{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jOTv7ORm8ylW",
    "outputId": "846808d2-77ca-4e82-8038-2caa68997915"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: sentence-transformers in /usr/local/lib/python3.11/dist-packages (3.4.1)\n",
      "Collecting faiss-cpu\n",
      "  Downloading faiss_cpu-1.11.0-cp311-cp311-manylinux_2_28_x86_64.whl.metadata (4.8 kB)\n",
      "Requirement already satisfied: transformers<5.0.0,>=4.41.0 in /usr/local/lib/python3.11/dist-packages (from sentence-transformers) (4.51.3)\n",
      "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (from sentence-transformers) (4.67.1)\n",
      "Requirement already satisfied: torch>=1.11.0 in /usr/local/lib/python3.11/dist-packages (from sentence-transformers) (2.6.0+cu124)\n",
      "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (from sentence-transformers) (1.6.1)\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (from sentence-transformers) (1.15.2)\n",
      "Requirement already satisfied: huggingface-hub>=0.20.0 in /usr/local/lib/python3.11/dist-packages (from sentence-transformers) (0.30.2)\n",
      "Requirement already satisfied: Pillow in /usr/local/lib/python3.11/dist-packages (from sentence-transformers) (11.2.1)\n",
      "Requirement already satisfied: numpy<3.0,>=1.25.0 in /usr/local/lib/python3.11/dist-packages (from faiss-cpu) (2.0.2)\n",
      "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from faiss-cpu) (24.2)\n",
      "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers) (3.18.0)\n",
      "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers) (2025.3.2)\n",
      "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers) (6.0.2)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers) (2.32.3)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers) (4.13.2)\n",
      "Requirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from torch>=1.11.0->sentence-transformers) (3.4.2)\n",
      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch>=1.11.0->sentence-transformers) (3.1.6)\n",
      "Collecting nvidia-cuda-nvrtc-cu12==12.4.127 (from torch>=1.11.0->sentence-transformers)\n",
      "  Downloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
      "Collecting nvidia-cuda-runtime-cu12==12.4.127 (from torch>=1.11.0->sentence-transformers)\n",
      "  Downloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
      "Collecting nvidia-cuda-cupti-cu12==12.4.127 (from torch>=1.11.0->sentence-transformers)\n",
      "  Downloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
      "Collecting nvidia-cudnn-cu12==9.1.0.70 (from torch>=1.11.0->sentence-transformers)\n",
      "  Downloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
      "Collecting nvidia-cublas-cu12==12.4.5.8 (from torch>=1.11.0->sentence-transformers)\n",
      "  Downloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
      "Collecting nvidia-cufft-cu12==11.2.1.3 (from torch>=1.11.0->sentence-transformers)\n",
      "  Downloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
      "Collecting nvidia-curand-cu12==10.3.5.147 (from torch>=1.11.0->sentence-transformers)\n",
      "  Downloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
      "Collecting nvidia-cusolver-cu12==11.6.1.9 (from torch>=1.11.0->sentence-transformers)\n",
      "  Downloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
      "Collecting nvidia-cusparse-cu12==12.3.1.170 (from torch>=1.11.0->sentence-transformers)\n",
      "  Downloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
      "Requirement already satisfied: nvidia-cusparselt-cu12==0.6.2 in /usr/local/lib/python3.11/dist-packages (from torch>=1.11.0->sentence-transformers) (0.6.2)\n",
      "Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch>=1.11.0->sentence-transformers) (2.21.5)\n",
      "Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.11.0->sentence-transformers) (12.4.127)\n",
      "Collecting nvidia-nvjitlink-cu12==12.4.127 (from torch>=1.11.0->sentence-transformers)\n",
      "  Downloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
      "Requirement already satisfied: triton==3.2.0 in /usr/local/lib/python3.11/dist-packages (from torch>=1.11.0->sentence-transformers) (3.2.0)\n",
      "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch>=1.11.0->sentence-transformers) (1.13.1)\n",
      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch>=1.11.0->sentence-transformers) (1.3.0)\n",
      "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.11/dist-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers) (2024.11.6)\n",
      "Requirement already satisfied: tokenizers<0.22,>=0.21 in /usr/local/lib/python3.11/dist-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers) (0.21.1)\n",
      "Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.11/dist-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers) (0.5.3)\n",
      "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn->sentence-transformers) (1.4.2)\n",
