llama3.2 copy.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI\n",
        "from langchain.embeddings import CacheBackedEmbeddings\n",
        "from langchain.vectorstores import FAISS\n",
        "from langchain.chains.question_answering import load_qa_chain\n",
        "from langchain.prompts import PromptTemplate\n",
        "import os\n",
        "from dotenv import load_dotenv\n",
        "load_dotenv()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "import google.generativeai as genai\n",
        "genai.configure(api_key=os.getenv(\"GOOGLE_API_KEY\"))\n",
        "\n",
        "# Load and split the text file\n",
        "with open(\"general.txt\", encoding=\"utf8\") as f:\n",
        "    raw_text = f.read()\n",
        "\n",
        "text_splitter = RecursiveCharacterTextSplitter(\n",
        "    chunk_size=1000,\n",
        "    chunk_overlap=100,\n",
        "    length_function=len,\n",
        ")\n",
        "text_chunks = text_splitter.split_text(raw_text)\n",
        "\n",
        "# Create embeddings and vector store\n",
        "embeddings = GoogleGenerativeAIEmbeddings(model=\"models/embedding-001\")\n",
        "vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)\n",
        "vector_store.save_local(\"faiss_index\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [],
      "source": [
        "def get_conversational_chain():\n",
        "    \"\"\"Create a conversational chain for answering questions.\"\"\"\n",
        "    prompt_template = \"\"\"\n",
        "    Answer the question as detailed as possible from the provided context, make sure to provide all the details,\n",
        "\n",
        "    Context:\\n {context}\\n\n",
        "    Question: \\n{question}\\n\n",
        "    Answer:\n",
        "    \"\"\"\n",
        "\n",
        "    model = ChatGoogleGenerativeAI(model=\"gemini-pro\", temperature=0.3)\n",
        "\n",
        "    prompt = PromptTemplate(\n",
        "        template=prompt_template, input_variables=[\"context\", \"question\"]\n",
        "    )\n",
        "\n",
        "    # Load a question-answering chain with the specified model and prompt\n",
        "    chain = load_qa_chain(model, chain_type=\"stuff\", prompt=prompt)\n",
        "\n",
        "    return chain\n",
        "\n",
        "def answer_question(user_question):\n",
        "    new_db = FAISS.load_local(\"faiss_index\", embeddings, allow_dangerous_deserialization=True)\n",
        "\n",
        "    docs = new_db.similarity_search(user_question)\n",
        "\n",
        "    chain = get_conversational_chain()\n",
        "\n",
        "    response = chain(\n",
        "        {\"input_documents\": docs, \"question\": user_question}, return_only_outputs=True\n",
        "    )\n",
        "\n",
        "    return response[\"output_text\"]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Chat with the context loaded from 'general.txt'\n",
            "\n",
            "Answer: The provided context does not contain any information about \"hi\". Therefore, I cannot answer this question from the provided context.\n",
            "\n",
            "\n",
            "Answer: **Srishti - The Literary Club**\n",
            "\n",
            "* Provides a platform for students to showcase their literary aspirations and talents.\n",
            "* Hosts events such as poetry-reading sessions, debates, extempore speeches, quizzes, cross-word games, book and film reviews, play enactments, and classical movie screenings.\n",
            "* Organizes events in regional languages like Tamil, Malayalam, Telugu, and Hindi.\n",
            "\n",
            "**Natyasudha - The Dance Club**\n",
            "\n",
            "* Focuses on dance performances and training.\n",
            "\n",
            "**Ragasudha - The Music Club**\n",
            "\n",
            "* Focuses on music performances and training.\n",
