main.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XYah37IMQi7H",
        "outputId": "39965743-d814-40e3-b205-385eb0d94119"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: unstructured[pdf] in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (0.12.6)\n",
            "Requirement already satisfied: backoff==2.2.1 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (2.2.1)\n",
            "Requirement already satisfied: beautifulsoup4==4.12.3 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (4.12.3)\n",
            "Requirement already satisfied: certifi==2024.2.2 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (2024.2.2)\n",
            "Requirement already satisfied: chardet==5.2.0 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (5.2.0)\n",
            "Requirement already satisfied: charset-normalizer==3.3.2 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (3.3.2)\n",
            "Requirement already satisfied: click==8.1.7 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (8.1.7)\n",
            "Requirement already satisfied: dataclasses-json==0.6.4 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (0.6.4)\n",
            "Requirement already satisfied: dataclasses-json-speakeasy==0.5.11 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (0.5.11)\n",
            "Requirement already satisfied: emoji==2.10.1 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (2.10.1)\n",
            "Requirement already satisfied: filetype==1.2.0 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (1.2.0)\n",
            "Requirement already satisfied: idna==3.6 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (3.6)\n",
            "Requirement already satisfied: joblib==1.3.2 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (1.3.2)\n",
            "Requirement already satisfied: jsonpath-python==1.0.6 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (1.0.6)\n",
            "Requirement already satisfied: langdetect==1.0.9 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (1.0.9)\n",
            "Requirement already satisfied: lxml==5.1.0 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (5.1.0)\n",
            "Requirement already satisfied: marshmallow==3.20.2 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (3.20.2)\n",
            "Requirement already satisfied: mypy-extensions==1.0.0 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (1.0.0)\n",
            "Requirement already satisfied: nltk==3.8.1 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (3.8.1)\n",
            "Requirement already satisfied: numpy==1.26.4 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (1.26.4)\n",
            "Requirement already satisfied: packaging==23.2 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (23.2)\n",
            "Requirement already satisfied: python-dateutil==2.8.2 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (2.8.2)\n",
            "Requirement already satisfied: python-iso639==2024.2.7 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (2024.2.7)\n",
            "Requirement already satisfied: python-magic==0.4.27 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (0.4.27)\n",
            "Requirement already satisfied: rapidfuzz==3.6.1 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (3.6.1)\n",
            "Requirement already satisfied: regex==2023.12.25 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (2023.12.25)\n",
            "Requirement already satisfied: requests==2.31.0 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (2.31.0)\n",
            "Requirement already satisfied: six==1.16.0 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (1.16.0)\n",
            "Requirement already satisfied: soupsieve==2.5 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (2.5)\n",
            "Requirement already satisfied: tabulate==0.9.0 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (0.9.0)\n",
            "Requirement already satisfied: tqdm==4.66.2 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (4.66.2)\n",
            "Requirement already satisfied: typing-extensions==4.9.0 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (4.9.0)\n",
            "Requirement already satisfied: typing-inspect==0.9.0 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (0.9.0)\n",
            "Requirement already satisfied: unstructured-client==0.18.0 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (0.18.0)\n",
            "Requirement already satisfied: urllib3==1.26.18 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (1.26.18)\n",
            "Requirement already satisfied: wrapt==1.16.0 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (1.16.0)\n",
            "Collecting antlr4-python3-runtime==4.9.3 (from unstructured[pdf])\n",
            "  Downloading antlr4-python3-runtime-4.9.3.tar.gz (117 kB)\n",
            "     ---------------------------------------- 0.0/117.0 kB ? eta -:--:--\n",
            "     --- ------------------------------------ 10.2/117.0 kB ? eta -:--:--\n",
            "     --------- --------------------------- 30.7/117.0 kB 325.1 kB/s eta 0:00:01\n",
            "     --------- --------------------------- 30.7/117.0 kB 325.1 kB/s eta 0:00:01\n",
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            "     ------------------------------------ 117.0/117.0 kB 401.5 kB/s eta 0:00:00\n",
            "  Installing build dependencies: started\n",
            "  Installing build dependencies: finished with status 'done'\n",
            "  Getting requirements to build wheel: started\n",
