide correct code blobs.\u001b[0m\n"
            ],
            "text/html": [
              "
Error in code parsing:\n",
              "Your code snippet is invalid, because the regex pattern <code>(.*?)</code> was not found in it.\n",
              "                Here is your code snippet:\n",
              "                Okay, let's see. The user asked for the current weather in Paris. I did a web search for \"current \n",
              "weather in Paris\" and got a bunch of results from different weather sites. Now I need to extract the current \n",
              "weather information from these results.\n",
              "\n",
              "Looking at the observation, the search results include links from Weather.com, AccuWeather, Weather Underground, \n",
              "Met Office, The Weather Network, Yr, etc. But the problem is that the observation just lists the URLs and titles, \n",
              "not the actual weather data. So I need to visit one of these pages to get the current conditions.\n",
              "\n",
              "Wait, but the tools available are only web_search and final_answer. There's no tool to visit the webpage directly. \n",
              "Wait, in the previous examples, there was a 'visit_webpage' tool, but in the current setup, the allowed tools are \n",
              "only web_search and final_answer. Wait, let me check the problem statement again.\n",
              "\n",
              "The user provided that the available tools are:\n",
              "\n",
              "def web_search(query: string) -> string:\n",
              "    \"\"\"Performs a duckduckgo web search based on your query (think a Google search) then returns the top search \n",
              "results.\n",
              "\n",
              "    Args:\n",
              "        query: The search query to perform.\n",
              "    \"\"\"\n",
              "\n",
              "def final_answer(answer: any) -> any:\n",
              "    \"\"\"Provides a final answer to the given problem.\n",
              "\n",
              "    Args:\n",
              "        answer: The final answer to the problem\n",
              "    \"\"\"\n",
              "\n",
              "Oh right, in the current setup, the only tools are web_search and final_answer. There's no 'visit_webpage' tool \n",
              "available. So in previous examples, sometimes they used visit_webpage, but according to the current problem's \n",
              "rules, that's not present. Wait, looking back at the initial problem description:\n",
              "\n",
              "\"you only have access to these tools, behaving like regular python functions: [...\\] web_search and final_answer\"\n",
              "\n",
              "So the tools are only those two. So the web_search function returns the top search results (as a string, probably a\n",
              "list of URLs and titles), but not the actual content of the pages. So to get the current weather, I need to look at\n",
              "the search results and see if any of them have the current weather details in their snippets.\n",
              "\n",
              "Wait, in the observation, the search results show things like \"January 8, 2026 -Today’s and tonight’s Paris, France\n",
              "weather forecast, weather conditions and Doppler radar from The Weather Channel and weather.com\" but that's just \n",
              "the title and description of the search result. The actual weather data (like temperature, etc.) isn't in the \n",
              "observation.\n",
              "\n",
              "Hmm, so perhaps the web_search returns a list of search results where each result includes a title, URL, and maybe \n",
              "a snippet. But in the observation provided, it's a list of entries with titles and URLs. For example:\n",
              "\n",
              "[Daily Forecast Paris, France· as of 4:00 AM \n",
              "PDT\\](https://weather.com/weather/tenday/l/1a8af5b9d8971c46dd5a52547f9221e22cd895d8d8639267a87df614d0912830)\n",
              "Be prepared with the most accurate 10-day forecast for Paris, France with highs, lows, chance of precipitation from\n",
              "The Weather Channel and Weather.com\n",
              "\n",
              "So the \"Be prepared with...\" part is the snippet. Maybe in some of these snippets, there's a mention of current \n",
              "weather. Let me check each of the search results.\n",
              "\n",
              "Looking at the observation:\n",
              "\n",
              "- The first result's snippet says \"Be prepared with the most accurate 10-day forecast for Paris, France with highs,\n",
              "lows, chance of precipitation from The Weather Channel and Weather.com\" – that's for 10-day forecast, not current.\n",
              "\n",
              "Second result: \"Paris, Ville de Paris, France Weather Forecast, with current conditions, wind, air quality, and \n",
              "what to expect for the next 3 days.\" – mentions \"current conditions\" so maybe this one has the current weather.\n",
              "\n",
              "Third result: \"January 8, 2026 -Today’s and tonight’s Paris, France weather forecast, weather conditions and \n",
              "Doppler radar from The Weather Channel and weather.com\" – this says \"weather conditions\" so possibly current.\n",
              "\n",
              "Fourth result: \"Hourly Local Weather Forecast, weather conditions, precipitation, dew point, humidity, wind from \n",
              "weather.com and The Weather Channel\" – again, weather conditions.\n",
              "\n",
              "But the problem is that the search results don't give the actual numbers. So perhaps the current weather data isn't\n",
              "in the search results snippets. So maybe I need to use the web_search results to find a specific page and then \n",
              "somehow get the data from it. But since there's no visit_webpage tool, how can I do that?\n",
              "\n",
              "Wait, the problem states that the available tools are only web_search and final_answer. So perhaps the web_search \n",
              "function, when queried for \"current weather in Paris\", returns the current weather data in the results. But in the \n",
              "actual implementation of web_search, maybe it returns structured data. However, according to the problem \n",
              "description, the web_search tool returns the top search results as a string. So maybe in reality, when you use \n",