            "\n",
            "**Amrita Talkies**\n",
            "\n",
            "* Aims to provide members with an experience and technical knowledge on film appreciation and filmmaking.\n",
            "* Conducts movie screenings, workshops, and competitions.\n",
            "\n",
            "**Sahaya Club**\n",
            "\n",
            "* A social club that aims to help the needy, including children and the elderly.\n",
            "\n",
            "**Intel IoT Club**\n",
            "\n",
            "* Empowers and nurtures students in the fields of IoT and AI.\n",
            "* Offers events, workshops, industrial trainings, and resources to enhance students' skills.\n",
            "\n",
            "**Arya Club**\n",
            "\n",
            "* Explores fundamental life-related problems, current and past affairs, and personality study.\n",
            "* Promotes patriotism and the importance of Indian culture and heritage.\n",
            "\n",
            "Exiting. Goodbye!\n"
          ]
        }
      ],
      "source": [
        "print(\"Chat with the context loaded from 'general.txt'\")\n",
        "while True:\n",
        "    user_question = input(\"Ask a question (or type 'exit' to quit): \")\n",
        "    if user_question.lower() == 'exit':\n",
        "        print(\"Exiting. Goodbye!\")\n",
        "        break\n",
        "    \n",
        "    answer = answer_question(user_question)\n",
        "    print(f\"\\nAnswer: {answer}\\n\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "3npp2iNHTzQl",
        "outputId": "c8d3a397-fce1-439a-a5cc-8aa8f75903b3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'1994'"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "answer_question(\"When did the Coimbatore campus start ?\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:     Started server process [14008]\n",
            "INFO:     Waiting for application startup.\n",
            "INFO:     Application startup complete.\n",
            "INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INFO:     127.0.0.1:63340 - \"POST /get-response HTTP/1.1\" 307 Temporary Redirect\n",
            "INFO:     127.0.0.1:63340 - \"POST /get-response/ HTTP/1.1\" 200 OK\n",
            "INFO:     127.0.0.1:63386 - \"POST /get-response HTTP/1.1\" 307 Temporary Redirect\n",
            "INFO:     127.0.0.1:63389 - \"POST /get-response HTTP/1.1\" 307 Temporary Redirect\n",
            "INFO:     127.0.0.1:63386 - \"POST /get-response/ HTTP/1.1\" 200 OK\n",
            "INFO:     127.0.0.1:63390 - \"POST /get-response/ HTTP/1.1\" 200 OK\n",
            "INFO:     127.0.0.1:63488 - \"POST /get-response HTTP/1.1\" 307 Temporary Redirect\n",
            "INFO:     127.0.0.1:63488 - \"POST /get-response/ HTTP/1.1\" 200 OK\n",
            "INFO:     127.0.0.1:63491 - \"POST /get-response HTTP/1.1\" 307 Temporary Redirect\n",
            "INFO:     127.0.0.1:63492 - \"POST /get-response/ HTTP/1.1\" 200 OK\n",
            "INFO:     127.0.0.1:63700 - \"POST /get-response HTTP/1.1\" 307 Temporary Redirect\n",
            "INFO:     127.0.0.1:63700 - \"POST /get-response/ HTTP/1.1\" 200 OK\n",
            "INFO:     127.0.0.1:63702 - \"OPTIONS /get-response HTTP/1.1\" 200 OK\n",
            "INFO:     127.0.0.1:63703 - \"POST /get-response HTTP/1.1\" 307 Temporary Redirect\n",
            "INFO:     127.0.0.1:63702 - \"OPTIONS /get-response/ HTTP/1.1\" 200 OK\n",
            "INFO:     127.0.0.1:63703 - \"POST /get-response/ HTTP/1.1\" 200 OK\n"
          ]
        }
      ],
      "source": [
        "import uvicorn\n",
        "from fastapi.middleware.cors import CORSMiddleware\n",
        "from fastapi import FastAPI, Request\n",
        "from pydantic import BaseModel\n",
        "from typing import List, Dict, Any\n",
        "from langchain.vectorstores import FAISS\n",
        "\n",
        "\n",
