diff --git a/IndianLawLLM.ipynb b/IndianLawLLM.ipynb
new file mode 100644
index 00000000..b8267056
--- /dev/null
+++ b/IndianLawLLM.ipynb
@@ -0,0 +1,819 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "57b3de00-61f6-418a-b837-57a207ee1c2c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install langchain -q --user "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "c8c66634-87ee-4799-a29a-e9505b1edc4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install transformers -q --user "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "0064e947-4ded-4bce-925b-66042f25f063",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install pypdf -q --user"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "367609ba-2daf-458d-9e31-b0bfc3b96f56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install sentence-transformers -q --user"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "4ea8f326-4535-41df-9c04-8b094c43072b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install chromadb -q --user"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "5102e563-bbd0-4cac-9c58-a4a64ba82e8b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install google-cloud -q --user"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "42f71c23-935e-44a0-894a-34af021f5955",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: intel_extension_for_pytorch in /home/ub9a04e0e61464b0670ffe393523c081/.local/lib/python3.9/site-packages (2.1.0)\n",
+      "Requirement already satisfied: psutil in /opt/intel/oneapi/intelpython/python3.9/lib/python3.9/site-packages (from intel_extension_for_pytorch) (5.9.0)\n",
+      "Requirement already satisfied: numpy in /opt/intel/oneapi/intelpython/python3.9/lib/python3.9/site-packages (from intel_extension_for_pytorch) (1.24.3)\n",
+      "Requirement already satisfied: packaging in /opt/intel/oneapi/intelpython/python3.9/lib/python3.9/site-packages (from intel_extension_for_pytorch) (23.1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install intel_extension_for_pytorch --user"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "b1261631-421a-4df5-85ce-c6b1b64f642d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "import re"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "8132a8ff-2c53-4ed7-ab16-afc1695c1ca3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "\n",
+    "# Suppress warnings for a cleaner output\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "5b1fe6e1-fb17-40ba-9cd4-ccdf4905dc38",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "7bd92eb1-f9ed-4518-a536-95654b75f92e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import intel_extension_for_pytorch as ipex"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "6e6c20bb-7299-4057-9b7e-2611a12d6105",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import necessary modules and classes\n",
+    "from langchain.vectorstores import Chroma\n",
+    "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM \n",
+    "from transformers import pipeline\n",
+    "from langchain.llms import HuggingFacePipeline\n",
+    "from langchain.embeddings import SentenceTransformerEmbeddings\n",
+    "from langchain.chains import RetrievalQA\n",
+    "from langchain.document_loaders import PyPDFLoader, DirectoryLoader\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "7a389773-65bb-4318-9d17-b69d60459c38",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_device_type():\n",
+    "    # random seed\n",
+    "    if torch.cuda.is_available():\n",
+    "        print(\"GPU is available\")\n",
+    "        seed = 88\n",
+    "        random.seed(seed)\n",
+    "        torch.cuda.manual_seed(seed)\n",
+    "        torch.cuda.manual_seed_all(seed)\n",
+    "        return torch.device(\"gpu\")\n",
+    "    elif torch.xpu.is_available():\n",
+    "        print(\"GPU is not available\")\n",
+    "        print(\"XPU is available\")\n",
+    "        seed = 88\n",
+    "        random.seed(seed)\n",
+    "        torch.xpu.manual_seed(seed)\n",
+    "        torch.xpu.manual_seed_all(seed)\n",
+    "        return torch.device(\"xpu\")\n",
+    "    else:\n",
+    "        print(\"XPU or GPU not available - returning with CPU\")\n",
+    "        return torch.device(\"cpu\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "447e72b8-c930-4e88-a823-4ebb1575d06a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "XPU or GPU not available - returning with CPU\n",
+      "cpu\n"
+     ]
+    }
+   ],
+   "source": [
+    "device_type = get_device_type()\n",
+    "print(device_type)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "d4fad5a3-cd22-430f-8534-20281ca40b3b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MODEL_CACHE_PATH = \"/home/common/data/Big_Data/GenAI/llm_models\"\n",
+    "\n",
+    "class ChatBotModel:\n",
+    "    \"\"\"\n",
+    "    ChatBotModel is a class for generating responses based on text prompts using a pretrained model.\n",