            "  Getting requirements to build wheel: finished with status 'done'\n",
            "  Installing backend dependencies: started\n",
            "  Installing backend dependencies: finished with status 'done'\n",
            "  Preparing metadata (pyproject.toml): started\n",
            "  Preparing metadata (pyproject.toml): finished with status 'done'\n",
            "Collecting cffi==1.16.0 (from unstructured[pdf])\n",
            "  Downloading cffi-1.16.0-cp311-cp311-win_amd64.whl.metadata (1.5 kB)\n",
            "Requirement already satisfied: coloredlogs==15.0.1 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (15.0.1)\n",
            "Requirement already satisfied: contourpy==1.2.0 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (1.2.0)\n",
            "Collecting cryptography==42.0.2 (from unstructured[pdf])\n",
            "  Downloading cryptography-42.0.2-cp39-abi3-win_amd64.whl.metadata (5.4 kB)\n",
            "Requirement already satisfied: cycler==0.12.1 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (0.12.1)\n",
            "Requirement already satisfied: deprecated==1.2.14 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (1.2.14)\n",
            "Collecting effdet==0.4.1 (from unstructured[pdf])\n",
            "  Downloading effdet-0.4.1-py3-none-any.whl.metadata (33 kB)\n",
            "Requirement already satisfied: filelock==3.13.1 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (3.13.1)\n",
            "Collecting flatbuffers==23.5.26 (from unstructured[pdf])\n",
            "  Downloading flatbuffers-23.5.26-py2.py3-none-any.whl.metadata (850 bytes)\n",
            "Requirement already satisfied: fonttools==4.49.0 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (4.49.0)\n",
            "Requirement already satisfied: fsspec==2024.2.0 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (2024.2.0)\n",
            "Collecting huggingface-hub==0.20.3 (from unstructured[pdf])\n",
            "  Downloading huggingface_hub-0.20.3-py3-none-any.whl.metadata (12 kB)\n",
            "Requirement already satisfied: humanfriendly==10.0 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (10.0)\n",
            "Collecting importlib-resources==6.1.1 (from unstructured[pdf])\n",
            "  Downloading importlib_resources-6.1.1-py3-none-any.whl.metadata (4.1 kB)\n",
            "Collecting iopath==0.1.10 (from unstructured[pdf])\n",
            "  Downloading iopath-0.1.10.tar.gz (42 kB)\n",
            "     ---------------------------------------- 0.0/42.2 kB ? eta -:--:--\n",
            "     ---------------------------------------- 42.2/42.2 kB 2.1 MB/s eta 0:00:00\n",
            "  Installing build dependencies: started\n",
            "  Installing build dependencies: finished with status 'done'\n",
            "  Getting requirements to build wheel: started\n",
            "  Getting requirements to build wheel: finished with status 'done'\n",
            "  Installing backend dependencies: started\n",
            "  Installing backend dependencies: finished with status 'done'\n",
            "  Preparing metadata (pyproject.toml): started\n",
            "  Preparing metadata (pyproject.toml): finished with status 'done'\n",
            "Requirement already satisfied: jinja2==3.1.3 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (3.1.3)\n",
            "Requirement already satisfied: kiwisolver==1.4.5 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (1.4.5)\n",
            "Collecting layoutparser==0.3.4 (from layoutparser[layoutmodels,tesseract]==0.3.4; extra == \"pdf\"->unstructured[pdf])\n",
            "  Downloading layoutparser-0.3.4-py3-none-any.whl.metadata (7.7 kB)\n",
            "Requirement already satisfied: markupsafe==2.1.5 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (2.1.5)\n",
            "Collecting matplotlib==3.7.2 (from unstructured[pdf])\n",
            "  Downloading matplotlib-3.7.2-cp311-cp311-win_amd64.whl.metadata (5.8 kB)\n",
            "Requirement already satisfied: mpmath==1.3.0 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (1.3.0)\n",
            "Requirement already satisfied: networkx==3.2.1 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (3.2.1)\n",
            "Collecting omegaconf==2.3.0 (from unstructured[pdf])\n",
            "  Downloading omegaconf-2.3.0-py3-none-any.whl.metadata (3.9 kB)\n",
            "Collecting onnx==1.15.0 (from unstructured[pdf])\n",
            "  Downloading onnx-1.15.0-cp311-cp311-win_amd64.whl.metadata (15 kB)\n",
            "Collecting onnxruntime==1.15.1 (from unstructured[pdf])\n",
            "  Downloading onnxruntime-1.15.1-cp311-cp311-win_amd64.whl.metadata (4.1 kB)\n",
            "Collecting opencv-python==4.8.0.76 (from unstructured[pdf])\n",
            "  Downloading opencv_python-4.8.0.76-cp37-abi3-win_amd64.whl.metadata (20 kB)\n",
            "Collecting pandas==2.2.0 (from unstructured[pdf])\n",
            "  Downloading pandas-2.2.0-cp311-cp311-win_amd64.whl.metadata (19 kB)\n",
            "Collecting pdf2image==1.17.0 (from unstructured[pdf])\n",
            "  Downloading pdf2image-1.17.0-py3-none-any.whl.metadata (6.2 kB)\n",
            "Collecting pdfminer-six==20221105 (from unstructured[pdf])\n",
            "  Downloading pdfminer.six-20221105-py3-none-any.whl.metadata (4.0 kB)\n",
            "Collecting pdfplumber==0.10.4 (from unstructured[pdf])\n",
            "  Downloading pdfplumber-0.10.4-py3-none-any.whl.metadata (39 kB)\n",
            "Collecting pikepdf==8.11.0 (from unstructured[pdf])\n",
            "  Downloading pikepdf-8.11.0-cp311-cp311-win_amd64.whl.metadata (8.6 kB)\n",