      "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn->sentence-transformers) (3.6.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch>=1.11.0->sentence-transformers) (3.0.2)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers) (3.4.1)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers) (3.10)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers) (2.4.0)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers) (2025.4.26)\n",
      "Downloading faiss_cpu-1.11.0-cp311-cp311-manylinux_2_28_x86_64.whl (31.3 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m31.3/31.3 MB\u001b[0m \u001b[31m72.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hDownloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl (363.4 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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      "\u001b[?25hDownloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (24.6 MB)\n",
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      "\u001b[?25hDownloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (883 kB)\n",
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      "\u001b[?25hDownloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl (664.8 MB)\n",
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      "\u001b[?25hDownloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl (211.5 MB)\n",
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      "\u001b[?25hDownloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl (56.3 MB)\n",
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      "\u001b[?25hDownloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl (127.9 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hDownloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl (207.5 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hDownloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m74.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, faiss-cpu, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12\n",
      "  Attempting uninstall: nvidia-nvjitlink-cu12\n",
      "    Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n",
      "    Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n",
      "      Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n",
      "  Attempting uninstall: nvidia-curand-cu12\n",
      "    Found existing installation: nvidia-curand-cu12 10.3.6.82\n",
      "    Uninstalling nvidia-curand-cu12-10.3.6.82:\n",
      "      Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n",
      "  Attempting uninstall: nvidia-cufft-cu12\n",
      "    Found existing installation: nvidia-cufft-cu12 11.2.3.61\n",
      "    Uninstalling nvidia-cufft-cu12-11.2.3.61:\n",
      "      Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n",
      "  Attempting uninstall: nvidia-cuda-runtime-cu12\n",
      "    Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n",
      "    Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n",
      "      Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n",
      "  Attempting uninstall: nvidia-cuda-nvrtc-cu12\n",
      "    Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n",
      "    Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n",
      "      Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n",
      "  Attempting uninstall: nvidia-cuda-cupti-cu12\n",
      "    Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n",
      "    Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n",
      "      Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n",
      "  Attempting uninstall: nvidia-cublas-cu12\n",
      "    Found existing installation: nvidia-cublas-cu12 12.5.3.2\n",
      "    Uninstalling nvidia-cublas-cu12-12.5.3.2:\n",
      "      Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n",
      "  Attempting uninstall: nvidia-cusparse-cu12\n",
      "    Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n",
      "    Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n",
      "      Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n",
      "  Attempting uninstall: nvidia-cudnn-cu12\n",
      "    Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n",
      "    Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n",
      "      Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n",
      "  Attempting uninstall: nvidia-cusolver-cu12\n",
      "    Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n",
      "    Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n",
      "      Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n",
      "Successfully installed faiss-cpu-1.11.0 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127\n"
     ]
    }
   ],
   "source": [
    "!pip install sentence-transformers faiss-cpu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ggX0OwHF9UHQ",
    "outputId": "2c6a9aa1-2a67-4b86-8f5d-fb8d2af127b7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: transformers in /usr/local/lib/python3.11/dist-packages (4.51.3)\n",