        "conversation_context: Dict[str, Dict[str, List[Dict[str, Any]]]] = {}\n",
        "\n",
        "class QueryRequest(BaseModel):\n",
        "    session_id: str\n",
        "    input_text: str\n",
        "\n",
        "app = FastAPI()\n",
        "\n",
        "@app.get(\"/\")\n",
        "def index():\n",
        "    return {\"message\": \"Hello! Use the /get-response endpoint to chat.\"}\n",
        "\n",
        "@app.post(\"/get-response/\")\n",
        "def get_response(request: QueryRequest):\n",
        "    \"\"\"\n",
        "    Handles a conversational API endpoint.\n",
        "    \"\"\"\n",
        "    session_id = request.session_id\n",
        "    user_input = request.input_text\n",
        "\n",
        "    # Retrieve or initialize the context for the session\n",
        "    if session_id not in conversation_context:\n",
        "        conversation_context[session_id] = {\"history\": []}\n",
        "\n",
        "    # Add user input to the conversation history\n",
        "    conversation_history = conversation_context[session_id][\"history\"]\n",
        "    conversation_history.append({\"user\": user_input})\n",
        "\n",
        "    # Generate a response using the conversational chain\n",
        "    response = answer_question(user_input)\n",
        "\n",
        "    # Add the response to the conversation history\n",
        "    conversation_history.append({\"bot\": response})\n",
        "\n",
        "    # Save the updated history\n",
        "    conversation_context[session_id][\"history\"] = conversation_history\n",
        "\n",
        "    return {\"session_id\": session_id, \"response\": response, \"history\": conversation_history}\n",
        "\n",
        "# CORS setup\n",
        "origins = [\"*\"]\n",
        "\n",
        "app.add_middleware(\n",
        "    CORSMiddleware,\n",
        "    allow_origins=origins,\n",
        "    allow_credentials=True,\n",
        "    allow_methods=[\"*\"],\n",
        "    allow_headers=[\"*\"],\n",
        ")\n",
        "\n",
        "# Main server setup\n",
        "if __name__ == \"__main__\":\n",
        "    config = uvicorn.Config(app, host=\"127.0.0.1\", port=8000, log_level=\"info\")\n",
        "    server = uvicorn.Server(config)\n",
        "    asyncio.run(server.serve())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:     Started server process [14008]\n",
            "INFO:     Waiting for application startup.\n",
            "INFO:     Application startup complete.\n",
            "INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)\n",
            "Task exception was never retrieved\n",
            "future: <Task finished name='Task-1' coro=<Server.serve() done, defined at c:\\Users\\nithi\\anaconda3\\envs\\College\\Lib\\site-packages\\uvicorn\\server.py:68> exception=KeyboardInterrupt()>\n",
            "Traceback (most recent call last):\n",
            "  File \"c:\\Users\\nithi\\anaconda3\\envs\\College\\Lib\\site-packages\\uvicorn\\main.py\", line 579, in run\n",
            "    server.run()\n",
            "  File \"c:\\Users\\nithi\\anaconda3\\envs\\College\\Lib\\site-packages\\uvicorn\\server.py\", line 66, in run\n",
            "    return asyncio.run(self.serve(sockets=sockets))\n",
            "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"c:\\Users\\nithi\\anaconda3\\envs\\College\\Lib\\site-packages\\nest_asyncio.py\", line 30, in run\n",
            "    return loop.run_until_complete(task)\n",
            "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"c:\\Users\\nithi\\anaconda3\\envs\\College\\Lib\\site-packages\\nest_asyncio.py\", line 92, in run_until_complete\n",
            "    self._run_once()\n",
            "  File \"c:\\Users\\nithi\\anaconda3\\envs\\College\\Lib\\site-packages\\nest_asyncio.py\", line 133, in _run_once\n",
            "    handle._run()\n",