            "Requirement already satisfied: pillow==10.2.0 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (10.2.0)\n",
            "Collecting pillow-heif==0.15.0 (from unstructured[pdf])\n",
            "  Downloading pillow_heif-0.15.0-cp311-cp311-win_amd64.whl.metadata (9.5 kB)\n",
            "Collecting portalocker==2.8.2 (from unstructured[pdf])\n",
            "  Downloading portalocker-2.8.2-py3-none-any.whl.metadata (8.5 kB)\n",
            "Collecting protobuf==4.23.4 (from unstructured[pdf])\n",
            "  Downloading protobuf-4.23.4-cp310-abi3-win_amd64.whl.metadata (540 bytes)\n",
            "Collecting pycocotools==2.0.7 (from unstructured[pdf])\n",
            "  Downloading pycocotools-2.0.7-cp311-cp311-win_amd64.whl.metadata (1.1 kB)\n",
            "Collecting pycparser==2.21 (from unstructured[pdf])\n",
            "  Downloading pycparser-2.21-py2.py3-none-any.whl.metadata (1.1 kB)\n",
            "Collecting pyparsing==3.0.9 (from unstructured[pdf])\n",
            "  Downloading pyparsing-3.0.9-py3-none-any.whl.metadata (4.2 kB)\n",
            "Collecting pypdf==4.0.1 (from unstructured[pdf])\n",
            "  Downloading pypdf-4.0.1-py3-none-any.whl.metadata (7.4 kB)\n",
            "Collecting pypdfium2==4.27.0 (from unstructured[pdf])\n",
            "  Downloading pypdfium2-4.27.0-py3-none-win_amd64.whl.metadata (48 kB)\n",
            "     ---------------------------------------- 0.0/48.1 kB ? eta -:--:--\n",
            "     ---------------------------------------- 48.1/48.1 kB 2.5 MB/s eta 0:00:00\n",
            "Collecting pytesseract==0.3.10 (from unstructured[pdf])\n",
            "  Downloading pytesseract-0.3.10-py3-none-any.whl.metadata (11 kB)\n",
            "Requirement already satisfied: python-multipart==0.0.9 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (0.0.9)\n",
            "Requirement already satisfied: pytz==2024.1 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (2024.1)\n",
            "Requirement already satisfied: pyyaml==6.0.1 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (6.0.1)\n",
            "Collecting safetensors==0.3.2 (from unstructured[pdf])\n",
            "  Downloading safetensors-0.3.2-cp311-cp311-win_amd64.whl.metadata (4.6 kB)\n",
            "Collecting scipy==1.10.1 (from unstructured[pdf])\n",
            "  Downloading scipy-1.10.1-cp311-cp311-win_amd64.whl.metadata (58 kB)\n",
            "     ---------------------------------------- 0.0/59.0 kB ? eta -:--:--\n",
            "     ---------------------------------------- 59.0/59.0 kB ? eta 0:00:00\n",
            "Requirement already satisfied: sympy==1.12 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (1.12)\n",
            "Collecting timm==0.9.12 (from unstructured[pdf])\n",
            "  Downloading timm-0.9.12-py3-none-any.whl.metadata (60 kB)\n",
            "     ---------------------------------------- 0.0/60.6 kB ? eta -:--:--\n",
            "     ---------------------------------------- 60.6/60.6 kB 3.1 MB/s eta 0:00:00\n",
            "Requirement already satisfied: tokenizers==0.15.2 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (0.15.2)\n",
            "Collecting torch==2.2.0 (from unstructured[pdf])\n",
            "  Downloading torch-2.2.0-cp311-cp311-win_amd64.whl.metadata (26 kB)\n",
            "Collecting torchvision==0.17.0 (from unstructured[pdf])\n",
            "  Downloading torchvision-0.17.0-cp311-cp311-win_amd64.whl.metadata (6.6 kB)\n",
            "Collecting transformers==4.37.1 (from unstructured[pdf])\n",
            "  Downloading transformers-4.37.1-py3-none-any.whl.metadata (129 kB)\n",
            "     ---------------------------------------- 0.0/129.4 kB ? eta -:--:--\n",
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            "Requirement already satisfied: tzdata==2024.1 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from unstructured[pdf]) (2024.1)\n",
            "Collecting unstructured-inference==0.7.23 (from unstructured[pdf])\n",
            "  Downloading unstructured_inference-0.7.23-py3-none-any.whl.metadata (5.8 kB)\n",
            "Collecting unstructured-pytesseract==0.3.12 (from unstructured[pdf])\n",
            "  Downloading unstructured.pytesseract-0.3.12-py3-none-any.whl.metadata (11 kB)\n",
            "Collecting zipp==3.17.0 (from unstructured[pdf])\n",
            "  Downloading zipp-3.17.0-py3-none-any.whl.metadata (3.7 kB)\n",
            "Requirement already satisfied: colorama in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from click==8.1.7->unstructured[pdf]) (0.4.6)\n",
            "Requirement already satisfied: pyreadline3 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from humanfriendly==10.0->unstructured[pdf]) (3.4.1)\n",
            "Requirement already satisfied: pywin32>=226 in c:\\users\\gokul\\onedrive\\desktop\\pyexpo\\myenv\\lib\\site-packages (from portalocker==2.8.2->unstructured[pdf]) (306)\n",
            "Downloading cffi-1.16.0-cp311-cp311-win_amd64.whl (181 kB)\n",
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            "Downloading cryptography-42.0.2-cp39-abi3-win_amd64.whl (2.9 MB)\n",
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            "Downloading effdet-0.4.1-py3-none-any.whl (112 kB)\n",
            "   ---------------------------------------- 0.0/112.5 kB ? eta -:--:--\n",
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            "Downloading flatbuffers-23.5.26-py2.py3-none-any.whl (26 kB)\n",
            "Downloading huggingface_hub-0.20.3-py3-none-any.whl (330 kB)\n",
