llama3.2.ipynb

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        "id": "w6-LkedPSVUu"
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "^C\n"
          ]
        }
      ],
      "source": [
        "!pip install langchain bitsandbytes accelerate\n",
        "!pip install -q -U faiss-cpu tiktoken sentence-transformers\n",
        "!pip install langchain-google-genai"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mr--kkpfYc_F"
      },
      "outputs": [],
      "source": [
        "!pip install FastAPI pydantic uvicorn pyngrok"
      ]
    },
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        {
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            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "pG-_jHZHVRnf"
      },
      "outputs": [],
      "source": [
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain.embeddings import CacheBackedEmbeddings, HuggingFaceEmbeddings\n",
        "from langchain.vectorstores import FAISS\n",
        "from langchain.llms import HuggingFacePipeline\n",
        "from langchain.storage import LocalFileStore\n",
        "from langchain.chains import RetrievalQA\n",
        "from langchain.callbacks import StdOutCallbackHandler\n",
        "import torch\n",
        "import transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "with open(\"general.txt\", encoding=\"utf8\") as f:\n",
        "    raw_text = f.read()\n"
      ]
    },
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      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\nithi\\AppData\\Local\\Temp\\ipykernel_6472\\656122397.py:10: LangChainDeprecationWarning: The class `HuggingFaceEmbeddings` was deprecated in LangChain 0.2.2 and will be removed in 1.0. An updated version of the class exists in the :class:`~langchain-huggingface package and should be used instead. To use it run `pip install -U :class:`~langchain-huggingface` and import as `from :class:`~langchain_huggingface import HuggingFaceEmbeddings``.\n",
            "  core_embeddings_model = HuggingFaceEmbeddings(model_name=embed_model_id)\n"
          ]
        }
      ],
      "source": [
        "\n",
        "text_splitter = RecursiveCharacterTextSplitter(\n",
        "    chunk_size = 1000, # the character length of the chunk\n",
        "    chunk_overlap = 100, # the character length of the overlap between chunks\n",
        "    length_function = len, # the length function - in this case, character length (aka the python len() fn.)\n",
        ")\n",
        "combined_documents = text_splitter.split_text(raw_text)\n",
        "\n",
        "store = LocalFileStore(\"./cache/\")\n",
        "embed_model_id = 'sentence-transformers/all-MiniLM-L6-v2'\n",
        "core_embeddings_model = HuggingFaceEmbeddings(model_name=embed_model_id)\n",
        "embedder = CacheBackedEmbeddings.from_bytes_store(\n",
        "    core_embeddings_model, store, namespace=embed_model_id)\n",
        "vector_store = FAISS.from_texts(combined_documents, embedder)"
      ]
    },
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      "outputs": [
        {
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          "text": [
            "`low_cpu_mem_usage` was None, now default to True since model is quantized.\n"
          ]
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              "model-00003-of-00005.safetensors:  92%|#########1| 4.51G/4.92G [00:00<?, ?B/s]"
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2c2b40f3827943d6b587bb1f5c6a1888",
              "version_major": 2,
              "version_minor": 0
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c6e0e6c3137046db8470e275102164d3",
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c4e3951baf1b41ef96760669fe2e4349",
              "version_major": 2,
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dde4e5d3fee84fe48c726ded68aa712b",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Device set to use cuda:0\n",
            "C:\\Users\\nithi\\AppData\\Local\\Temp\\ipykernel_6472\\949642081.py:31: LangChainDeprecationWarning: The class `HuggingFacePipeline` was deprecated in LangChain 0.0.37 and will be removed in 1.0. An updated version of the class exists in the :class:`~langchain-huggingface package and should be used instead. To use it run `pip install -U :class:`~langchain-huggingface` and import as `from :class:`~langchain_huggingface import HuggingFacePipeline``.\n",
            "  llm = HuggingFacePipeline(pipeline=generate_text)\n"
          ]
        }
      ],
      "source": [
        "model_id = \"meta-llama/Llama-3.2-11B-Vision-Instruct\"\n",
        "\n",
        "bnb_config = transformers.BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type='nf4',\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_compute_dtype=torch.bfloat16\n",
        ")\n",
        "\n",
        "model_config = transformers.AutoConfig.from_pretrained(model_id)\n",
        "\n",
        "model = transformers.AutoModelForCausalLM.from_pretrained(\n",
        "    model_id,\n",
        "    trust_remote_code=True,\n",
        "    config=model_config,\n",
        "    quantization_config=bnb_config,\n",
        "    cache_dir='models'\n",
        ")\n",
        "\n",
        "model.eval()\n",
        "\n",
        "tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)\n",
        "generate_text = transformers.pipeline(\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    task=\"text-generation\",\n",
        "    return_full_text=True,\n",
        "    temperature=0.7,\n",
        "    max_new_tokens=256\n",
        ")\n",
        "\n",
        "llm = HuggingFacePipeline(pipeline=generate_text)\n",
        "retriever = vector_store.as_retriever()\n",
        "\n",
        "handler = StdOutCallbackHandler()\n",
        "\n",
        "qa_with_sources_chain = RetrievalQA.from_chain_type(\n",
        "      llm=llm,\n",
        "      retriever=retriever,\n",
        "      callbacks=[handler],\n",
        "      return_source_documents=True\n",
        "  )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SKN6HfhMWYOy"
      },
      "outputs": [],
      "source": [
        "def generate_response(question):\n",
        "  return qa_with_sources_chain({\"query\" : question})['result']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "3npp2iNHTzQl",
        "outputId": "c8d3a397-fce1-439a-a5cc-8aa8f75903b3"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-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"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "' The Coimbatore campus started in 1994.'"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "generate_response(\"When did the Coimbatore campus start ?\")"
      ]
    },
    {
      "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",
          "text": [
            "Downloading ngrok ...\n",
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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",
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        }
      ],
      "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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