              "web_search, it might return the current weather data directly from a service, but in the example given, the \n",
              "observation shows URLs and titles.\n",
              "\n",
              "Wait, looking at the previous example where the task was \"Which city has the highest population: Guangzhou or \n",
              "Shanghai?\" the code used web_search for each city and the observation showed 'Guangzhou has a population of 15 \n",
              "million inhabitants as of 2021.' and '26 million (2019)'.\n",
              "\n",
              "Ah, right! So in some cases, the web_search tool might return actual data in the snippets. So maybe when I search \n",
              "for \"current weather in Paris\", the web_search result includes the current temperature, conditions, etc., in the \n",
              "snippets.\n",
              "\n",
              "Looking at the current observation:\n",
              "\n",
              "The search results listed in the observation don't have the actual numbers. For example, the first result's snippet\n",
              "is about 10-day forecast, the second says \"Paris, Ville de Paris, France Weather Forecast, with current conditions,\n",
              "wind, air quality, and what to expect for the next 3 days.\" But it doesn't say the actual current temperature.\n",
              "\n",
              "Wait, but in the Guangzhou/Shanghai example, the web_search return included the actual numbers. So perhaps in this \n",
              "case, the web_search for \"current weather in Paris\" would return a result that includes the current temperature. \n",
              "Let me check the observation again.\n",
              "\n",
              "In the current observation, the search results list:\n",
              "\n",
              "[Daily Forecast Paris, France· as of 4:00 AM \n",
              "PDT\\](https://weather.com/weather/tenday/l/1a8af5b9d8971c46dd5a52547f9221e22cd895d8d8639267a87df614d0912830)\n",
              "Be prepared with the most accurate 10-day forecast for Paris, France with highs, lows, chance of precipitation from\n",
              "The Weather Channel and Weather.com\n",
              "\n",
              "[Paris, Ville de Paris, France Weather Forecast | \n",
              "AccuWeather\\](https://www.accuweather.com/en/fr/paris/623/weather-forecast/623)\n",
              "Paris, Ville de Paris, France Weather Forecast, with current conditions, wind, air quality, and what to expect for \n",
              "the next 3 days.\n",
              "\n",
              "[Weather \n",
              "Today\\](https://weather.com/weather/today/l/1a8af5b9d8971c46dd5a52547f9221e22cd895d8d8639267a87df614d0912830)\n",
              "January 8, 2026 -Today’s and tonight’s Paris, France weather forecast, weather conditions and Doppler radar from \n",
              "The Weather Channel and weather.com\n",
              "\n",
              "[Hourly Weather Paris, France · as of 4:00 PM \n",
              "PST\\](https://weather.com/weather/hourbyhour/l/1a8af5b9d8971c46dd5a52547f9221e22cd895d8d8639267a87df614d0912830)\n",
              "Hourly Local Weather Forecast, weather conditions, precipitation, dew point, humidity, wind from weather.com and \n",
              "The Weather Channel\n",
              "\n",
              "[Paris, France 10-Day Weather Forecast | Weather Underground\\](https://www.wunderground.com/forecast/fr/paris)\n",
              "Paris Weather Forecasts. Weather Underground provides local & long-range weather forecasts, weatherreports, maps & \n",
              "tropical weather conditions for the Paris area.\n",
              "\n",
              "[Paris (France) weather - Met Office\\](https://weather.metoffice.gov.uk/forecast/u09tvnxyj)\n",
              "January 8, 2026 -Paris 7 day weather forecast including weather warnings, temperature, rain, wind, visibility, \n",
              "humidity and UV\n",
              "\n",
              "[Weather – WHIO TV 7 and WHIO Radio\\](https://www.whio.com/weather/)\n",
              "1 day ago -24/7 Dayton weather with forecasts, radar, weather alerts and how to keep your family safe during severe\n",
              "storms.\n",
              "\n",
              "[Paris, IDF, FR Current Weather - The Weather \n",
              "Network\\](https://www.theweathernetwork.com/en/city/fr/ile-de-france/paris/current)\n",
              "Get Paris, IDF, FR current weather report with temperature, feels like, wind, humidity, pressure, UV and more from \n",
              "TheWeatherNetwork.com.\n",
              "\n",
              "[Weather \n",
              "Tenday\\](https://weather.com/weather/tenday/l/d2a540efb4e9604b3c1d01b7851a1d9d2ab4c7b3ba428e5799936ac54404c035)\n",
              "Be prepared with the most accurate 10-day forecast for Paris, Île-de-France, France with highs, lows, chance of \n",
              "precipitation from The Weather Channel and Weather.com\n",
              "\n",
              "[Yr - Paris - Hourly weather \n",
              "forecast\\](https://www.yr.no/en/forecast/hourly-table/2-2988507/France/Île-de-France+Region/Paris+Department/Paris?\n",
              "i=0)\n",
              "Today's weather forecast for Paris by the hour.\n",
              "\n",
              "Wait, but the actual current weather data (like temperature, etc.) isn't in the observation. The snippets just \n",
              "describe the content of the pages. So to get the actual current weather, I need to access one of these pages, but \n",
              "there's no tool to visit the webpage. Hmm. But in the previous examples, like the \"current age of the pope\" task, \n",
              "they used wikipedia_search and web_search and got the age directly. So perhaps in this case, the web_search for \n",
              "\"current weather in Paris\" returns something like \"Current temperature in Paris is 10°C, partly cloudy\" in the \n",
              "search results.\n",
              "\n",
              "Wait, maybe the actual implementation of the web_search tool in this environment is such that when you search for \n",
              "\"current weather in Paris\", it returns the current weather data in the search results. For example, maybe the \n",
              "search results include a featured snippet with the current weather.\n",
              "\n",
              "Looking at the observation again, the current search results don't have that. But perhaps in reality, when the \n",