      "Requirement already satisfied: torch in /usr/local/lib/python3.11/dist-packages (2.6.0+cu124)\n",
      "Requirement already satisfied: accelerate in /usr/local/lib/python3.11/dist-packages (1.6.0)\n",
      "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from transformers) (3.18.0)\n",
      "Requirement already satisfied: huggingface-hub<1.0,>=0.30.0 in /usr/local/lib/python3.11/dist-packages (from transformers) (0.30.2)\n",
      "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from transformers) (2.0.2)\n",
      "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from transformers) (24.2)\n",
      "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.11/dist-packages (from transformers) (6.0.2)\n",
      "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.11/dist-packages (from transformers) (2024.11.6)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from transformers) (2.32.3)\n",
      "Requirement already satisfied: tokenizers<0.22,>=0.21 in /usr/local/lib/python3.11/dist-packages (from transformers) (0.21.1)\n",
      "Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.11/dist-packages (from transformers) (0.5.3)\n",
      "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.11/dist-packages (from transformers) (4.67.1)\n",
      "Requirement already satisfied: typing-extensions>=4.10.0 in /usr/local/lib/python3.11/dist-packages (from torch) (4.13.2)\n",
      "Requirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from torch) (3.4.2)\n",
      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch) (3.1.6)\n",
      "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from torch) (2025.3.2)\n",
      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch) (12.4.127)\n",
      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch) (12.4.127)\n",
      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch) (12.4.127)\n",
      "Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /usr/local/lib/python3.11/dist-packages (from torch) (9.1.0.70)\n",
      "Requirement already satisfied: nvidia-cublas-cu12==12.4.5.8 in /usr/local/lib/python3.11/dist-packages (from torch) (12.4.5.8)\n",
      "Requirement already satisfied: nvidia-cufft-cu12==11.2.1.3 in /usr/local/lib/python3.11/dist-packages (from torch) (11.2.1.3)\n",
      "Requirement already satisfied: nvidia-curand-cu12==10.3.5.147 in /usr/local/lib/python3.11/dist-packages (from torch) (10.3.5.147)\n",
      "Requirement already satisfied: nvidia-cusolver-cu12==11.6.1.9 in /usr/local/lib/python3.11/dist-packages (from torch) (11.6.1.9)\n",
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      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch) (1.3.0)\n",
      "Requirement already satisfied: psutil in /usr/local/lib/python3.11/dist-packages (from accelerate) (5.9.5)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch) (3.0.2)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->transformers) (3.4.1)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->transformers) (3.10)\n",
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      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->transformers) (2025.4.26)\n"
     ]
    }
   ],
   "source": [
    "!pip install transformers torch accelerate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-QREyhOV9kgG",
    "outputId": "b23fbb62-3c20-412a-9159-00a02aa1480d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.6.1)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (2.0.2)\n",
      "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.15.2)\n",
      "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.4.2)\n",
      "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (3.6.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install scikit-learn numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "g77wK4cG9ynl",
    "outputId": "70d38ead-7c9f-4130-a7f0-ab3cc175f85e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting streamlit\n",
      "  Downloading streamlit-1.45.0-py3-none-any.whl.metadata (8.9 kB)\n",
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      "  Downloading watchdog-6.0.0-py3-none-manylinux2014_x86_64.whl.metadata (44 kB)\n",
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      "  Downloading pydeck-0.9.1-py2.py3-none-any.whl.metadata (4.1 kB)\n",
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      "\u001b[?25hDownloading pydeck-0.9.1-py2.py3-none-any.whl (6.9 MB)\n",
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      "\u001b[?25hInstalling collected packages: watchdog, pydeck, streamlit\n",
      "Successfully installed pydeck-0.9.1 streamlit-1.45.0 watchdog-6.0.0\n"
     ]
    }
   ],
   "source": [
    "!pip install streamlit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Gq5EvbCs9oRo"
   },
   "outputs": [],
   "source": [
    "!pip install langchain\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Fffg01WPPuZ6",