            "   ---------------------------------------- 0.0/330.1 kB ? eta -:--:--\n",
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            "Downloading importlib_resources-6.1.1-py3-none-any.whl (33 kB)\n",
            "Downloading layoutparser-0.3.4-py3-none-any.whl (19.2 MB)\n",
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            "Building wheels for collected packages: antlr4-python3-runtime, iopath\n",
            "  Building wheel for antlr4-python3-runtime (pyproject.toml): started\n",
            "  Building wheel for antlr4-python3-runtime (pyproject.toml): finished with status 'done'\n",
            "  Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144578 sha256=cbf61f17712305d61ecdfa238a53c5ed625b71e8ebfd0e9cd12c5642c30c4e9b\n",
            "  Stored in directory: c:\\users\\gokul\\appdata\\local\\pip\\cache\\wheels\\1a\\97\\32\\461f837398029ad76911109f07047fde1d7b661a147c7c56d1\n",
            "  Building wheel for iopath (pyproject.toml): started\n",
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            "  Created wheel for iopath: filename=iopath-0.1.10-py3-none-any.whl size=31543 sha256=13dcd08ab5e1e346b07acf8d9432918dc2d8539aaa35a10818f5c18d15019226\n",
            "  Stored in directory: c:\\users\\gokul\\appdata\\local\\pip\\cache\\wheels\\ba\\5e\\16\\6117f8fe7e9c0c161a795e10d94645ebcf301ccbd01f66d8ec\n",
            "Successfully built antlr4-python3-runtime iopath\n",
            "Installing collected packages: safetensors, flatbuffers, antlr4-python3-runtime, zipp, unstructured-pytesseract, scipy, pytesseract, pypdfium2, pypdf, pyparsing, pycparser, protobuf, portalocker, pillow-heif, pdf2image, opencv-python, omegaconf, importlib-resources, torch, pikepdf, pandas, onnx, matplotlib, iopath, huggingface-hub, cffi, torchvision, pycocotools, onnxruntime, cryptography, transformers, timm, pdfminer-six, pdfplumber, effdet, layoutparser, unstructured-inference\n",
            "  Attempting uninstall: safetensors\n",
            "    Found existing installation: safetensors 0.4.2\n",
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            "  Attempting uninstall: importlib-resources\n",
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            "      Successfully uninstalled importlib_resources-6.3.0\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 2.2.1\n",
            "    Uninstalling torch-2.2.1:\n",
            "      Successfully uninstalled torch-2.2.1\n",
            "  Attempting uninstall: pandas\n",
            "    Found existing installation: pandas 2.2.1\n",
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            "  Attempting uninstall: matplotlib\n",
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            "      Successfully uninstalled matplotlib-3.8.3\n",
            "  Attempting uninstall: huggingface-hub\n",
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            "      Successfully uninstalled huggingface-hub-0.21.4\n",
            "  Attempting uninstall: onnxruntime\n",
            "    Found existing installation: onnxruntime 1.17.1\n",
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            "      Successfully uninstalled onnxruntime-1.17.1\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.38.2\n",
            "    Uninstalling transformers-4.38.2:\n",
            "      Successfully uninstalled transformers-4.38.2\n",
            "Successfully installed antlr4-python3-runtime-4.9.3 cffi-1.16.0 cryptography-42.0.2 effdet-0.4.1 flatbuffers-23.5.26 huggingface-hub-0.20.3 importlib-resources-6.1.1 iopath-0.1.10 layoutparser-0.3.4 matplotlib-3.7.2 omegaconf-2.3.0 onnx-1.15.0 onnxruntime-1.15.1 opencv-python-4.8.0.76 pandas-2.2.0 pdf2image-1.17.0 pdfminer-six-20221105 pdfplumber-0.10.4 pikepdf-8.11.0 pillow-heif-0.15.0 portalocker-2.8.2 protobuf-4.23.4 pycocotools-2.0.7 pycparser-2.21 pyparsing-3.0.9 pypdf-4.0.1 pypdfium2-4.27.0 pytesseract-0.3.10 safetensors-0.3.2 scipy-1.10.1 timm-0.9.12 torch-2.2.0 torchvision-0.17.0 transformers-4.37.1 unstructured-inference-0.7.23 unstructured-pytesseract-0.3.12 zipp-3.17.0\n"
          ]
        }
      ],
      "source": [
        "%pip install gradio --quiet\n",
        "%pip install xformer --quiet\n",
        "%pip install chromadb --quiet\n",
        "%pip install langchain --quiet\n",
        "%pip install accelerate --quiet\n",
        "%pip install transformers --quiet\n",
        "%pip install bitsandbytes --quiet\n",
        "%pip install unstructured --quiet\n",
        "%pip install sentence-transformers --quiet\n",
        "%pip install unstructured[pdf]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "HJjAvn9qQmGT"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Users\\gokul\\OneDrive\\Desktop\\pyexpo\\myenv\\Lib\\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",
        "import gradio as gr\n",
        "\n",
        "from textwrap import fill\n",
        "from IPython.display import Markdown, display\n",
        "\n",
        "from langchain.prompts.chat import (\n",
        "    ChatPromptTemplate,\n",
        "    HumanMessagePromptTemplate,\n",
        "    SystemMessagePromptTemplate,\n",
        "    )\n",
        "\n",
        "from langchain import PromptTemplate\n",
        "from langchain import HuggingFacePipeline\n",
        "from langchain.vectorstores import Chroma\n",
        "from langchain.schema import AIMessage, HumanMessage\n",
        "from langchain.memory import ConversationBufferMemory\n",
        "from langchain.embeddings import HuggingFaceEmbeddings\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain.document_loaders import UnstructuredMarkdownLoader, UnstructuredURLLoader\n",