              "web_search function is called, it might return structured data. However, in the given observation, the output is a \n",
              "list of URLs and snippets. But maybe in this specific case, one of the search results has a snippet with the \n",
              "current weather.\n",
              "\n",
              "For example, maybe the \"Paris, IDF, FR Current Weather - The Weather Network\" snippet says something like \"Current \n",
              "weather: 5°C, cloudy\" but in the observation provided, the snippets are just general descriptions.\n",
              "\n",
              "Wait, the user's observation shows:\n",
              "\n",
              "[Paris, IDF, FR Current Weather - The Weather \n",
              "Network\\](https://www.theweathernetwork.com/en/city/fr/ile-de-france/paris/current)\n",
              "Get Paris, IDF, FR current weather report with temperature, feels like, wind, humidity, pressure, UV and more from \n",
              "TheWeatherNetwork.com.\n",
              "\n",
              "So the snippet here says \"Get Paris, IDF, FR current weather report with temperature, feels like, wind, humidity, \n",
              "pressure, UV and more...\" but doesn't actually give the numbers. So perhaps the web_search tool isn't returning the\n",
              "actual current data but just the page titles and descriptions. Then how to get the actual data?\n",
              "\n",
              "This is a problem. Since there's no tool to visit the webpages and get their content, and the current web_search \n",
              "returns only the list of search results without the actual data, then perhaps this task can't be solved with the \n",
              "available tools. But that can't be the case. Maybe the web_search function actually returns the current weather \n",
              "data when searched for \"current weather in Paris\" in some specific way.\n",
              "\n",
              "Wait, perhaps the web_search tool is actually integrated with a weather API and returns the current weather \n",
              "directly. For example, in some systems, when you search for \"current weather Paris\", it might return the \n",
              "temperature, etc., as part of the search results.\n",
              "\n",
              "Looking back at the Guangzhou/Shanghai example: when they did web_search for \"Guangzhou population\", the \n",
              "observation showed 'Guangzhou has a population of 15 million inhabitants as of 2021.' So the web_search tool in \n",
              "this context does return actual data in the search results, not just URLs and titles.\n",
              "\n",
              "So perhaps in the current case, the search results for \"current weather in Paris\" would include a line like \n",
              "\"Current temperature in Paris: 10°C, cloudy\" in the search results.\n",
              "\n",
              "But in the observation provided for this task, the search results are listed as URLs and snippets that don't have \n",
              "the actual numbers, but maybe in reality, when the web_search is run, it does return the current data. However, in \n",
              "the observation given here, the snippets are the ones shown, which don't include the numbers. But perhaps the \n",
              "actual implementation of web_search in the system would return the data in the search results.\n",
              "\n",
              "Wait, looking at the user's observation:\n",
              "\n",
              "\"</code>\n",
              "                It seems like you're trying to return the final answer, you can do it as follows:\n",
              "                <code>\n",
              "                final_answer(\"YOUR FINAL ANSWER HERE\")\n",
              "                </code>\n",
              "Make sure to provide correct code blobs.\n",
              "
\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[2m[Step 2: Duration 10.23 seconds| Input tokens: 5,131 | Output tokens: 4,726]\u001b[0m\n"
            ],
            "text/html": [
              "
[Step 2: Duration 10.23 seconds| Input tokens: 5,131 | Output tokens: 4,726]\n",
              "
\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[38;2;212;183;2m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ \u001b[0m\u001b[1;37mStep 3\u001b[0m\u001b[38;2;212;183;2m ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\n"
            ],
            "text/html": [
              "
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              "
\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              " ─ \u001b[1mExecuting parsed code:\u001b[0m ──────────────────────────────────────────────────────────────────────────────────────── \n",
              "  \u001b[38;2;255;70;137;48;2;39;40;34mimport\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;34mre\u001b[0m\u001b[48;2;39;40;34m                                                                                                      \u001b[0m  \n",
              "  \u001b[38;2;248;248;242;48;2;39;40;34msearch_results\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;34mweb_search\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;34mcurrent weather in Paris\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;34mmatch\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;34mre\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;34msearch\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;34mr\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;230;219;116;48;2;39;40;34md+)\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;34ms*°C\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[38;2;248;248;242;48;2;39;40;34msearch_results\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;102;217;239;48;2;39;40;34mif\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;34mmatch\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;34m    \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtemperature\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;34mmatch\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;34mgroup\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;34m1\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;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m°C\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;34m    \u001b[0m\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;34mtemperature\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;102;217;239;48;2;39;40;34melse\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;34m    \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmatch\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;34mre\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;34msearch\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;34mr\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;230;219;116;48;2;39;40;34md+)\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;34ms*degrees\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[38;2;248;248;242;48;2;39;40;34msearch_results\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;34m    \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mif\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;34mmatch\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;34m        \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtemperature\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;34mmatch\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;34mgroup\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;34m1\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;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m degrees\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;34m        \u001b[0m\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;34mtemperature\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;34m    \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34melse\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;34m        \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmatch\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;34mre\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;34msearch\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;34mr\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;230;219;116;48;2;39;40;34md+)\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;34ms*Celsius\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[38;2;248;248;242;48;2;39;40;34msearch_results\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;34m        \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mif\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;34mmatch\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;34m            \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtemperature\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;34mmatch\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;34mgroup\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;34m1\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;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m°C\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;34m            \u001b[0m\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;34mtemperature\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;34m        \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34melse\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;34m            \u001b[0m\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;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mCould not find current weather data. Here are the search results: \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;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;34msearch_results\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m  \n",
              " ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n"
            ],
            "text/html": [
              "
 ─ Executing parsed code: ──────────────────────────────────────────────────────────────────────────────────────── \n",
              "  import re                                                                                                        \n",
              "  search_results = web_search(query=\"current weather in Paris\")                                                    \n",
              "  match = re.search(r'(\\d+)\\s*°C', search_results)                                                                 \n",
              "  if match:                                                                                                        \n",
              "      temperature = match.group(1) + \"°C\"                                                                          \n",
              "      final_answer(temperature)                                                                                    \n",
              "  else:                                                                                                            \n",
              "      match = re.search(r'(\\d+)\\s*degrees', search_results)                                                        \n",
              "      if match:                                                                                                    \n",
              "          temperature = match.group(1) + \" degrees\"                                                                \n",
              "          final_answer(temperature)                                                                                \n",
              "      else:                                                                                                        \n",
              "          match = re.search(r'(\\d+)\\s*Celsius', search_results)                                                    \n",
              "          if match:                                                                                                \n",
              "              temperature = match.group(1) + \"°C\"                                                                  \n",
              "              final_answer(temperature)                                                                            \n",
              "          else:                                                                                                    \n",
              "              final_answer(\"Could not find current weather data. Here are the search results: \" + search_results)  \n",
              " ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── \n",
              "
\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [],
            "text/html": [
              "
\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1;38;2;212;183;2mFinal answer: Could not find current weather data. Here are the search results: ## Search Results\u001b[0m\n",
              "\n",