            "  File \"c:\\Users\\nithi\\anaconda3\\envs\\College\\Lib\\asyncio\\events.py\", line 88, in _run\n",
            "    self._context.run(self._callback, *self._args)\n",
            "  File \"c:\\Users\\nithi\\anaconda3\\envs\\College\\Lib\\asyncio\\tasks.py\", line 396, in __wakeup\n",
            "    self.__step()\n",
            "  File \"c:\\Users\\nithi\\anaconda3\\envs\\College\\Lib\\asyncio\\tasks.py\", line 303, in __step\n",
            "    self.__step_run_and_handle_result(exc)\n",
            "  File \"c:\\Users\\nithi\\anaconda3\\envs\\College\\Lib\\asyncio\\tasks.py\", line 314, in __step_run_and_handle_result\n",
            "    result = coro.send(None)\n",
            "             ^^^^^^^^^^^^^^^\n",
            "  File \"c:\\Users\\nithi\\anaconda3\\envs\\College\\Lib\\site-packages\\uvicorn\\server.py\", line 69, in serve\n",
            "    with self.capture_signals():\n",
            "         ^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"c:\\Users\\nithi\\anaconda3\\envs\\College\\Lib\\contextlib.py\", line 144, in __exit__\n",
            "    next(self.gen)\n",
            "  File \"c:\\Users\\nithi\\anaconda3\\envs\\College\\Lib\\site-packages\\uvicorn\\server.py\", line 330, in capture_signals\n",
            "    signal.raise_signal(captured_signal)\n",
            "KeyboardInterrupt\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INFO:     127.0.0.1:62647 - \"GET /get-response/When%20did%20the%20Coimbatore%20campus%20start HTTP/1.1\" 200 OK\n",
            "INFO:     127.0.0.1:62664 - \"GET /get-response/what%20are%20the%20clubs%20at%20amrita HTTP/1.1\" 200 OK\n",
            "INFO:     127.0.0.1:62669 - \"GET /explain%20them HTTP/1.1\" 404 Not Found\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:     Shutting down\n",
            "INFO:     Waiting for application shutdown.\n",
            "INFO:     Application shutdown complete.\n",
            "INFO:     Finished server process [14008]\n"
          ]
        }
      ],
      "source": [
        "import nest_asyncio\n",
        "import uvicorn\n",
        "from fastapi.middleware.cors import CORSMiddleware\n",
        "from fastapi import FastAPI\n",
        "\n",
        "nest_asyncio.apply()\n",
        "\n",
        "app = FastAPI()\n",
        "\n",
        "@app.get(\"/\")\n",
        "def index():\n",
        "    return {\"hello\": \"world\"}\n",
        "\n",
        "@app.get(\"/get-response/{input_text}\")\n",
        "def get_student(input_text: str):\n",
        "    output = answer_question(input_text)\n",
        "    return {\"response\": f\"Mocked response for: {output}\"}\n",
        "\n",
        "origins = [\"*\"]\n",
        "\n",
        "app.add_middleware(\n",
        "    CORSMiddleware,\n",
        "    allow_origins=origins,\n",
        "    allow_credentials=True,\n",
        "    allow_methods=[\"*\"],\n",
        "    allow_headers=[\"*\"],\n",
        ")\n",
        "\n",
        "# Use this block for Jupyter or environments with an active event loop\n",
        "uvicorn.run(app, host=\"127.0.0.1\", port=8000)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zZkRSwAKSCME"
      },
      "source": [
        "## **FASTAPI**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "sPmOWMqBYF9Q"
      },
      "outputs": [],
      "source": [
        "import locale\n",
        "locale.getpreferredencoding = lambda: \"UTF-8\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "P7z6uuZ-6Q0g"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
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            "                                                                                                    \n",
            "Installing ngrok ... \n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n",
            "  File \"C:\\Users\\nithi\\anaconda3\\envs\\college\\Lib\\site-packages\\pyngrok\\installer.py\", line 125, in install_ngrok\n",