        "from langchain.chains import LLMChain, SimpleSequentialChain, RetrievalQA, ConversationalRetrievalChain\n",
        "\n",
        "from transformers import BitsAndBytesConfig, AutoModelForCausalLM, AutoTokenizer, GenerationConfig, pipeline\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
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        "id": "LQU4iUDAQmC5",
        "outputId": "447f833c-5fcd-405a-bf83-c187e613a215"
      },
      "outputs": [
        {
          "ename": "RuntimeError",
          "evalue": "No GPU found. A GPU is needed for quantization.",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "Cell \u001b[1;32mIn[8], line 13\u001b[0m\n\u001b[0;32m     10\u001b[0m tokenizer \u001b[38;5;241m=\u001b[39m AutoTokenizer\u001b[38;5;241m.\u001b[39mfrom_pretrained(MODEL_NAME, use_fast\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m     11\u001b[0m tokenizer\u001b[38;5;241m.\u001b[39mpad_token \u001b[38;5;241m=\u001b[39m tokenizer\u001b[38;5;241m.\u001b[39meos_token\n\u001b[1;32m---> 13\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mAutoModelForCausalLM\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m     14\u001b[0m \u001b[43m    \u001b[49m\u001b[43mMODEL_NAME\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtorch_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat16\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     15\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtrust_remote_code\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m     16\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdevice_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mauto\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m     17\u001b[0m \u001b[43m    \u001b[49m\u001b[43mquantization_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquantization_config\u001b[49m\n\u001b[0;32m     18\u001b[0m \u001b[43m)\u001b[49m\n\u001b[0;32m     20\u001b[0m generation_config \u001b[38;5;241m=\u001b[39m GenerationConfig\u001b[38;5;241m.\u001b[39mfrom_pretrained(MODEL_NAME)\n\u001b[0;32m     21\u001b[0m generation_config\u001b[38;5;241m.\u001b[39mmax_new_tokens \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1024\u001b[39m\n",
            "File \u001b[1;32mc:\\Users\\gokul\\OneDrive\\Desktop\\pyexpo\\myenv\\Lib\\site-packages\\transformers\\models\\auto\\auto_factory.py:566\u001b[0m, in \u001b[0;36m_BaseAutoModelClass.from_pretrained\u001b[1;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[0;32m    564\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(config) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[0;32m    565\u001b[0m     model_class \u001b[38;5;241m=\u001b[39m _get_model_class(config, \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping)\n\u001b[1;32m--> 566\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmodel_class\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    567\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mhub_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[0;32m    568\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    569\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    570\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnrecognized configuration class \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconfig\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m for this kind of AutoModel: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    571\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mModel type should be one of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(c\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mfor\u001b[39;00m\u001b[38;5;250m \u001b[39mc\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01min\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping\u001b[38;5;241m.\u001b[39mkeys())\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    572\u001b[0m )\n",
            "File \u001b[1;32mc:\\Users\\gokul\\OneDrive\\Desktop\\pyexpo\\myenv\\Lib\\site-packages\\transformers\\modeling_utils.py:3032\u001b[0m, in \u001b[0;36mPreTrainedModel.from_pretrained\u001b[1;34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, *model_args, **kwargs)\u001b[0m\n\u001b[0;32m   3030\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m load_in_8bit \u001b[38;5;129;01mor\u001b[39;00m load_in_4bit:\n\u001b[0;32m   3031\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mcuda\u001b[38;5;241m.\u001b[39mis_available():\n\u001b[1;32m-> 3032\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo GPU found. A GPU is needed for quantization.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   3033\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (is_accelerate_available() \u001b[38;5;129;01mand\u001b[39;00m is_bitsandbytes_available()):\n\u001b[0;32m   3034\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(\n\u001b[0;32m   3035\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUsing `load_in_8bit=True` requires Accelerate: `pip install accelerate` and the latest version of\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   3036\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m bitsandbytes `pip install -i https://test.pypi.org/simple/ bitsandbytes` or\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   3037\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m `pip install bitsandbytes`.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   3038\u001b[0m         )\n",
            "\u001b[1;31mRuntimeError\u001b[0m: No GPU found. A GPU is needed for quantization."