              "\u001b[1;38;2;212;183;2m[Daily Forecast Paris, France· as of 4:00 AM \u001b[0m\n",
              "\u001b[1;38;2;212;183;2mPDT](https://weather.com/weather/tenday/l/1a8af5b9d8971c46dd5a52547f9221e22cd895d8d8639267a87df614d0912830)\u001b[0m\n",
              "\u001b[1;38;2;212;183;2mBe prepared with the most accurate 10-day forecast for Paris, France with highs, lows, chance of precipitation from\u001b[0m\n",
              "\u001b[1;38;2;212;183;2mThe Weather Channel and Weather.com\u001b[0m\n",
              "\n",
              "\u001b[1;38;2;212;183;2m[Paris, Ville de Paris, France Weather Forecast | \u001b[0m\n",
              "\u001b[1;38;2;212;183;2mAccuWeather](https://www.accuweather.com/en/fr/paris/623/weather-forecast/623)\u001b[0m\n",
              "\u001b[1;38;2;212;183;2mParis, Ville de Paris, France Weather Forecast, with current conditions, wind, air quality, and what to expect for \u001b[0m\n",
              "\u001b[1;38;2;212;183;2mthe next 3 days.\u001b[0m\n",
              "\n",
              "\u001b[1;38;2;212;183;2m[Weather \u001b[0m\n",
              "\u001b[1;38;2;212;183;2mToday](https://weather.com/weather/today/l/1a8af5b9d8971c46dd5a52547f9221e22cd895d8d8639267a87df614d0912830)\u001b[0m\n",
              "\u001b[1;38;2;212;183;2mJanuary 8, 2026 -Today’s and tonight’s Paris, France weather forecast, weather conditions and Doppler radar from \u001b[0m\n",
              "\u001b[1;38;2;212;183;2mThe Weather Channel and weather.com\u001b[0m\n",
              "\n",
              "\u001b[1;38;2;212;183;2m[Hourly Weather Paris, France · as of 4:00 PM \u001b[0m\n",
              "\u001b[1;38;2;212;183;2mPST](https://weather.com/weather/hourbyhour/l/1a8af5b9d8971c46dd5a52547f9221e22cd895d8d8639267a87df614d0912830)\u001b[0m\n",
              "\u001b[1;38;2;212;183;2mHourly Local Weather Forecast, weather conditions, precipitation, dew point, humidity, wind from weather.com and \u001b[0m\n",
              "\u001b[1;38;2;212;183;2mThe Weather Channel\u001b[0m\n",
              "\n",
              "\u001b[1;38;2;212;183;2m[Paris, France 10-Day Weather Forecast | Weather Underground](https://www.wunderground.com/forecast/fr/paris)\u001b[0m\n",
              "\u001b[1;38;2;212;183;2mParis Weather Forecasts. Weather Underground provides local & long-range weather forecasts, weatherreports, maps & \u001b[0m\n",
              "\u001b[1;38;2;212;183;2mtropical weather conditions for the Paris area.\u001b[0m\n",
              "\n",
              "\u001b[1;38;2;212;183;2m[Paris (France) weather - Met Office](https://weather.metoffice.gov.uk/forecast/u09tvnxyj)\u001b[0m\n",
              "\u001b[1;38;2;212;183;2mJanuary 8, 2026 -Paris 7 day weather forecast including weather warnings, temperature, rain, wind, visibility, \u001b[0m\n",
              "\u001b[1;38;2;212;183;2mhumidity and UV\u001b[0m\n",
              "\n",
              "\u001b[1;38;2;212;183;2m[Weather – WHIO TV 7 and WHIO Radio](https://www.whio.com/weather/)\u001b[0m\n",
              "\u001b[1;38;2;212;183;2m1 day ago -24/7 Dayton weather with forecasts, radar, weather alerts and how to keep your family safe during severe\u001b[0m\n",
              "\u001b[1;38;2;212;183;2mstorms.\u001b[0m\n",
              "\n",
              "\u001b[1;38;2;212;183;2m[Paris, IDF, FR Current Weather - The Weather \u001b[0m\n",
              "\u001b[1;38;2;212;183;2mNetwork](https://www.theweathernetwork.com/en/city/fr/ile-de-france/paris/current)\u001b[0m\n",
              "\u001b[1;38;2;212;183;2mGet Paris, IDF, FR current weather report with temperature, feels like, wind, humidity, pressure, UV and more from \u001b[0m\n",
              "\u001b[1;38;2;212;183;2mTheWeatherNetwork.com.\u001b[0m\n",
              "\n",
              "\u001b[1;38;2;212;183;2m[Weather \u001b[0m\n",
              "\u001b[1;38;2;212;183;2mTenday](https://weather.com/weather/tenday/l/d2a540efb4e9604b3c1d01b7851a1d9d2ab4c7b3ba428e5799936ac54404c035)\u001b[0m\n",
              "\u001b[1;38;2;212;183;2mBe prepared with the most accurate 10-day forecast for Paris, Île-de-France, France with highs, lows, chance of \u001b[0m\n",
              "\u001b[1;38;2;212;183;2mprecipitation from The Weather Channel and Weather.com\u001b[0m\n",
              "\n",
              "\u001b[1;38;2;212;183;2m[Yr - Paris - Hourly weather \u001b[0m\n",
              "\u001b[1;38;2;212;183;2mforecast](https://www.yr.no/en/forecast/hourly-table/2-2988507/France/Île-de-France+Region/Paris+Department/Paris?i\u001b[0m\n",
              "\u001b[1;38;2;212;183;2m=0)\u001b[0m\n",
              "\u001b[1;38;2;212;183;2mToday's weather forecast for Paris by the hour.\u001b[0m\n"
            ],
            "text/html": [
              "
Final answer: Could not find current weather data. Here are the search results: ## Search Results\n",
              "\n",
              "[Daily Forecast Paris, France· as of 4:00 AM \n",
              "PDT](https://weather.com/weather/tenday/l/1a8af5b9d8971c46dd5a52547f9221e22cd895d8d8639267a87df614d0912830)\n",
              "Be prepared with the most accurate 10-day forecast for Paris, France with highs, lows, chance of precipitation from\n",
              "The Weather Channel and Weather.com\n",
              "\n",
              "[Paris, Ville de Paris, France Weather Forecast | \n",
              "AccuWeather](https://www.accuweather.com/en/fr/paris/623/weather-forecast/623)\n",
              "Paris, Ville de Paris, France Weather Forecast, with current conditions, wind, air quality, and what to expect for \n",
              "the next 3 days.\n",
              "\n",
              "[Weather \n",
              "Today](https://weather.com/weather/today/l/1a8af5b9d8971c46dd5a52547f9221e22cd895d8d8639267a87df614d0912830)\n",
              "January 8, 2026 -Today’s and tonight’s Paris, France weather forecast, weather conditions and Doppler radar from \n",
              "The Weather Channel and weather.com\n",
              "\n",
              "[Hourly Weather Paris, France · as of 4:00 PM \n",
              "PST](https://weather.com/weather/hourbyhour/l/1a8af5b9d8971c46dd5a52547f9221e22cd895d8d8639267a87df614d0912830)\n",
              "Hourly Local Weather Forecast, weather conditions, precipitation, dew point, humidity, wind from weather.com and \n",
              "The Weather Channel\n",
              "\n",
              "[Paris, France 10-Day Weather Forecast | Weather Underground](https://www.wunderground.com/forecast/fr/paris)\n",
