Upload simple_agent.ipynb

simple_agent.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "94c8b42c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "from langchain.chat_models import init_chat_model\n",
+    "\n",
+    "if not os.environ.get(\"GOOGLE_API_KEY\"):\n",
+    "  os.environ[\"GOOGLE_API_KEY\"] = getpass.getpass(\"Enter API key for Google Gemini: \")\n",
+    "llm = init_chat_model(\"gemini-2.5-flash-preview-04-17\", model_provider=\"google_genai\", temperature=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "ce83aa2a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "from langchain.document_loaders import WebBaseLoader\n",
+    "from langchain_community.tools import DuckDuckGoSearchResults\n",
+    "def download_web_pages(query: str) -> str:\n",
+    "    \"\"\"\n",
+    "    Performs a web search using the given query, downloads the content of two relevant web pages,\n",
+    "    and returns their combined content as a raw string.\n",
+    "\n",
+    "    This is useful when the task requires analysis of web page content, such as retrieving poems, \n",
+    "    changelogs, or other textual resources.\n",
+    "\n",
+    "    Args:\n",
+    "        query (str): The search query.\n",
+    "\n",
+    "    Returns:\n",
+    "        str: The combined raw text content of the two retrieved web pages.\n",
+    "    \"\"\"\n",
+    "    search_engine = DuckDuckGoSearchResults(output_format=\"list\", num_results=2)\n",
+    "    page_urls = [url[\"link\"] for url in search_engine(query)]\n",
+    "\n",
+    "    loader = WebBaseLoader(web_paths=(page_urls))\n",
+    "    docs = loader.load()\n",
+    "\n",
+    "    combined_text = \"\\n\\n\".join(doc.page_content[:15000] for doc in docs)\n",
+    "\n",
+    "    # Clean up excessive newlines, spaces and strip leading/trailing whitespace\n",
+    "    cleaned_text = re.sub(r'\\n{3,}', '\\n\\n', combined_text).strip()\n",
+    "    cleaned_text = re.sub(r'[ \\t]{6,}', ' ', cleaned_text)\n",
+    "\n",
+    "    # Strip leading/trailing whitespace\n",
+    "    cleaned_text = cleaned_text.strip()\n",
+    "    return cleaned_text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "586df437",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.retrievers import WikipediaRetriever\n",
+    "\n",
+    "def wikipedia_search(query: str) -> str:\n",
+    "    \"\"\"\n",
+    "    Searches for a Wikipedia articles using the provided query and returns the content of the corresponding Wikipedia pages.\n",
+    "\n",
+    "    Args:\n",
+    "        query (str): The search term to look up on Wikipedia.\n",
+    "\n",
+    "    Returns:\n",
+    "        str: The text content of the Wikipedia articles related to the query.\n",
+    "    \"\"\"\n",
+    "    retriever = WikipediaRetriever()\n",
+    "    docs = retriever.invoke(query)\n",
+    "    combined_text = \"\\n\\n\".join(doc.page_content for doc in docs)\n",
+    "    return combined_text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "fac2cb01",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google import genai\n",
+    "from google.genai import types\n",
+    "\n",
+    "def youtube_viewer(youtube_url: str, question: str) -> str:\n",
+    "    \"\"\"\n",
+    "    Analyzes a YouTube video from the provided URL and returns an answer \n",
+    "    to the given question based on the analysis results.\n",
+    "\n",
+    "    Args:\n",
+    "        youtube_url (str): The URL of the YouTube video, in the format \n",
+    "            \"https://www.youtube.com/...\".\n",
+    "        question (str): A question related to the content of the video.\n",
+    "\n",
+    "    Returns:\n",
+    "        str: An answer to the question based on the video's content.\n",
+    "    \"\"\"\n",
+    "    client = genai.Client()\n",
+    "    response = client.models.generate_content(\n",
+    "        model='models/gemini-2.5-flash-preview-04-17',\n",
+    "        contents=types.Content(\n",
+    "            parts=[\n",
+    "                types.Part(\n",
+    "                    file_data=types.FileData(file_uri=youtube_url)\n",
+    "                ),\n",
+    "                types.Part(text=question)\n",
+    "            ]\n",
+    "        )\n",
+    "    )\n",
+    "    return response.text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "4c498a3d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Key 'title' is not supported in schema, ignoring\n",
+      "Key 'title' is not supported in schema, ignoring\n",
+      "Key 'title' is not supported in schema, ignoring\n"
+     ]
+    }
+   ],
+   "source": [
+    "tools = [download_web_pages, wikipedia_search, youtube_viewer]\n",
+    "\n",
+    "# Bind the tools to the agent\n",
+    "llm_with_tools = llm.bind_tools(tools)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "10628e0f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import TypedDict, Annotated, Optional\n",
+    "from langchain_core.messages import AnyMessage\n",
+    "from langgraph.graph.message import add_messages\n",
+    "\n",
+    "class AgentState(TypedDict):\n",
+    "    \"\"\"Agent state for the graph.\"\"\"\n",
+    "    input_file: Optional[str]\n",