          ]
        }
      ],
      "source": [
        "    MODEL_NAME = \"mistralai/Mistral-7B-Instruct-v0.1\"\n",
        "\n",
        "    quantization_config = BitsAndBytesConfig(\n",
        "        load_in_4bit=True,\n",
        "        bnb_4bit_compute_dtype=torch.float16,\n",
        "        bnb_4bit_quant_type=\"nf4\",\n",
        "        bnb_4bit_use_double_quant=True,\n",
        "    )\n",
        "\n",
        "    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=True)\n",
        "    tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "    model = AutoModelForCausalLM.from_pretrained(\n",
        "        MODEL_NAME, torch_dtype=torch.float16,\n",
        "        trust_remote_code=True,\n",
        "        device_map=\"auto\",\n",
        "        quantization_config=quantization_config\n",
        "    )\n",
        "\n",
        "    generation_config = GenerationConfig.from_pretrained(MODEL_NAME)\n",
        "    generation_config.max_new_tokens = 1024\n",
        "    generation_config.temperature = 0.0001\n",
        "    generation_config.top_p = 0.95\n",
        "    generation_config.do_sample = True\n",
        "    generation_config.repetition_penalty = 1.15\n",
        "\n",
        "    pipeline = pipeline(\n",
        "        \"text-generation\",\n",
        "        model=model,\n",
        "        tokenizer=tokenizer,\n",
        "        return_full_text=True,\n",
        "        generation_config=generation_config,\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p0-zA5lGQl_j"
      },
      "outputs": [],
      "source": [
        "llm = HuggingFacePipeline(\n",
        "    pipeline=pipeline,\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "id": "U3yeYBXDQl9h",
        "outputId": "c41a32a1-dd88-4730-a9aa-98af81725a16"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "data": {
            "text/markdown": [
              "<b>Explain the difference between ChatGPT and open source LLMs in a couple of lines.</b>"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/markdown": [
              "<p>\n",
              "ChatGPT is a proprietary model developed by OpenAI, while open source LLMs are models that are made available for anyone to use, modify, and distribute under an open-source license.</p>"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "query = \"Explain the difference between ChatGPT and open source LLMs in a couple of lines.\"\n",
        "result = llm(\n",
        "    query\n",
        ")\n",
        "\n",
        "display(Markdown(f\"<b>{query}</b>\"))\n",
        "display(Markdown(f\"<p>{result}</p>\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        },
        "id": "bSWIqXe1Ql7L",
        "outputId": "b1b77cdd-29ee-492d-c882-cb917972d5df"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1d5aba365a7c4a97a3e8a3d069f36dc2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "modules.json:   0%|          | 0.00/385 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "becd9fb0a5db46babe3d9aa98e80db79",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "README.md:   0%|          | 0.00/67.9k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0d1637b2a4b44212b9243cb55b80c148",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e8d787718ec74b4ea68a336f71bf195d",
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bd94e4ba786e4228873502a735c109ae",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "3c012030deec4e15ac134b27d91cd86e",
              "version_major": 2,
              "version_minor": 0
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f0fe3d23a2ae49b2adef168c698fec2e",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "ae004e47984f41f88fdd43e8bc0c43a5",
              "version_major": 2,
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1522f0b8818947b88bdc8bce23181892",
              "version_major": 2,
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a9f6d2ec11e94f2c8f5d9c2da0e64ec9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "embeddings = HuggingFaceEmbeddings(\n",
        "    model_name=\"thenlper/gte-large\",\n",
        "    model_kwargs={\"device\": \"cuda\"},\n",
        "    encode_kwargs={\"normalize_embeddings\": True},\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JhNHeiTzQl4K"
      },
      "outputs": [],
      "source": [
        "template = \"\"\"\n",
        "[INST] <>\n",
        "Act as a Machine Learning engineer who is teaching high school students.\n",
        "<>\n",
        "\n",
        "{text} [/INST]\n",
        "\"\"\"\n",
        "\n",
        "prompt = PromptTemplate(\n",
        "    input_variables=[\"text\"],\n",
        "    template=template,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "id": "rl2eSb4jQl1l",
        "outputId": "ef33b068-7c89-4151-c0d7-ae50ebd9eb0f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "data": {
            "text/markdown": [
              "<b>Explain what are Deep Neural Networks in 2-3 sentences</b>"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/markdown": [
              "<p>Deep neural networks (DNN) are a type of artificial intelligence model that simulates the structure and function of the human brain. They consist of multiple layers of interconnected nodes, or neurons, that process information by passing it through successive layers until an output is generated. DNNs can be trained on large amounts of data to recognize patterns and make predictions, making them useful for tasks such as image recognition, speech recognition, and natural language processing.</p>"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "query = \"Explain what are Deep Neural Networks in 2-3 sentences\"\n",