+    "\n",
+    "    Attributes:\n",
+    "    - model_id_or_path: model Id for text generation. Default is \"\"MBZUAI/LaMini-Flan-T5-783M\"\"\n",
+    "    - torch_dtype: The data type to use in the model.\n",
+    "    - optimize : If True Intel Optimizer for pytorch is used.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        model_id_or_path: str = \"MBZUAI/LaMini-Flan-T5-783M\",  \n",
+    "        torch_dtype: torch.dtype = torch.bfloat16,\n",
+    "        optimize: bool = True,\n",
+    "    ) -> None:\n",
+    "        \"\"\"\n",
+    "        The initializer for ChatBotModel class.\n",
+    "\n",
+    "        Parameters:\n",
+    "        - model_id_or_path: The identifier or path of the pretrained model.\n",
+    "        - torch_dtype: The data type to use in the model. Default is torch.bfloat16.\n",
+    "        - optimize: If True, ipex is used to optimized the model\n",
+    "        \"\"\"\n",
+    "        self.torch_dtype = torch_dtype\n",
+    "        self.device = get_device_type()\n",
+    "        self.model_id_or_path = model_id_or_path\n",
+    "        local_model_id = self.model_id_or_path.replace(\"/\", \"--\")\n",
+    "        local_model_path = os.path.join(MODEL_CACHE_PATH, local_model_id)\n",
+    "        \n",
+    "\n",
+    "        if (\n",
+    "            self.device == self.device.startswith(\"xpu\")\n",
+    "            if isinstance(self.device, str)\n",
+    "            else self.device.type == \"xpu\"\n",
+    "        ):\n",
+    "\n",
+    "            self.autocast = torch.xpu.amp.autocast\n",
+    "        else:\n",
+    "            self.autocast = torch.cpu.amp.autocast\n",
+    "        self.torch_dtype = torch_dtype\n",
+    "        print(f\"torch_dtype : {self.torch_dtype}\")\n",
+    "        print(f\"device : {self.device}\")\n",
+    "        print(f\"model_id_or_path : {self.model_id_or_path}\")\n",
+    "        print(f\"local_model_id : {local_model_id}\")\n",
+    "        print(f\"local_model_path : {local_model_path}\")\n",
+    "        try:\n",
+    "            # Initialize the tokenizer and base model for text generation\n",
+    "            self.tokenizer = AutoTokenizer.from_pretrained(local_model_path, \n",
+    "                                                           trust_remote_code=True )\n",
+    "            self.model = (\n",
+    "                        AutoModelForSeq2SeqLM.from_pretrained(\n",
+    "                            local_model_path,\n",
+    "                            device_map=\"auto\",\n",
+    "                            low_cpu_mem_usage=True,\n",
+    "                            trust_remote_code=True,\n",
+    "                            torch_dtype=torch.float32\n",
+    "                        )\n",
+    "                        .to(self.device)\n",
+    "                        .eval()\n",
+    "                        )\n",
+    "        except (OSError, ValueError, EnvironmentError) as e:\n",
+    "            logging.info(\n",
+    "                f\"Tokenizer / model not found locally. Downloading tokenizer / model for {self.model_id_or_path} to cache...: {e}\"\n",
+    "            )\n",
+    "            self.tokenizer = AutoTokenizer.from_pretrained(self.model_id_or_path, trust_remote_code=True )\n",
+    "            self.model = (\n",
+    "                        AutoModelForSeq2SeqLM.from_pretrained(\n",
+    "                            self.model_id_or_path,\n",
+    "                            device_map=\"auto\",\n",
+    "                            low_cpu_mem_usage=True,\n",
+    "                            trust_remote_code=True,\n",
+    "                            torch_dtype=torch.float32\n",
+    "                        )\n",
+    "                        .to(self.device)\n",
+    "                        .eval()\n",
+    "                        )\n",
+    "           \n",
+    "        self.max_length = 256\n",
+    "        if optimize:\n",
+    "            if hasattr(ipex, \"optimize_transformers\"):\n",
+    "                try:\n",
+    "                    ipex.optimize_transformers(self.model, dtype=self.torch_dtype)\n",
+    "                except:\n",
+    "                    ipex.optimize(self.model, dtype=self.torch_dtype)\n",
+    "                else:\n",
+    "                    ipex.optimize(self.model, dtype=self.torch_dtype)\n",
+    "        print(\"ChatBotModel Initialization Complete\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "80426f7d-b0e1-40be-a4f0-36405efbd96c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-12-06 09:52:51,388 - root - INFO - Tokenizer / model not found locally. Downloading tokenizer / model for MBZUAI/LaMini-Flan-T5-783M to cache...: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/home/common/data/Big_Data/GenAI/llm_models/MBZUAI--LaMini-Flan-T5-783M'. Use `repo_type` argument if needed.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "XPU or GPU not available - returning with CPU\n",