            "    _install_ngrok_zip(ngrok_path, download_path)\n",
            "  File \"C:\\Users\\nithi\\anaconda3\\envs\\college\\Lib\\site-packages\\pyngrok\\installer.py\", line 140, in _install_ngrok_zip\n",
            "    with zipfile.ZipFile(zip_path, \"r\") as zip_ref:\n",
            "         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"C:\\Users\\nithi\\anaconda3\\envs\\college\\Lib\\zipfile\\__init__.py\", line 1331, in __init__\n",
            "    self.fp = io.open(file, filemode)\n",
            "              ^^^^^^^^^^^^^^^^^^^^^^^\n",
            "OSError: [Errno 22] Invalid argument: 'C:\\\\Users\\\\nithi\\\\AppData\\\\Local\\\\Temp\\\\ngrok-v3-stable-windows-amd64.zip'\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"<frozen runpy>\", line 198, in _run_module_as_main\n",
            "  File \"<frozen runpy>\", line 88, in _run_code\n",
            "  File \"C:\\Users\\nithi\\anaconda3\\envs\\College\\Scripts\\ngrok.exe\\__main__.py\", line 7, in <module>\n",
            "  File \"C:\\Users\\nithi\\anaconda3\\envs\\college\\Lib\\site-packages\\pyngrok\\ngrok.py\", line 597, in main\n",
            "    run(sys.argv[1:])\n",
            "  File \"C:\\Users\\nithi\\anaconda3\\envs\\college\\Lib\\site-packages\\pyngrok\\ngrok.py\", line 583, in run\n",
            "    install_ngrok(pyngrok_config)\n",
            "  File \"C:\\Users\\nithi\\anaconda3\\envs\\college\\Lib\\site-packages\\pyngrok\\ngrok.py\", line 99, in install_ngrok\n",
            "    installer.install_ngrok(pyngrok_config.ngrok_path, ngrok_version=pyngrok_config.ngrok_version)\n",
            "  File \"C:\\Users\\nithi\\anaconda3\\envs\\college\\Lib\\site-packages\\pyngrok\\installer.py\", line 127, in install_ngrok\n",
            "    raise PyngrokNgrokInstallError(f\"An error occurred while downloading ngrok from {url}: {e}\")\n",
            "pyngrok.exception.PyngrokNgrokInstallError: An error occurred while downloading ngrok from https://bin.equinox.io/c/bNyj1mQVY4c/ngrok-v3-stable-windows-amd64.zip: [Errno 22] Invalid argument: 'C:\\\\Users\\\\nithi\\\\AppData\\\\Local\\\\Temp\\\\ngrok-v3-stable-windows-amd64.zip'\n"
          ]
        }
      ],
      "source": [
        "!ngrok config add-authtoken 2byhqoCrlLHUUIkJkBxspq8hs1R_6cPvKVeHHsTTuPwE3WTiw"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "7zR64dCa4vnJ"
      },
      "outputs": [],
      "source": [
        "from fastapi import FastAPI\n",
        "from pydantic import BaseModel\n",
        "import uvicorn\n",
        "from pyngrok import ngrok\n",
        "from fastapi.middleware.cors import CORSMiddleware\n",
        "import nest_asyncio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "qssnGJgs5m2Y"
      },
      "outputs": [],
      "source": [
        "app = FastAPI()\n",
        "\n",
        "@app.get(\"/\")\n",
        "def index():\n",
        "    return {'hello' : 'world'}\n",
        "\n",
        "@app.get('/get-response/{input_text}')\n",
        "def get_student(input_text : str):\n",
        "    return generate_response(input_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "eIK5F4lS5Z6Q"
      },
      "outputs": [],
      "source": [
        "origins = [\"*\"]\n",
        "\n",
        "app.add_middleware(\n",
        "    CORSMiddleware,\n",
        "    allow_origins=origins,\n",
        "    allow_credentials=True,\n",
        "    allow_methods=[\"*\"],\n",
        "    allow_headers=[\"*\"],\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kyMm24tm6GB5",
        "outputId": "ef835f88-3293-4cc1-ac11-dd54b17e7031"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Installing ngrok ...                                                                                \r"
          ]
        },
        {
          "ename": "PyngrokNgrokInstallError",