    "outputId": "5e890fb6-4fe3-443f-fd76-8484fa7da8db"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: cachetools<6,>=4.0 in /usr/local/lib/python3.11/dist-packages (from streamlit) (5.5.2)\n",
      "Requirement already satisfied: click<9,>=7.0 in /usr/local/lib/python3.11/dist-packages (from streamlit) (8.1.8)\n",
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      "Requirement already satisfied: requests<3,>=2.27 in /usr/local/lib/python3.11/dist-packages (from streamlit) (2.32.3)\n",
      "Requirement already satisfied: tenacity<10,>=8.1.0 in /usr/local/lib/python3.11/dist-packages (from streamlit) (9.1.2)\n",
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      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from altair<6,>=4.0->streamlit) (3.1.6)\n",
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      "Requirement already satisfied: narwhals>=1.14.2 in /usr/local/lib/python3.11/dist-packages (from altair<6,>=4.0->streamlit) (1.37.1)\n",
      "Requirement already satisfied: gitdb<5,>=4.0.1 in /usr/local/lib/python3.11/dist-packages (from gitpython!=3.1.19,<4,>=3.0.7->streamlit) (4.0.12)\n",
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      "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->altair<6,>=4.0->streamlit) (3.0.2)\n",
      "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.11/dist-packages (from jsonschema>=3.0->altair<6,>=4.0->streamlit) (25.3.0)\n",
      "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.11/dist-packages (from jsonschema>=3.0->altair<6,>=4.0->streamlit) (2025.4.1)\n",
      "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.11/dist-packages (from jsonschema>=3.0->altair<6,>=4.0->streamlit) (0.36.2)\n",
      "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.11/dist-packages (from jsonschema>=3.0->altair<6,>=4.0->streamlit) (0.24.0)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas<3,>=1.4.0->streamlit) (1.17.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install streamlit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "DTfbb4NLPvjX"
   },
   "outputs": [],
   "source": [
    "!pip install pyngrok"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6BGkJ0gfXnx1"
   },
   "outputs": [],
   "source": [
    "!pip install langchain_community"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cXgX5F4m-De4"
   },
   "outputs": [],
   "source": [
    "# Required libraries\n",
    "import faiss\n",
    "import numpy as np\n",
    "from sentence_transformers import SentenceTransformer\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline\n",
    "import torch\n",
    "import random\n",
    "import pandas as pd\n",
    "from langchain.llms import HuggingFacePipeline, OpenAI\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "_ZEAI5I7-NR6"
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv('train_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "7dFjKU8I-iOO"
   },
   "outputs": [],
   "source": [
    "questions = train['question'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "8IP9T0X0Dn5V"
   },
   "outputs": [],
   "source": [
    "answers = train['answer'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "PUIJ8QqOol5M"
   },
   "outputs": [],
   "source": [
    "qa_pairs = [f\"Q: {q} A: {a}\" for q, a in zip(questions, answers)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MS8Ijjn2oqsk"
   },
   "source": [
    "# Embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 532,
     "referenced_widgets": [
      "c0744d0d2e6e475486aa91815fe030cd",
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      "eb2d43637acc480fb98351cfee01b933",
      "8b2b238eb8d146489750f0542a9ed6ab",
      "62e553d9c2d6482098f8c05b33867934",
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      "1692088e97d44a2a9b3cfb5019339905",
      "a519e6c7d5db4cc08410296d4b377722",
      "6c88a45006e24894a5b6e404b301de11",
      "31ab0b61c5d340ee89df4d86e9b0d469",
      "f7cbc5a5756f4467b13fb9c49730e7b3",
      "0d8fe8a348264704a4201e70c5af9213",
      "030293563a3d42e1b159f801178d34c4",
      "087e45c843a84cf4b32ccd0493743496",
      "21b175dd54ca474691d9b000c9df288b",
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    },
    "id": "zUDBOlkVotBj",
    "outputId": "2c88a282-5579-498a-97f4-823d03ec0c8a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
      "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
      "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
      "You will be able to reuse this secret in all of your notebooks.\n",
      "Please note that authentication is recommended but still optional to access public models or datasets.\n",
      "  warnings.warn(\n"
     ]
    },
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     "data": {
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      },
      "text/plain": [
       "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "README.md:   0%|          | 0.00/10.5k [00:00<?, ?B/s]"
      ]
     },
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    },
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      "text/plain": [
