chatbot_development.ipynb

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   "source": [
    "# Chatbot Development\n",
    "\n",
    "Use this notebook to load the model and then initialize, update, and test the chatbot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup and Imports"
   ]
  },
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   "execution_count": 2,
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     "text": [
      "/Users/rishikabansal/Git/6.s041-chatbot/6s041_chatbot/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from huggingface_hub import login\n",
    "\n",
    "\n",
    "from src.chat import SchoolChatbot\n",
    "from config import BASE_MODEL, MY_MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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     "data": {
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       "model_id": "f9bdccb75b8447a886ac8ca2fdfed506",
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       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
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   ],
   "source": [
    "\"\"\"\n",
    "TODO: Add your Hugging Face token\n",
    "Options:\n",
    "1. Use login() and enter token when prompted. It won't ask for your token if you already logged in using the command: huggingface-cli login in the terminal.\n",
    "2. Set environment variable HUGGINGFACE_TOKEN\n",
    "3. Pass token directly (not recommended for shared notebooks)\n",
    "\"\"\"\n",
    "\n",
    "login()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize and test chatbot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Create chatbot instance using chat.py\n",
    "\"\"\"\n",
    "chatbot = SchoolChatbot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Question: I live in Jamaica Plain and want to send my child to a school that offers Spanish classes. What schools are available?\n",
      "Response: To help you find eligible schools, I need your address or zip code in Boston. What is your address?\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Test out generating some responses from the chatbot.\n",
    "Inference time\n",
    "\"\"\"\n",
    "test_question = \"I live in Jamaica Plain and want to send my child to a school that offers Spanish classes. What schools are available?\"\n",
    "\n",
    "print(f\"\\nQuestion: {test_question}\")\n",
    "response = chatbot.get_response(test_question)\n",
    "print(f\"Response: {response}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TODO: Update pre-trained Llama to be a school choice chatbot\n",
    "\n",
    "This part is up to you! You might want to finetune the model, simply make a really good system prompt, use RAG, provide the model boston school choice data in-context, etc. Be creative!\n",
    "\n",
    "You can also feel free to do this in another script and then evaluate the model here.\n",
    "\n",
    "Tips:\n",
    "- HuggingFace has built-in methods to finetune models, if you choose that route. Take advantage of those methods! You can then save your new, finetuned model in the HuggingFace Hub. Change MY_MODEL in config.py to the name of the model in the hub to make your chatbot use it.\n",
    "- You may also want to consider LoRA if you choose finetuning."
   ]
  }
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