              "Paris Weather Forecasts. Weather Underground provides local & long-range weather forecasts, weatherreports, maps & \n",
              "tropical weather conditions for the Paris area.\n",
              "\n",
              "[Paris (France) weather - Met Office](https://weather.metoffice.gov.uk/forecast/u09tvnxyj)\n",
              "January 8, 2026 -Paris 7 day weather forecast including weather warnings, temperature, rain, wind, visibility, \n",
              "humidity and UV\n",
              "\n",
              "[Weather – WHIO TV 7 and WHIO Radio](https://www.whio.com/weather/)\n",
              "1 day ago -24/7 Dayton weather with forecasts, radar, weather alerts and how to keep your family safe during severe\n",
              "storms.\n",
              "\n",
              "[Paris, IDF, FR Current Weather - The Weather \n",
              "Network](https://www.theweathernetwork.com/en/city/fr/ile-de-france/paris/current)\n",
              "Get Paris, IDF, FR current weather report with temperature, feels like, wind, humidity, pressure, UV and more from \n",
              "TheWeatherNetwork.com.\n",
              "\n",
              "[Weather \n",
              "Tenday](https://weather.com/weather/tenday/l/d2a540efb4e9604b3c1d01b7851a1d9d2ab4c7b3ba428e5799936ac54404c035)\n",
              "Be prepared with the most accurate 10-day forecast for Paris, Île-de-France, France with highs, lows, chance of \n",
              "precipitation from The Weather Channel and Weather.com\n",
              "\n",
              "[Yr - Paris - Hourly weather \n",
              "forecast](https://www.yr.no/en/forecast/hourly-table/2-2988507/France/Île-de-France+Region/Paris+Department/Paris?i\n",
              "=0)\n",
              "Today's weather forecast for Paris by the hour.\n",
              "
\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[2m[Step 3: Duration 13.25 seconds| Input tokens: 14,212 | Output tokens: 7,864]\u001b[0m\n"
            ],
            "text/html": [
              "
[Step 3: Duration 13.25 seconds| Input tokens: 14,212 | Output tokens: 7,864]\n",
              "
\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Could not find current weather data. Here are the search results: ## Search Results\n",
            "\n",
            "[Daily Forecast Paris, France· as of 4:00 AM PDT](https://weather.com/weather/tenday/l/1a8af5b9d8971c46dd5a52547f9221e22cd895d8d8639267a87df614d0912830)\n",
            "Be prepared with the most accurate 10-day forecast for Paris, France with highs, lows, chance of precipitation from The Weather Channel and Weather.com\n",
            "\n",
            "[Paris, Ville de Paris, France Weather Forecast | AccuWeather](https://www.accuweather.com/en/fr/paris/623/weather-forecast/623)\n",
            "Paris, Ville de Paris, France Weather Forecast, with current conditions, wind, air quality, and what to expect for the next 3 days.\n",
            "\n",
            "[Weather Today](https://weather.com/weather/today/l/1a8af5b9d8971c46dd5a52547f9221e22cd895d8d8639267a87df614d0912830)\n",
            "January 8, 2026 -Today’s and tonight’s Paris, France weather forecast, weather conditions and Doppler radar from The Weather Channel and weather.com\n",
            "\n",
            "[Hourly Weather Paris, France · as of 4:00 PM PST](https://weather.com/weather/hourbyhour/l/1a8af5b9d8971c46dd5a52547f9221e22cd895d8d8639267a87df614d0912830)\n",
            "Hourly Local Weather Forecast, weather conditions, precipitation, dew point, humidity, wind from weather.com and The Weather Channel\n",
            "\n",
            "[Paris, France 10-Day Weather Forecast | Weather Underground](https://www.wunderground.com/forecast/fr/paris)\n",
            "Paris Weather Forecasts. Weather Underground provides local & long-range weather forecasts, weatherreports, maps & tropical weather conditions for the Paris area.\n",
            "\n",
            "[Paris (France) weather - Met Office](https://weather.metoffice.gov.uk/forecast/u09tvnxyj)\n",
            "January 8, 2026 -Paris 7 day weather forecast including weather warnings, temperature, rain, wind, visibility, humidity and UV\n",
            "\n",
            "[Weather – WHIO TV 7 and WHIO Radio](https://www.whio.com/weather/)\n",
            "1 day ago -24/7 Dayton weather with forecasts, radar, weather alerts and how to keep your family safe during severe storms.\n",
            "\n",
            "[Paris, IDF, FR Current Weather - The Weather Network](https://www.theweathernetwork.com/en/city/fr/ile-de-france/paris/current)\n",
            "Get Paris, IDF, FR current weather report with temperature, feels like, wind, humidity, pressure, UV and more from TheWeatherNetwork.com.\n",
            "\n",
            "[Weather Tenday](https://weather.com/weather/tenday/l/d2a540efb4e9604b3c1d01b7851a1d9d2ab4c7b3ba428e5799936ac54404c035)\n",
            "Be prepared with the most accurate 10-day forecast for Paris, Île-de-France, France with highs, lows, chance of precipitation from The Weather Channel and Weather.com\n",
            "\n",
            "[Yr - Paris - Hourly weather forecast](https://www.yr.no/en/forecast/hourly-table/2-2988507/France/Île-de-France+Region/Paris+Department/Paris?i=0)\n",
            "Today's weather forecast for Paris by the hour.\n"
          ]
        }
      ],
      "source": [
        "model = InferenceClientModel()\n",
        "agent = CodeAgent(\n",
        "\ttools=[DuckDuckGoSearchTool()],\n",
        "\tmodel=model\n",
        "\t)\n",
        "result = agent.run(\"What is the current weather in Paris?\")\n",
        "print(result)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that you know how to wire up an agent with tools and models, let’s look at the most visual use case: agents that operate computers by looking at the screen, just like you do."
      ],
      "metadata": {
        "id": "BnOvCsxlVpjJ"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k2v1izEAuna2"
      },
      "source": [
        "## Computer Use Agents\n",
        "### Using Holo2-Localization"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Automating computer tasks have always been either hardcoded with automation tools like Playwright or through LLMs parsing HTML codes behind websites. This is very brittle in many ways: websites can have images that can never be parsed to pass to LLM, the hardcoded website automations break when website layouts change and many other failures occur. Luckily, to operate over graphical user interfaces (GUIs), we can use VLMs as well."