          "evalue": "An error occurred while downloading ngrok from https://bin.equinox.io/c/bNyj1mQVY4c/ngrok-v3-stable-windows-amd64.zip: [Errno 22] Invalid argument: 'C:\\\\Users\\\\nithi\\\\AppData\\\\Local\\\\Temp\\\\ngrok-v3-stable-windows-amd64.zip'",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
            "File \u001b[1;32mc:\\Users\\nithi\\anaconda3\\envs\\College\\Lib\\site-packages\\pyngrok\\installer.py:125\u001b[0m, in \u001b[0;36minstall_ngrok\u001b[1;34m(ngrok_path, ngrok_version, **kwargs)\u001b[0m\n\u001b[0;32m    123\u001b[0m     download_path \u001b[38;5;241m=\u001b[39m _download_file(url, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m--> 125\u001b[0m     \u001b[43m_install_ngrok_zip\u001b[49m\u001b[43m(\u001b[49m\u001b[43mngrok_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdownload_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    126\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
            "File \u001b[1;32mc:\\Users\\nithi\\anaconda3\\envs\\College\\Lib\\site-packages\\pyngrok\\installer.py:140\u001b[0m, in \u001b[0;36m_install_ngrok_zip\u001b[1;34m(ngrok_path, zip_path)\u001b[0m\n\u001b[0;32m    138\u001b[0m _print_progress(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInstalling ngrok ... \u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 140\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mzipfile\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mZipFile\u001b[49m\u001b[43m(\u001b[49m\u001b[43mzip_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m zip_ref:\n\u001b[0;32m    141\u001b[0m     logger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExtracting ngrok binary from \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mzip_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mngrok_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m ...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
            "File \u001b[1;32mc:\\Users\\nithi\\anaconda3\\envs\\College\\Lib\\zipfile\\__init__.py:1331\u001b[0m, in \u001b[0;36mZipFile.__init__\u001b[1;34m(self, file, mode, compression, allowZip64, compresslevel, strict_timestamps, metadata_encoding)\u001b[0m\n\u001b[0;32m   1330\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1331\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfp \u001b[38;5;241m=\u001b[39m \u001b[43mio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilemode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1332\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m:\n",
            "\u001b[1;31mOSError\u001b[0m: [Errno 22] Invalid argument: 'C:\\\\Users\\\\nithi\\\\AppData\\\\Local\\\\Temp\\\\ngrok-v3-stable-windows-amd64.zip'",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[1;31mPyngrokNgrokInstallError\u001b[0m                  Traceback (most recent call last)",
            "Cell \u001b[1;32mIn[5], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m ngrok_tunnel \u001b[38;5;241m=\u001b[39m \u001b[43mngrok\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m8000\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPublic URL:\u001b[39m\u001b[38;5;124m'\u001b[39m, ngrok_tunnel\u001b[38;5;241m.\u001b[39mpublic_url)\n\u001b[0;32m      3\u001b[0m nest_asyncio\u001b[38;5;241m.\u001b[39mapply()\n",
            "File \u001b[1;32mc:\\Users\\nithi\\anaconda3\\envs\\College\\Lib\\site-packages\\pyngrok\\ngrok.py:331\u001b[0m, in \u001b[0;36mconnect\u001b[1;34m(addr, proto, name, pyngrok_config, **options)\u001b[0m\n\u001b[0;32m    327\u001b[0m _upgrade_legacy_params(pyngrok_config, options)\n\u001b[0;32m    329\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOpening tunnel named: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 331\u001b[0m api_url \u001b[38;5;241m=\u001b[39m \u001b[43mget_ngrok_process\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpyngrok_config\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mapi_url\n\u001b[0;32m    333\u001b[0m logger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating tunnel with options: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00moptions\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    335\u001b[0m tunnel \u001b[38;5;241m=\u001b[39m NgrokTunnel(api_request(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mapi_url\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/api/tunnels\u001b[39m\u001b[38;5;124m\"\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPOST\u001b[39m\u001b[38;5;124m\"\u001b[39m, data\u001b[38;5;241m=\u001b[39moptions,\n\u001b[0;32m    336\u001b[0m                                  timeout\u001b[38;5;241m=\u001b[39mpyngrok_config\u001b[38;5;241m.\u001b[39mrequest_timeout),\n\u001b[0;32m    337\u001b[0m                      pyngrok_config, api_url)\n",
            "File \u001b[1;32mc:\\Users\\nithi\\anaconda3\\envs\\College\\Lib\\site-packages\\pyngrok\\ngrok.py:154\u001b[0m, in \u001b[0;36mget_ngrok_process\u001b[1;34m(pyngrok_config)\u001b[0m\n\u001b[0;32m    151\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pyngrok_config \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    152\u001b[0m     pyngrok_config \u001b[38;5;241m=\u001b[39m conf\u001b[38;5;241m.\u001b[39mget_default()\n\u001b[1;32m--> 154\u001b[0m \u001b[43minstall_ngrok\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpyngrok_config\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    156\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m process\u001b[38;5;241m.\u001b[39mget_process(pyngrok_config)\n",
            "File \u001b[1;32mc:\\Users\\nithi\\anaconda3\\envs\\College\\Lib\\site-packages\\pyngrok\\ngrok.py:99\u001b[0m, in \u001b[0;36minstall_ngrok\u001b[1;34m(pyngrok_config)\u001b[0m\n\u001b[0;32m     96\u001b[0m     pyngrok_config \u001b[38;5;241m=\u001b[39m conf\u001b[38;5;241m.\u001b[39mget_default()\n\u001b[0;32m     98\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(pyngrok_config\u001b[38;5;241m.\u001b[39mngrok_path):\n\u001b[1;32m---> 99\u001b[0m     \u001b[43minstaller\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minstall_ngrok\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpyngrok_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mngrok_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mngrok_version\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpyngrok_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mngrok_version\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    101\u001b[0m config_path \u001b[38;5;241m=\u001b[39m conf\u001b[38;5;241m.\u001b[39mget_config_path(pyngrok_config)\n\u001b[0;32m    103\u001b[0m \u001b[38;5;66;03m# Install the config to the requested path\u001b[39;00m\n",
            "File \u001b[1;32mc:\\Users\\nithi\\anaconda3\\envs\\College\\Lib\\site-packages\\pyngrok\\installer.py:127\u001b[0m, in \u001b[0;36minstall_ngrok\u001b[1;34m(ngrok_path, ngrok_version, **kwargs)\u001b[0m\n\u001b[0;32m    125\u001b[0m     _install_ngrok_zip(ngrok_path, download_path)\n\u001b[0;32m    126\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m--> 127\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m PyngrokNgrokInstallError(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAn error occurred while downloading ngrok from \u001b[39m\u001b[38;5;132;01m{\u001b[39;00murl\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
            "\u001b[1;31mPyngrokNgrokInstallError\u001b[0m: An error occurred while downloading ngrok from https://bin.equinox.io/c/bNyj1mQVY4c/ngrok-v3-stable-windows-amd64.zip: [Errno 22] Invalid argument: 'C:\\\\Users\\\\nithi\\\\AppData\\\\Local\\\\Temp\\\\ngrok-v3-stable-windows-amd64.zip'"
          ]
        }
      ],
      "source": [
        "ngrok_tunnel = ngrok.connect(8000)\n",
        "print('Public URL:', ngrok_tunnel.public_url)\n",
        "nest_asyncio.apply()\n",
        "uvicorn.run(app, port=8000)"
      ]
    }
  ],
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