+      "torch_dtype : torch.bfloat16\n",
+      "device : cpu\n",
+      "model_id_or_path : MBZUAI/LaMini-Flan-T5-783M\n",
+      "local_model_id : MBZUAI--LaMini-Flan-T5-783M\n",
+      "local_model_path : /home/common/data/Big_Data/GenAI/llm_models/MBZUAI--LaMini-Flan-T5-783M\n",
+      "ChatBotModel Initialization Complete\n"
+     ]
+    }
+   ],
+   "source": [
+    "chat_bot_model = ChatBotModel()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fc526da0-e777-4396-b219-c7512de7490b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "962394e6-5b4d-4e2b-86dd-f4cd3b611568",
+   "metadata": {},
+   "source": [
+    "# Getting Data From PDFs - Save in Vector DB"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "78f669e5-3be9-4e34-bb89-607018c24f5c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize a directory loader to load PDF documents from a directory# Define the directory where the Chroma database will persist its data\n",
+    "persist_directory = \"choma-db\"\n",
+    "\n",
+    "# Make sure the directory exists, create if it doesn't\n",
+    "\n",
+    "if not os.path.exists(persist_directory):\n",
+    "    os.makedirs(persist_directory)\n",
+    "\n",
+    "# Initialize a directory loader to load PDF documents from a directory\n",
+    "loader = DirectoryLoader(\"law_data\", glob=\"./*.pdf\", loader_cls=PyPDFLoader)\n",
+    "documents = loader.load()\n",
+    "\n",
+    "# Initialize a text splitter to split documents into smaller chunks\n",
+    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=200)\n",
+    "\n",
+    "# Split the loaded documents into chunks\n",
+    "texts = text_splitter.split_documents(documents)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "eb5e7dc1-28f7-40ba-9fd4-2e3b15609a32",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-12-06 09:57:35,950 - sentence_transformers.SentenceTransformer - INFO - Load pretrained SentenceTransformer: multi-qa-mpnet-base-dot-v1\n",
+      "2023-12-06 09:57:38,427 - sentence_transformers.SentenceTransformer - INFO - Use pytorch device: cpu\n",
+      "2023-12-06 09:57:38,764 - chromadb.telemetry.product.posthog - INFO - Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2380f0d843b34cad85fa162353128cb7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/474 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Creating a Vector DB using Chroma DB and SentenceTransformerEmbeddings\n",
+    "# Initialize SentenceTransformerEmbeddings with a pre-trained model\n",
+    "embeddings = SentenceTransformerEmbeddings(model_name=\"multi-qa-mpnet-base-dot-v1\")\n",
+    "# Create a Chroma vector database from the text chunks\n",
+    "db = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "c1163e40-0848-445f-ba33-0d8da42d55b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To save and load the saved vector db (if needed in the future)\n",
+    "# Persist the database to disk\n",
+    "db.persist()\n",
+    "db = Chroma(persist_directory=persist_directory, embedding_function=embeddings)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77c93e8a-05c0-40d4-b35b-c9f89bce1d8c",
+   "metadata": {},
+   "source": [
+    "# RetrievalQA chain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "053a30a8-1e6c-4bf5-92b7-863a90cdad49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a text generation pipeline\n",
+    "pipe = pipeline(\n",
+    "    'text2text-generation',\n",
+    "    model = chat_bot_model.model,\n",
+    "    tokenizer = chat_bot_model.tokenizer,\n",
+    "    max_length = 512,\n",
+    "    do_sample = True,\n",
+    "    temperature = 0.3,\n",
+    "    top_p= 0.95\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "b62af8f7-04f4-4e92-a17d-46d4559c2d0c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize a local language model pipeline\n",
+    "local_llm = HuggingFacePipeline(pipeline=pipe)\n",
+    "# Create a RetrievalQA chain\n",
+    "qa_chain = RetrievalQA.from_chain_type(\n",
+    "    llm=local_llm,\n",
+    "    chain_type='stuff',\n",
+    "    retriever=db.as_retriever(search_type=\"similarity\", search_kwargs={\"k\": 2}),\n",
+    "    return_source_documents=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cde1fccb-6c47-49bf-a20d-d8ca3aeaa530",
+   "metadata": {},
+   "source": [
+    "# User Interactions "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "3d1b9e66-39da-49c9-870c-eac072bc2641",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def chat_bot_front_end():\n",
+    "    while True:\n",
+    "        # Prompt the user for a query\n",
+    "        input_query = str(input(\"Enter your query (or 'EXIT' to stop): \"))\n",
+    "    \n",
+    "        # Check if the user entered 'EXIT' to stop the program\n",
+    "        if input_query.upper() == 'EXIT':\n",
+    "            print(\"Exiting...\")\n",
+    "            break\n",
+    "    \n",