      ],
      "metadata": {
        "id": "UPc-e2LvVq3j"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 8,
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        },
        "id": "BYIcgRP7usM9",
        "outputId": "2f09d041-fbe5-4da9-b512-ac9f8cdb4d9f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json: 0.00B [00:00, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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          "output_type": "display_data",
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          "data": {
            "text/plain": [
              "Downloading (incomplete total...): 0.00B [00:00, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bf4ff63c94f1444ea3434c02ca763fe9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Fetching 2 files:   0%|          | 0/2 [00:00\\n\\n\\n\\n{\"x\":551,\"y\":397}']\n"
          ]
        }
      ],
      "source": [
        "text_prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, thinking=False)\n",
        "inputs = processor(\n",
        "    text=[text_prompt],\n",
        "    images=[processed_image],\n",
        "    padding=True,\n",
        "    return_tensors=\"pt\",\n",
        ").to(model.device)\n",
        "generated_ids = model.generate(**inputs, max_new_tokens=32)\n",
        "decoded_output = processor.decode(generated_ids, skip_special_tokens=True)\n",
        "print(decoded_output)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let’s take a look at the decoded content stripped of special tokens and the input.\n",
        "\n"
      ],
      "metadata": {
        "id": "XKjIvHymZse3"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tQnbwdy5v06C",
        "outputId": "76bc89a9-738b-4505-d405-45a89ad280f7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['{\"x\":551,\"y\":397}']\n"
          ]
        }
      ],
      "source": [
        "# Let’s take a look at the decoded content stripped of special tokens and the input.\n",
        "input_len = len(inputs[\"input_ids\"][0])\n",
        "outs = processor.batch_decode(generated_ids[:, input_len:], skip_special_tokens=True)\n",
        "print(outs)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can validate the action like follows."
      ],
      "metadata": {
        "id": "k4_lUaWSZuOl"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sGt99U-1v4FX",
        "outputId": "11e7d913-b7aa-4109-9aa6-a8ba84dcfb16"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "action: x=551 y=397\n"
          ]
        }
      ],
      "source": [
        "action = ClickCoordinates.model_validate_json(outs[0])\n",
        "print(\"action:\", action)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, let’s visualize the output.\n",
        "\n"
      ],
      "metadata": {
        "id": "Mi0uGoNDZwNu"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651
        },
        "id": "1Dvu2Ddav8zi",
        "outputId": "ac32e8c0-a269-4354-bebd-d9089930415c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted action: Click on 'Book a hotel in Paris on August 3rd for 3 nights' at coordinates (934.4960000000001, 343.00800000000004)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "
"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Visualizing the result:\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patches as patches\n",
        "from matplotlib.figure import Figure\n",
        "from matplotlib.axes import Axes\n",
        "\n",
        "def visualize_click(action, processed_img, task):\n",
        "    fig, ax = plt.subplots(1, 1, figsize=(12, 8))\n",
        "    ax.imshow(processed_img)\n",
        "    x = action.x / 1000 * processed_img.width\n",
        "    y = action.y / 1000 * processed_img.height\n",
        "    print(f\"Predicted action: Click on '{task}' at coordinates ({x}, {y})\")\n",
        "\n",
        "    ax.plot(x, y, \"bo\", markersize=10, markeredgewidth=3)\n",
        "\n",
        "    circle = patches.Circle((x, y), 10, linewidth=2, edgecolor=\"red\", facecolor=\"none\")\n",
        "    ax.add_patch(circle)\n",
        "\n",
        "    ax.annotate(\n",
        "            f\"Click: {task}\",\n",
        "            xy=(x, y),\n",
        "            xytext=(x + 20, y - 20),\n",
        "            bbox=dict(boxstyle=\"round,pad=0.3\", facecolor=\"yellow\", alpha=0.7),\n",
        "            fontsize=10,\n",
        "        )\n",
        "    ax.axis(\"off\")\n",
        "    plt.tight_layout()\n",
        "    return fig, ax\n",
        "\n",
        "fig, ax = visualize_click(action, processed_image, task=task)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gC2zOUzMwFQa"
      },
      "source": [
        "### Using ScaleCUA for Agentic Computer Use\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ScaleCUA is an agentic model that can do many actions when given a task. It’s based on Qwen2.5-VL, and comes in 3B, 7B and 32B. It comes with its own configuration, so there’s a slight adjustment when loading the processor."
      ],
      "metadata": {
        "id": "FeYWnTMwZy4i"
      }
    },
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          "data": {
            "text/plain": [
              "Fetching 2 files:   0%|          | 0/2 [00:00 None:\n",
        "\"\"\"Clicks on the screen at the specified coordinates. The `x` and `y` parameter specify where the mouse event occurs. If not provided, the current mouse position is used. The `clicks` parameter specifies how many times to click, and the `button` parameter specifies which mouse button to use ('left', 'right', or 'middle').\"\"\"\n",
        "pass\n",
        "def doubleClick(\n",
        "x: float | None = None,\n",
        "y: float | None = None,\n",
        "button: str = \"left\",\n",
        ") -> None:\n",
        "\"\"\"Performs a double click. This is a wrapper function for click(x, y, 2, 'left').\"\"\"\n",
        "pass\n",
        "def rightClick(x: float | None = None, y: float | None = None) -> None:\n",
        "\"\"\"Performs a right mouse button click. This is a wrapper function for click(x, y, 1, 'right').\"\"\"\n",
        "pass\n",
        "def moveTo(x: float, y: float) -> None:\n",
        "\"\"\"Move the mouse to the specified coordinates.\"\"\"\n",
        "pass\n",
        "def dragTo(\n",
        "x: float | None = None, y: float | None = None, button: str = \"left\"\n",
        ") -> None:\n",
        "\"\"\"Performs a drag-to action with optional `x` and `y` coordinates and button.\"\"\"\n",
        "pass\n",
        "def swipe(\n",
        "from_coord: tuple[float, float] | None = None,\n",
        "to_coord: tuple[float, float] | None = None,\n",
        "direction: str = \"up\",\n",
        "amount: float = 0.5,\n",
        ") -> None:\n",
        "\"\"\"Performs a swipe action on the screen. The `from_coord` and `to_coord` specify the starting and ending coordinates of the swipe. If `to_coord` is not provided, the `direction` and `amount` parameters are used to determine the swipe direction and distance. The `direction` can be 'up', 'down', 'left', or 'right', and the `amount` specifies how far to swipe relative to the screen size (0 to 1).\"\"\"\n",
        "pass\n",
        "def long_press(x: float, y: float, duration: int = 1) -> None:\n",
        "\"\"\"Long press on the screen at the specified coordinates. The `duration` specifies how long to hold the press in seconds.\"\"\"\n",
        "pass\n",
        "## Input Specification\n",
        "- Screenshot of the current screen + task description\n",
        "## Output Format\n",
        "\n",
        "[A set of executable action command]\n",
        "\n",
        "## Note\n",
        "- Avoid action(s) that would lead to invalid states.\n",
        "- The generated action(s) must exist within the defined action space.\n",
        "- The generated action(s) should be enclosed within  tags.'''\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you can see, this model isn’t limited to only clicking. We add the system prompt to the messages as follows. We give the same task to this model with the same image as the previous example."