       "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
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     },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n",
      "WARNING:huggingface_hub.file_download:Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n"
     ]
    },
    {
     "data": {
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       "model_id": "ee85efdec6df423d9b0ad2c316b36e62",
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       "model.safetensors:   0%|          | 0.00/90.9M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
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      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "66249acfa75d46c2a8e52cb7735f2631",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ad06dc006d214741946ebc5fcbf00fdf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "embedding_model = SentenceTransformer('all-MiniLM-L6-v2')\n",
    "# question_embeddings = embedding_model.encode(qa_pairs)\n",
    "answer_embeddings = embedding_model.encode(answers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IdjpkF9WozsV"
   },
   "source": [
    "#  Building a FAISS index\n",
    "A **FAISS index** is a fast, efficient data structure for searching similar vectors in high-dimensional space. It's commonly used in **semantic search**, **RAG**, and **recommendation systems**. You store vector embeddings (e.g., from text) and query them to retrieve the most similar items. FAISS supports both exact and approximate search, and scales to millions of vectors with CPU or GPU acceleration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "p5LUYNO0oyS_"
   },
   "outputs": [],
   "source": [
    "index = faiss.IndexFlatL2(answer_embeddings.shape[1])\n",
    "index.add(np.array(answer_embeddings))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5RSKojiwo_Er"
   },
   "source": [
    "# Loading Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6fgrIOQ8EhIP"
   },
   "source": [
    "# Primary Model\n",
    "Load the LLaMA 3.2B instruct model from Hugging Face"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
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    },
    "id": "prQ2Lmu9--54",
    "outputId": "df8c95d5-9aef-4c6f-bcff-951063efc4e7"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "818aa668039d4929a0ae7eb80af29b15",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/54.5k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac7c6a0b173444aba5ffa9f32b5f9d84",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer.json:   0%|          | 0.00/9.09M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b83c6812c32a442cba7365b22efdf0c3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "special_tokens_map.json:   0%|          | 0.00/296 [00:00<?, ?B/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b7ffb1fe748c482d9bb14bae3e907b65",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "config.json:   0%|          | 0.00/877 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "235dbf84a82440aa8dcd3b5f04c31cb5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "model.safetensors:   0%|          | 0.00/2.47G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b730a09cc8bd4a80b93b6c3454c3f543",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "generation_config.json:   0%|          | 0.00/189 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_name = \"meta-llama/Llama-3.2-1B-Instruct\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
    "model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map=\"auto\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BYunoQytaIN8"
   },
   "source": [
    "# Secondary Model for Verification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 315,
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      "Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n",
      "WARNING:huggingface_hub.file_download:Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n"
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Device set to use cuda:0\n"
     ]
    }
   ],
   "source": [
    "# from transformers import pipeline, set_seed\n",
    "# set_seed(42)\n",
    "# verifier_model  = pipeline('text-generation', model='gpt2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4bJjeuOqcUpe"
   },
   "source": [
    "# Sanitazion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "OrMXXrtTZYJq"
   },
   "outputs": [],
   "source": [
    "def sanitize_answer(question, answer):\n",
    "   return any(word.lower() in answer.lower() for word in question.lower().split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "OTFnFwONZx0W"
   },
   "outputs": [],
   "source": [
    "recent_questions = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "m8ep8ob3susI"
   },
   "outputs": [],
   "source": [
    "def is_finance_question(user_query):\n",
    "    check_prompt = (\n",
    "        f\"You are a financial expert. Determine whether the following question is clearly about finance:\\n\\n\"\n",
    "        f\"Question: {user_query}\\n\\n\"\n",