+    "    messages: Annotated[list[AnyMessage], add_messages]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "f1594fb9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.messages import SystemMessage, HumanMessage\n",
+    "\n",
+    "def assistant(state: AgentState):\n",
+    "  sys_msg = SystemMessage(\n",
+    "    content=\n",
+    "    \"\"\"\n",
+    "You are a helpful assistant tasked with answering questions using a set of tools. When given a question, follow these steps:\n",
+    "1. Create a clear, step-by-step plan to solve the question.\n",
+    "2. If a tool is necessary, select the most appropriate tool based on its functionality. If one tool isn't working, use another with similar functionality.\n",
+    "3. Execute your plan and provide the response in the following format:\n",
+    "\n",
+    "FINAL ANSWER: [YOUR FINAL ANSWER]\n",
+    "\n",
+    "Your final answer should be:\n",
+    "\n",
+    "- A number (without commas or units unless explicitly requested),\n",
+    "- A short string (avoid articles, abbreviations, and use plain text for digits unless otherwise specified),\n",
+    "- A comma-separated list (apply the formatting rules above for each element, with exactly one space after each comma).\n",
+    "\n",
+    "Ensure that your answer is concise and follows the task instructions strictly. If the answer is more complex, break it down in a way that follows the format.\n",
+    "Begin your response with \"FINAL ANSWER: \" followed by the answer, and nothing else.\n",
+    "    \"\"\"\n",
+    "  )\n",
+    "\n",
+    "  return {\n",
+    "    \"messages\": [llm_with_tools.invoke([sys_msg] + state[\"messages\"])],\n",
+    "    \"input_file\": state[\"input_file\"]\n",
+    "  }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "b36bc36c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from langgraph.prebuilt import ToolNode, tools_condition\n",
+    "from IPython.display import Image, display\n",
+    "from langgraph.graph import StateGraph, START\n",
+    "\n",
+    "# Build the state graph\n",
+    "# The graph\n",
+    "builder = StateGraph(AgentState)\n",
+    "\n",
+    "# Define nodes: these do the work\n",
+    "builder.add_node(\"assistant\", assistant)\n",
+    "builder.add_node(\"tools\", ToolNode(tools))\n",
+    "# Define edges: these determine how the control flow moves\n",
+    "builder.add_edge(START, \"assistant\")\n",
+    "builder.add_conditional_edges(\n",
+    "    \"assistant\",\n",
+    "    tools_condition,\n",
+    ")\n",
+    "builder.add_edge(\"tools\", \"assistant\")\n",
+    "react_graph = builder.compile()\n",
+    "# Show the butler's thought process\n",
+    "display(Image(react_graph.get_graph(xray=True).draw_mermaid_png()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "ead8e45f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed21ed00ada141289cbee25b93029435",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from huggingface_hub import login\n",
+    "\n",
+    "# This will prompt you for your token\n",
+    "login()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "b4b1299d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Logged in as: SpyFox\n"
+     ]
+    }
+   ],
+   "source": [
+    "from huggingface_hub import whoami\n",
+    "\n",
+    "info = whoami()\n",
+    "username = info[\"name\"]\n",
+    "\n",
+    "print(f\"Logged in as: {username}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "45e5f7ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DEFAULT_API_URL = \"https://agents-course-unit4-scoring.hf.space\"    \n",
+    "api_url = DEFAULT_API_URL\n",
+    "questions_url = f\"{api_url}/questions\"\n",
+    "submit_url = f\"{api_url}/submit\"\n",
+    "files_url = f\"{api_url}/files\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "af2e50c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fetching questions from: https://agents-course-unit4-scoring.hf.space/questions\n",
+      "Fetched 20 questions.\n",
+      "{'task_id': 'cabe07ed-9eca-40ea-8ead-410ef5e83f91', 'question': \"What is the surname of the equine veterinarian mentioned in 1.E Exercises from the chemistry materials licensed by Marisa Alviar-Agnew & Henry Agnew under the CK-12 license in LibreText's Introductory Chemistry materials as compiled 08/21/2023?\", 'Level': '1', 'file_name': ''}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "print(f\"Fetching questions from: {questions_url}\")\n",
+    "try:\n",
+    "    response = requests.get(questions_url, timeout=15)\n",
+    "    response.raise_for_status()\n",
+    "    questions_data = response.json()\n",
+    "    if not questions_data:\n",
+    "          print(\"Fetched questions list is empty.\")\n",
+    "    print(f\"Fetched {len(questions_data)} questions.\")\n",
+    "except requests.exceptions.RequestException as e:\n",
+    "    print(f\"Error fetching questions: {e}\")\n",
+    "except requests.exceptions.JSONDecodeError as e:\n",
+    "      print(f\"Error decoding JSON response from questions endpoint: {e}\")\n",
+    "      print(f\"Response text: {response.text[:500]}\")\n",
+    "except Exception as e:\n",
+    "    print(f\"An unexpected error occurred fetching questions: {e}\")\n",