        "result = llm(prompt.format(text=query))\n",
        "\n",
        "display(Markdown(f\"<b>{query}</b>\"))\n",
        "display(Markdown(f\"<p>{result}</p>\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N71teXzEQlx8",
        "outputId": "6e5906e0-49f7-4da6-de28-002d32e31fbc"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "urls = [\n",
        "    \"https://smartnet.niua.org/sites/default/files/resources/Smart_Cities_Council_India_Readiness_Guide_v2016-02.pdf\"\n",
        "]\n",
        "\n",
        "loader = UnstructuredURLLoader(urls=urls)\n",
        "documents = loader.load()\n",
        "\n",
        "len(documents)\n",
        "# Output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YJUmLO2hQlv7",
        "outputId": "c8780005-e194-4433-f483-342e66283af2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3656"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=64)\n",
        "texts_chunks = text_splitter.split_documents(documents)\n",
        "\n",
        "len(texts_chunks)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-OM2bedXQlsW"
      },
      "outputs": [],
      "source": [
        "db = Chroma.from_documents(texts_chunks, embeddings, persist_directory=\"db\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "taVbByAiQlqO"
      },
      "outputs": [],
      "source": [
        "template = \"\"\"\n",
        "[INST] <>\n",
        "Act as an AI Assistant. Use the following information to answer the question at the end.\n",
        "<>\n",
        "\n",
        "{context}\n",
        "\n",
        "{question} [/INST]\n",
        "\"\"\"\n",
        "\n",
        "prompt = PromptTemplate(template=template, input_variables=[\"context\", \"question\"])\n",
        "\n",
        "qa_chain = RetrievalQA.from_chain_type(\n",
        "    llm=llm,\n",
        "    chain_type=\"stuff\",\n",
        "    retriever=db.as_retriever(search_kwargs={\"k\": 2}),\n",
        "    return_source_documents=True,\n",
        "    chain_type_kwargs={\"prompt\": prompt},\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        },
        "id": "ywRds5ouQln-",
        "outputId": "8a47a31e-8840-4e17-acfc-eceb7f7ac3d1"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "data": {
            "text/markdown": [
              "<b>What is Indus Valley Civilization</b>"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/markdown": [
              "<p>The Indus Valley Civilization was a Bronze Age civilization that flourished in the Northwestern part of the Indian subcontinent from around 2600 BCE to 1900 BCE. It was one of the world's earliest urban civilizations and was characterized by advanced urban planning, architecture, sanitation systems, and trade networks. The civilization was centered on several major cities, including Harappa and Mohenjo-Daro, which were connected by well-maintained roads and canals. The people of the Indus Valley Civilization were known for their use of standardized weights and measures, as well as their knowledge of astronomy and mathematics. Despite its many achievements, the Indus Valley Civilization eventually declined and was replaced by other regional powers.</p>"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "query = \"What is Indus Valley Civilization\"\n",
        "result_ = qa_chain(\n",
        "    query\n",
        ")\n",
        "result = result_[\"result\"].strip()\n",
        "\n",
        "\n",
        "display(Markdown(f\"<b>{query}</b>\"))\n",
        "display(Markdown(f\"<p>{result}</p>\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ujegv_0zQ_uu",
        "outputId": "413f6305-4e87-4cf4-eaef-3919d4ea0cf4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[Document(page_content='India is a country of unity in vast diversity, the multiplicity of heritage, culture, economic (cid:70)(cid:79)(cid:68)(cid:86)(cid:86)(cid:15)(cid:3)(cid:85)(cid:68)(cid:70)(cid:72)(cid:15)(cid:3)(cid:72)(cid:87)(cid:70)(cid:17)(cid:15)(cid:3)(cid:76)(cid:86)(cid:3)(cid:85)(cid:72)(cid:192)(cid:72)(cid:70)(cid:87)(cid:72)(cid:71)(cid:3)(cid:68)(cid:81)(cid:71)(cid:3)(cid:70)(cid:82)(cid:72)(cid:91)(cid:76)(cid:86)(cid:87)(cid:86)(cid:3)(cid:76)(cid:81)(cid:3) every city. National unity and integrity have been maintained even through acute economic and social inequalities. Though each city has a unique combination of socioeconomic strata, estimated acceleration of mass migration to cities in the future will likely change the equation. While developing the concept', metadata={'source': 'https://smartnet.niua.org/sites/default/files/resources/Smart_Cities_Council_India_Readiness_Guide_v2016-02.pdf'}),\n",
              " Document(page_content='(cid:88)(cid:85)(cid:68)(cid:79)(cid:3)(cid:83)(cid:82)(cid:89)(cid:72)(cid:85)(cid:87)(cid:92)(cid:3)(cid:76)(cid:86)(cid:3)(cid:68)(cid:3)(cid:74)(cid:76)(cid:68)(cid:81)(cid:87)(cid:3)(cid:69)(cid:68)(cid:85)(cid:85)(cid:76)(cid:72)(cid:85)(cid:3)(cid:87)(cid:82)(cid:3)(cid:82)(cid:89)(cid:72)(cid:85)(cid:70)(cid:82)(cid:80)(cid:72)(cid:15)(cid:3)(cid:76)(cid:73)(cid:3) (cid:87)(cid:75)(cid:72)(cid:3)(cid:74)(cid:82)(cid:68)(cid:79)(cid:3)(cid:76)(cid:86)(cid:3)(cid:87)(cid:82)(cid:3)(cid:68)(cid:70)(cid:75)(cid:76)(cid:72)(cid:89)(cid:72)(cid:3)(cid:76)(cid:81)(cid:70)(cid:79)(cid:88)(cid:86)(cid:76)(cid:89)(cid:72)(cid:3)(cid:68)(cid:81)(cid:71)(cid:3)(cid:86)(cid:88)(cid:86)(cid:87)(cid:68)(cid:76)(cid:81)(cid:68)(cid:69)(cid:79)(cid:72)(cid:3) development. After poor monsoon rains', metadata={'source': 'https://smartnet.niua.org/sites/default/files/resources/Smart_Cities_Council_India_Readiness_Guide_v2016-02.pdf'})]"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "result_[\"source_documents\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YPmcOrjoQ_nn"