    "        f\"Respond only with 'Yes' or 'No'.\"\n",
    "    )\n",
    "    input_ids = tokenizer(check_prompt, return_tensors=\"pt\").to(model.device)\n",
    "    output_ids = model.generate(\n",
    "        **input_ids,\n",
    "        max_new_tokens=10,\n",
    "        temperature=0.0,\n",
    "        top_p=0.9,\n",
    "        pad_token_id=tokenizer.eos_token_id\n",
    "    )\n",
    "    response = tokenizer.decode(output_ids[0], skip_special_tokens=True).strip()\n",
    "    return response.lower().startswith(\"yes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "8FbbAE5aYpwr"
   },
   "outputs": [],
   "source": [
    "def ask_finance_bot(user_query, top_k=3):\n",
    "    normalized_query = user_query.lower().strip()\n",
    "    count = recent_questions.get(normalized_query, 0) + 1\n",
    "    recent_questions[normalized_query] = count\n",
    "\n",
    "    # Step 1: Embed query\n",
    "    query_embedding = embedding_model.encode([user_query])\n",
    "    D, I = index.search(np.array(query_embedding), top_k)\n",
    "    retrieved_answers = [answer_embeddings[i] for i in I[0]]\n",
    "    context = \"\\n\".join([f\"- {text}\" for text in retrieved_answers])\n",
    "\n",
    "    # Adjust temperature for repeated questions\n",
    "    temperature = min(0.7 + 0.1 * (count - 1), 1.0)\n",
    "\n",
    "    # Step 2: Build instruction\n",
    "    instruction = (\n",
    "        \"You are a highly knowledgeable AI assistant specializing strictly in finance.\\n\"\n",
    "        \"Strictly answer only financially related topics.\\n\"\n",
    "        \"Never answer questions that are not financially related.\\n\"\n",
    "        \"Do not answer anything outside finance.\\n\"\n",
    "        \"Always provide accurate, objective, and concise answers to financial questions.\\n\"\n",
    "        \"Avoid unnecessary elaboration and focus directly on answering the user's query.\\n\"\n",
    "        \"Use the background context only if it is accurate, clear, and relevant. If the context is unclear, incomplete, low-quality, or irrelevant, ignore it and generate your own correct, concise financial answer.\\n\"\n",
    "        \"Do not copy or repeat the context verbatim — instead, synthesize your own response based on it.\\n\"\n",
    "        \"Do not speculate or use personal phrases like 'I think' or 'In my opinion'.\\n\"\n",
    "        \"If a user repeats a question, rephrase your response differently but keep the meaning and context consistent.\\n\"\n",
    "        \"Always answer only questions clearly related to finance — including personal finance, investing, credit, banking, insurance, budgeting, financial planning, macroeconomics, and markets.\\n\"\n",
    "        \"Provide answers that are well-explained, logically structured, and easy to understand.\\n\"\n",
    "        \"Write in a neutral and professional tone — avoid being casual or overly brief.\\n\"\n",
    "        \"If a retrieved answer is based on personal experience or opinion, do not display it — instead, generate your own accurate and objective financial response.\\n\"\n",
    "        \"If a valid financial question is asked, always answer — never refuse or say 'I can't help with that.'\\n\"\n",
    "        \"If a question is unrelated to finance, respond: 'I'm specialized in finance and can't help with that.'\\n\"\n",
    "        \"Do not provide medical, legal, or general life advice.\\n\"\n",
    "        \"Avoid incomplete explanations.\\n\"\n",
    "        \"Avoid unnecessary elaboration — focus on clarity and usefulness.\\n\"\n",
    "        \"Your answers must sound like a trustworthy, human financial advisor with deep domain knowledge.\\n\"\n",
    "        \"Structure complex answers with examples, bullet points, or breakdowns if needed.\\n\"\n",
    "        \"Do not answer questions that are vague, ambiguous, or lacking sufficient context.\\n\"\n",
    "        \"If a question is unclear, ask clarifying questions to get more information.\\n\"\n",
    "        \"Do not repeat the same answer for different questions — generate unique responses.\\n\"\n",
    "        \"Do not use overly technical jargon — explain concepts.\\n\"\n",
    "        \"Do not answer in general terms — stick to finance related terminologies.\\n\"\n",
    "        \"Do not interpret greetings, vague messages, or incomplete inputs as financial queries.\\n\"\n",
    "        \"Do not hallucinate questions or inject answers.\\n\"\n",
    "        \"Do not assume the user's intent.\\n\"\n",
    "        \"If the question is not financial related, respond: 'I'm specialized in finance and can't help with that. How can I assist you with a finance-related question today?'\\n\"\n",
    "        \"If the user uses a greeting such as 'Hi', 'Hello', or 'Hey', respond with: 'Hello! How can I help you with your finance-related question today?'\\n\"\n",
    "        \"You are a finance chatbot. Only respond to direct user questions. Do not create or simulate both sides of a conversation. Do not provide additional Q&A pairs unless asked.\\n\"\n",