+    "        # Execute the query using the QA chain\n",
+    "        # Assuming qa_chain is a predefined function or mechanism to process the query\n",
+    "        llm_response = qa_chain({\"query\": input_query})\n",
+    "    \n",
+    "        # Print the response\n",
+    "        print(llm_response['result'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9cf9e1dc-6ae0-43f4-9c46-a2488db1b7fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Enter your query (or 'EXIT' to stop):  what is IPC\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7b684a69325e4da6a3a3b010bb3572fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "IPC is a general provision in the Indian Penal Code, 1860 that deals with attempts to commit offenses and makes them punishable with imprisonment, not only those punishable for life or death.\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Enter your query (or 'EXIT' to stop):  More information of IPC\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "55877229985a42e59f4faac5de0a6892",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "IPC stands for Indian Penal Code.\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Enter your query (or 'EXIT' to stop):  What is Article\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47de06f2a26241afa89be5bd55316dac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The article is helpful in ascertaining the intention of the accused Hindu who ridicules the Prophet of the Mohammedans.\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Enter your query (or 'EXIT' to stop):  What is Section\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "91d162bbc9b2483a91daa3695d0f3a68",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Section is (1) intention or knowledge relating to commission of murder; and (2) the doing of an act towards it.\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Enter your query (or 'EXIT' to stop):  how many sections are there\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c0e93c72759c40ac9b185248b15874ca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are two sections mentioned in the context.\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Enter your query (or 'EXIT' to stop):  name few Indian Penal Code\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c76479a6d2ac4b6881361ba63eb1a54e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The Indian Penal Code is not mentioned in the provided context.\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Enter your query (or 'EXIT' to stop):  who drafted Indian penal Code ?\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1d5c7a1706b64640a635487bea6fb222",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The Indian Penal Code was drafted by the First Indian Law Commission presided over by Lord Thomas Babington Macaulay.\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Enter your query (or 'EXIT' to stop):  Explain more of Contempt of Supreme Court and High Courts\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a6e0eb18ff6e478fb0f3100dec5c23cc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Contempt of Supreme Court and High Courts refers to the act of defying the jurisdiction, powers, and authority of the courts, except those contained in the Contempt of Courts Act, 1971.94.\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Enter your query (or 'EXIT' to stop):  Give more of Section228 0f IPC\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3af6a25e7b3f4f55adc22bcf29de7e32",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I'm sorry, but the given context does not provide any information about Section228 of IPC. Therefore, I cannot provide a helpful answer to the question.\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Enter your query (or 'EXIT' to stop):  Give more of Section 228 of IPC\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ebfd66cec83a480185838bf772ac0c15",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Section 228 of IPC, 1860 makes disclosure of identity of victim of certain offences punishable. Printing or publishing name of any matter which may make known the identity of any person against whom an offence under sections 376, 376A, 376B, 376C or 376D is alleged or found to have been committed can be punished. Keeping in view the social object of preventing social victimisation or ostracism of the victim of a.\n"
+     ]
+    }
+   ],
+   "source": [
+    "chat_bot_front_end()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0bbff479-17d8-4e90-a286-af71d1c74bd6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pytorch-gpu",
+   "language": "python",
+   "name": "pytorch-gpu"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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diff --git a/law_data/IndianPenalCodeBook_1.pdf b/law_data/IndianPenalCodeBook_1.pdf
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