+    "\n",
+    "print(questions_data[7])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "3c6996e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running agent on 20 questions...\n",
+      "Question: How many studio albums were published by Mercedes Sosa between 2000 and 2009 (included)? You can use the latest 2022 version of english wikipedia.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\wasyl\\AppData\\Local\\Temp\\ipykernel_22488\\81366616.py:19: LangChainDeprecationWarning: The method `BaseTool.__call__` was deprecated in langchain-core 0.1.47 and will be removed in 1.0. Use :meth:`~invoke` instead.\n",
+      "  page_urls = [url[\"link\"] for url in search_engine(query)]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Answer: 3\n",
+      "Question: In the video https://www.youtube.com/watch?v=L1vXCYZAYYM, what is the highest number of bird species to be on camera simultaneously?\n",
+      "Answer: 3\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[30], line 28\u001b[0m\n\u001b[0;32m     26\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAnswer: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msubmitted_answer\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     27\u001b[0m     answers_payload\u001b[38;5;241m.\u001b[39mappend({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtask_id\u001b[39m\u001b[38;5;124m\"\u001b[39m: task_id, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msubmitted_answer\u001b[39m\u001b[38;5;124m\"\u001b[39m: submitted_answer})\n\u001b[1;32m---> 28\u001b[0m     time\u001b[38;5;241m.\u001b[39msleep(\u001b[38;5;241m10\u001b[39m)  \u001b[38;5;66;03m# Sleep for 10 seconds to avoid rate limiting\u001b[39;00m\n\u001b[0;32m     29\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     30\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mError running agent on task \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtask_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "import time\n",
+    "\n",
+    "def extract_after_final_answer(text):\n",
+    "    keyword = \"FINAL ANSWER: \"\n",
+    "    index = text.find(keyword)\n",
+    "    if index != -1:\n",
+    "        return text[index + len(keyword):]\n",
+    "    else:\n",
+    "        return \"\"\n",
+    "\n",
+    "answers_payload = []\n",
+    "\n",
+    "print(f\"Running agent on {len(questions_data)} questions...\")\n",
+    "for item in questions_data:\n",
+    "    task_id = item.get(\"task_id\")\n",
+    "    question_text = item.get(\"question\")\n",
+    "    print(f\"Question: {question_text}\")\n",
+    "    messages = [HumanMessage(content=question_text)]\n",
+    "\n",
+    "    if not task_id or question_text is None:\n",
+    "        print(f\"Skipping item with missing task_id or question: {item}\")\n",
+    "        continue\n",
+    "    try:\n",
+    "        submitted_answer = react_graph.invoke({\"messages\": messages, \"input_file\": None})\n",
+    "        submitted_answer = extract_after_final_answer(submitted_answer['messages'][-1].content)\n",
+    "        print(f\"Answer: {submitted_answer}\")\n",
+    "        answers_payload.append({\"task_id\": task_id, \"submitted_answer\": submitted_answer})\n",
+    "        time.sleep(10)  # Sleep for 10 seconds to avoid rate limiting\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error running agent on task {task_id}: {e}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "8e47414a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Agent finished. Submitting 20 answers for user 'SpyFox'...\n"
+     ]
+    }
+   ],
+   "source": [
+    "submission_data = {\"username\": username.strip(), \"agent_code\": \"https://huggingface.co/spaces/SpyFox/agents_final_test/tree/main\", \"answers\": answers_payload}\n",
+    "status_update = f\"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'...\"\n",
+    "print(status_update)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "a086f73f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Submitting 20 answers to: https://agents-course-unit4-scoring.hf.space/submit\n",
+      "Submission successful.\n",
+      "Submission Successful!\n",
+      "User: SpyFox\n",
+      "Overall Score: 45.0% (9/20 correct)\n",
+      "Message: Score calculated successfully: 9/20 total questions answered correctly (20 valid tasks attempted). High score updated on leaderboard.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Submitting {len(answers_payload)} answers to: {submit_url}\")\n",
+    "\n",
+    "response = requests.post(submit_url, json=submission_data, timeout=60)\n",
+    "response.raise_for_status()\n",
+    "result_data = response.json()\n",
+    "final_status = (\n",
+    "    f\"Submission Successful!\\n\"\n",
+    "    f\"User: {result_data.get('username')}\\n\"\n",
+    "    f\"Overall Score: {result_data.get('score', 'N/A')}% \"\n",
+    "    f\"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\\n\"\n",
+    "    f\"Message: {result_data.get('message', 'No message received.')}\"\n",
+    ")\n",
+    "print(\"Submission successful.\")\n",
+    "print(final_status)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}