    "    )\n",
    "\n",
    "    # Step 3: Try generating a good answer up to 6 times\n",
    "    for _ in range(6):\n",
    "        prompt = f\"\"\"{instruction}\n",
    "\n",
    "Background context:\n",
    "{context}\n",
    "\n",
    "User question: {user_query}\n",
    "\n",
    "Answer:\"\"\"\n",
    "\n",
    "        input_ids = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n",
    "        output_ids = model.generate(\n",
    "            **input_ids,\n",
    "            max_new_tokens=10000,\n",
    "            temperature=temperature,\n",
    "            top_p=0.9,\n",
    "            pad_token_id=tokenizer.eos_token_id\n",
    "        )\n",
    "\n",
    "        response = tokenizer.decode(output_ids[0], skip_special_tokens=True)\n",
    "        answer_text = response.split(\"Answer:\")[-1].strip()\n",
    "\n",
    "        if sanitize_answer(user_query, answer_text):\n",
    "            return answer_text\n",
    "\n",
    "    return \"I'm not confident in the response. Please consult a certified financial expert.\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iXhDmf5rZtCn",
    "outputId": "fab5b8d5-5375-4ff7-9427-72e2b023b50d"
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Finance Bot is ready. Type your question or type 'exit' to quit.\n",
      "\n",
      "You: what is football\n",
      "\n",
      "DiMowkayBot: I'm specialized in finance and can't help with that. How can I assist you with a finance-related question today?\n",
      "\n",
      "You: what do you know about lombard Commercial Insurance\n",
      "\n",
      "DiMowkayBot: Lombard Commercial Insurance is a commercial insurance company that provides business insurance solutions to businesses of all sizes. They offer a range of insurance products, including liability, property, and workers' compensation insurance, as well as specialized products such as cyber insurance and data breach insurance. Lombard Commercial Insurance has a global presence and provides insurance coverage to businesses in the United States, Canada, and the United Kingdom. They are known for their comprehensive risk management and claims handling services, as well as their focus on providing personalized customer service. Lombard Commercial Insurance has a long history, dating back to 1837, and has been a trusted partner for businesses for over 180 years. They are a leading provider of commercial insurance solutions, and are recognized for their expertise and professionalism in the industry.\n",
      "\n",
      "You: what do you know about lombard Commercial Insurance\n",
      "\n",
      "DiMowkayBot: Lombard Commercial Insurance is a leading provider of commercial insurance, offering a wide range of insurance products to protect businesses against various risks. Their primary areas of focus include liability, property, workers' compensation, business interruption, and directors' and officers' (D&O) insurance.\n",
      "\n",
      "Key services:\n",
      "\n",
      "- Liability insurance: provides protection against business-related lawsuits and claims\n",
      "- Property insurance: offers coverage for business assets, including property, equipment, and inventory\n",
      "- Workers' compensation insurance: provides financial support for employees injured on the job\n",
      "- Business interruption insurance: provides financial support to businesses during periods of closure or business interruption due to unforeseen events\n",
      "- Directors' and officers' insurance (D&O) insurance: provides protection for directors, officers, and key employees in the event of a lawsuit or adverse event.\n",
      "\n",
      "Lombard Commercial Insurance is available in various formats, including group and individual policies, and can be tailored to meet specific business needs.\n",
      "\n",
      "Lombard Commercial Insurance has a strong reputation for providing comprehensive, flexible, and cost-effective insurance solutions.\n",
      "\n",
      "Rating: 4.8/5 (based on industry reviews and ratings)\n",
      "\n",
      "Source: Industry publications and reviews, including the Financial Times, Business Insurance, and Insurance Journal.\n",
      "\n",
      "Please note that the rating is based on general industry reviews and may not reflect your individual experience with Lombard Commercial Insurance.\n",
      "\n",
      "You: exit\n",
      "Goodbye!\n"
     ]
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    print(\"Finance Bot is ready. Type your question or type 'exit' to quit.\")\n",
    "    while True:\n",
    "        user_input = input(\"\\nYou: \")\n",
    "        if user_input.lower() in ['exit', 'quit']:\n",
    "            print(\"Goodbye!\")\n",
    "            break\n",
    "        answer = ask_finance_bot(user_input)\n",
    "        print(f\"\\nDiMowkayBot: {answer}\")\n"
   ]
  }
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