      ],
      "metadata": {
        "id": "b7JqQMpgZ2XN"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "of3x3epHwTLv"
      },
      "outputs": [],
      "source": [
        "messages = [\n",
        "    {\n",
        "      \"role\": \"system\",\n",
        "      \"content\":[\n",
        "        {\n",
        "          \"type\": \"text\",\n",
        "          \"text\": SYSTEM_PROMPT,\n",
        "        }\n",
        "      ]\n",
        "    },\n",
        "    {\n",
        "        \"role\": \"user\",\n",
        "        \"content\": [\n",
        "            {\n",
        "                \"type\": \"image\",\n",
        "                \"image\": \"https://huggingface.co/datasets/vlmbook/images/resolve/main/calendar.jpg\",\n",
        "            },\n",
        "            {\"type\": \"text\", \"text\": \"Book a hotel from July 3rd to July 6th.\"},\n",
        "        ],\n",
        "    }\n",
        "]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can preprocess and infer as usual.\n",
        "\n"
      ],
      "metadata": {
        "id": "xLXE5STsZ4X3"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vK8WPx5TwV_o",
        "outputId": "64534f71-7c21-46b7-841b-2e0826a7f4b3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['\\nclick(x=528, y=339)\\nclick(x=635, y=339)\\n']\n"
          ]
        }
      ],
      "source": [
        "inputs = processor.apply_chat_template(\n",
        "    messages, tokenize=True, return_dict=True, add_generation_prompt=True, return_tensors=\"pt\"\n",
        ")\n",
        "inputs = inputs.to(\"cuda\")\n",
        "generated_ids = model.generate(**inputs, max_new_tokens=128)\n",
        "input_len = len(inputs[\"input_ids\"][0])\n",
        "action_str = processor.batch_decode(generated_ids[:, input_len:], skip_special_tokens=True)\n",
        "print(action_str)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you can see, this model adds two clicks since we have given two dates. We can parse this output and visualize the clicks."
      ],
      "metadata": {
        "id": "Ut7rAuftZ5zR"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "kfZUaudqwYnN"
      },
      "outputs": [],
      "source": [
        "import re\n",
        "def parse_clicks_from_action_block(action_text):\n",
        "    action_text = \"\\n\".join(action_text)\n",
        "    pattern = r\"click\\s*\\(\\s*x\\s*=\\s*(\\d+)\\s*,\\s*y\\s*=\\s*(\\d+)\\s*\\)\"\n",
        "    matches = re.findall(pattern, action_text)\n",
        "    clicks = [(int(x), int(y)) for x, y in matches]\n",
        "    return clicks\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "qSdS8xbuwbaS",
        "outputId": "6901a4b4-f05f-441c-ee62-ce9786a72c91"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(528, 339), (635, 339)]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "
"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def visualize_clicks_on_image(\n",
        "    image,\n",
        "    clicks,\n",
        "):\n",
        "    plt.figure(figsize=(6, 6))\n",
        "    plt.imshow(image)\n",
        "    ax = plt.gca()\n",
        "    if clicks:\n",
        "        xs, ys = zip(*clicks)\n",
        "        ax.scatter(xs, ys, s=80, edgecolors=\"red\", facecolors=\"none\", linewidths=2)\n",
        "        for i, (x, y) in enumerate(clicks, start=1):\n",
        "            ax.text(\n",
        "                x,\n",
        "                y,\n",
        "                str(i),\n",
        "                color=\"yellow\",\n",
        "                fontsize=10,\n",
        "                ha=\"center\",\n",
        "                va=\"center\",\n",
        "                weight=\"bold\",\n",
        "            )\n",
        "    ax.set_axis_off()\n",
        "    plt.show()\n",
        "\n",
        "clicks = parse_clicks_from_action_block(action_str)\n",
        "print(clicks)\n",
        "image_url = \"https://huggingface.co/datasets/vlmbook/images/resolve/main/calendar.jpg\"\n",
        "image = Image.open(requests.get(image_url, stream=True).raw)\n",
        "\n",
        "visualize_clicks_on_image(\n",
        "    image=image,\n",
        "    clicks=clicks,\n",
        ")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note:** This chapter ends with vision browser example, this should be ran out of notebook."
      ],
      "metadata": {
        "id": "PLIbuz6eaBi2"
      }
    }
  ],
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      "machine_shape": "hm"
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