      },
      "outputs": [],
      "source": [
        "custom_template = \"\"\"You are an AI Assistant. Given the\n",
        "following conversation and a follow up question, rephrase the follow up question\n",
        "to be a standalone question. At the end of standalone question add this\n",
        "'Answer the question in English language.' If you do not know the answer reply with 'I am sorry, I dont have enough information'.\n",
        "Chat History:\n",
        "{chat_history}\n",
        "Follow Up Input: {question}\n",
        "Standalone question:\n",
        "\"\"\"\n",
        "\n",
        "CUSTOM_QUESTION_PROMPT = PromptTemplate.from_template(custom_template)\n",
        "\n",
        "memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)\n",
        "\n",
        "qa_chain = ConversationalRetrievalChain.from_llm(\n",
        "    llm=llm,\n",
        "    retriever=db.as_retriever(search_kwargs={\"k\": 2}),\n",
        "    memory=memory,\n",
        "    condense_question_prompt=CUSTOM_QUESTION_PROMPT,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "id": "AK73JQvuQllF",
        "outputId": "60022e3f-1765-4cb8-981a-01fe9076a931"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "data": {
            "text/markdown": [
              "<b>Who you are?</b>"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/markdown": [
              "<p>I am a company that values its employees, clients, and the communities it serves. I strive to operate with integrity, an adventurous spirit, and dedication to the well-being of people in my life and work.</p>"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "query = \"Who you are?\"\n",
        "result_ = qa_chain({\"question\": query})\n",
        "result = result_[\"answer\"].strip()\n",
        "\n",
        "display(Markdown(f\"<b>{query}</b>\"))\n",
        "display(Markdown(f\"<p>{result}</p>\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "id": "5oDfviCTRJue",
        "outputId": "8df04946-4672-4b6b-f1c3-32fe7625a72f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "data": {
            "text/markdown": [
              "<b>Atomic Bombings of Hiroshima and Nagasaki</b>"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/markdown": [
              "<p>The atomic bombings of Hiroshima and Nagasaki were two separate events during World War II in which the United States dropped nuclear bombs on the Japanese cities of Hiroshima and Nagasaki, respectively. These attacks were the first time nuclear weapons were used in warfare and resulted in the deaths of thousands of people. The bombings are widely considered to be a major factor in Japan's surrender and the end of World War II.</p>"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "query = \"Atomic Bombings of Hiroshima and Nagasaki\"\n",
        "\n",
        "result_ = qa_chain({\"question\": query})\n",
        "result = result_[\"answer\"].strip()\n",
        "\n",
        "display(Markdown(f\"<b>{query}</b>\"))\n",
        "display(Markdown(f\"<p>{result}</p>\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jXnnxv0QRJrC"
      },
      "outputs": [],
      "source": [
        "def querying(query, history):\n",
        "  memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)\n",
        "\n",
        "  qa_chain = ConversationalRetrievalChain.from_llm(\n",
        "      llm=llm,\n",
        "      retriever=db.as_retriever(search_kwargs={\"k\": 2}),\n",
        "      memory=memory,\n",
        "      condense_question_prompt=CUSTOM_QUESTION_PROMPT,\n",
        "  )\n",
        "\n",
        "  result = qa_chain({\"question\": query})\n",
        "  return result[\"answer\"].strip()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "PnBDJ13BRJmT",
        "outputId": "76587069-10b6-4bac-ed96-72d564c20268"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
            "Running on public URL: https://2d1207847c32deffa4.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div><iframe src=\"https://2d1207847c32deffa4.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "iface = gr.ChatInterface(\n",
        "    fn = querying,\n",
        "    chatbot=gr.Chatbot(height=600),\n",
        "    textbox=gr.Textbox(placeholder=\"Ask your question\", container=False, scale=7),\n",
        "    title=\"Chat\",\n",
        "    theme=\"soft\",\n",
        "    # examples=[\"Why Hiberus has created GenAI Ecosystem?\",\n",
        "    #           \"What is GenAI Ecosystem?\"],\n",
        "    # cache_examples=True,\n",
        "    retry_btn=\"Retry\",\n",
        "    undo_btn=\"Undo\",\n",
        "    clear_btn=\"Clear\",\n",
        "    submit_btn=\"Submit\"\n",
        "\n",
        "    )\n",
        "\n",
        "iface.launch(share=True, debug=True)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.11.8"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
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