{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abf90ca5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import requests\n",
    "from langchain_openai import ChatOpenAI\n",
    "from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4299f37",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "sys.path.append(os.path.abspath(\"../src\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73b38064",
   "metadata": {},
   "outputs": [],
   "source": [
    "from agent import SmartAgent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f925adb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- Constants ---\n",
    "DEFAULT_API_URL = \"https://agents-course-unit4-scoring.hf.space\"\n",
    "HUGGINGFACEHUB_API_TOKEN = os.getenv(\"HUGGINGFACEHUB_API_TOKEN\")\n",
    "OPENAI_API_KEY = os.getenv(\"OPENAI_API_KEY\")\n",
    "\n",
    "REPO_ID = \"meta-llama/Llama-3.1-8B-Instruct\"\n",
    "PROVIDER_TYPE = \"openai\"  # \"openai\" or \"huggingface\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "541ebb1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "TAVILY_API_KEY = os.getenv(\"TAVILY_API_KEY\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "320e99b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "api_url = DEFAULT_API_URL\n",
    "questions_url = f\"{api_url}/questions\"\n",
    "submit_url = f\"{api_url}/submit\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f31b88db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Instantiate Agent\n",
    "try:\n",
    "    if PROVIDER_TYPE == \"huggingface\":\n",
    "        llm = HuggingFaceEndpoint(\n",
    "            repo_id=REPO_ID,\n",
    "            huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,\n",
    "        )\n",
    "        chat = ChatHuggingFace(llm=llm, verbose=True)\n",
    "    elif PROVIDER_TYPE == \"openai\":\n",
    "        chat = ChatOpenAI(model=\"gpt-4o\", temperature=0.2)\n",
    "    else:\n",
    "        print(f\"Provider {PROVIDER_TYPE} not supported.\")\n",
    "\n",
    "    agent = SmartAgent(chat)\n",
    "\n",
    "except Exception as e:\n",
    "    print(f\"Error instantiating agent: {e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4d18d12",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. Fetch Questions\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}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9627e327",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. Run your Agent\n",
    "results_log = []\n",
    "answers_payload = []\n",
    "\n",
    "item = questions_data[0]\n",
    "print(f\"Running agent on question: {item}\")\n",
    "\n",
    "task_id = item.get(\"task_id\")\n",
    "question_text = item.get(\"question\")\n",
    "file_name = item.get(\"file_name\")\n",
    "if file_name != \"\":\n",
    "    files_url = f\"{api_url}/files/{task_id}\"\n",
    "    file = requests.get(files_url, timeout=15)\n",
    "    with open(file_name, \"wb\") as f:\n",
    "        f.write(file.content)\n",
    "    print(f\"Downloaded {files_url}.\")\n",
    "if not task_id or question_text is None:\n",
    "    print(f\"Skipping item with missing task_id or question: {item}\")\n",
    "try:\n",
    "    submitted_answer = agent(question_text, file_name)\n",
    "    answers_payload.append({\"task_id\": task_id, \"submitted_answer\": submitted_answer})\n",
    "    results_log.append(\n",
    "        {\n",
    "            \"Task ID\": task_id,\n",
    "            \"Question\": question_text,\n",
    "            \"Submitted Answer\": submitted_answer,\n",
    "        }\n",
    "    )\n",
    "except Exception as e:\n",
    "    print(f\"Error running agent on task {task_id}: {e}\")\n",
    "    results_log.append(\n",
    "        {\n",
    "            \"Task ID\": task_id,\n",
    "            \"Question\": question_text,\n",
    "            \"Submitted Answer\": f\"AGENT ERROR: {e}\",\n",
    "        }\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "699cba0f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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