{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "ylbT549oymIl"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/home/vasim/Khatir/Programming/ML Projects/gemini-pro with docs/.venv/lib/python3.10/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"
          ]
        },
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 1,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import os\n",
        "from langchain.document_loaders import (\n",
        "    PyPDFLoader,\n",
        "    TextLoader,\n",
        "    Docx2txtLoader\n",
        ")\n",
        "\n",
        "from langchain.text_splitter import CharacterTextSplitter\n",
        "# from PyPDF2 import PdfReader\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain_google_genai import GoogleGenerativeAIEmbeddings\n",
        "import google.generativeai as genai\n",
        "from langchain.vectorstores import FAISS\n",
        "from langchain_google_genai import ChatGoogleGenerativeAI\n",
        "from langchain.chains.question_answering import load_qa_chain\n",
        "from langchain.prompts import PromptTemplate\n",
        "from langchain.memory import ConversationBufferMemory\n",
        "from dotenv import load_dotenv\n",
        "load_dotenv()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "os.chdir(\"../\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "from src.utils import (\n",
        "    process_files, answer_query, extract_text_from_file\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "a8tNUutJB9EA"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Running on local URL:  http://127.0.0.1:7862\n",
            "\n",
            "To create a public link, set `share=True` in `launch()`.\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div><iframe src=\"http://127.0.0.1:7862/\" 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"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Text extracted\n",
            "Chunks splitted\n",
            "Document search created\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/home/vasim/Khatir/Programming/ML Projects/gemini-pro with docs/.venv/lib/python3.10/site-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The function `__call__` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.\n",
            "  warn_deprecated(\n"
          ]
        }
      ],
      "source": [
        "# Gradio App\n",
        "import gradio as gr\n",
        "\n",
        "gr.close_all()\n",
        "\n",
        "title = \"\"\n",
        "description = f\"Chat with any docs\"\n",
        "\n",
        "# def answer_query(message, history):\n",
        "#     docs = db.similarity_search(message)\n",
        "#     message = agent(\n",
        "#         {\"input_documents\":docs, \"question\": message}\n",
        "#         ,return_only_outputs=True)\n",
        "#     return message['output_text']\n",
        "\n",
        "\n",
        "chatbot = gr.Chatbot(label=\"ExploreText\")\n",
        "\n",
        "with gr.Blocks(\n",
        "    title=\"ExploreText\",\n",
        "    ) as textbot:\n",
        "\n",
        "    gr.Markdown(\"# <center> Welcome to ExploreDoc Web App</center>\")\n",
        "    \n",
        "    with gr.Accordion(\"Upload a file here\", open=False):\n",
        "        file_output = gr.File()\n",
        "        upload_button = gr.UploadButton(\"Click to Upload a File\", file_types=[\"txt\",\"doc\",\"pdf\"])\n",
        "        upload_button.upload(process_files, upload_button, file_output)\n",
        "\n",
        "    # with gr.Row(\"Chat with Text\"):\n",
        "    gr.ChatInterface(fn=answer_query, chatbot=chatbot, submit_btn=\"Ask\", undo_btn=None, retry_btn=None, clear_btn=None)\n",
        "    gr.Markdown(\"<center>  Developed by <a href='https://92-vasim.github.io' target='_blank'>Mohammed Vasim<a/> | AI Engineer & Computer Vision Engineer @ ZestIoT.  </center>\")\n",
        "        \n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    textbot.queue().launch()\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "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": "3.10.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}