Quantum_Phase_Transitions.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyOPmaGd0Wp/dmAdLC1l0+7z",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/graylan0/Quantum-Innovations-Snippets-AIGEN/blob/main/Quantum_Phase_Transitions.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YXS8W6LkvmNU",
        "outputId": "af0e9c3a-68a2-4bb3-afee-241f51b301d7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-12-01 14:31:31--  https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/resolve/main/llama-2-7b-chat.ggmlv3.q8_0.bin\n",
            "Resolving huggingface.co (huggingface.co)... 18.172.134.88, 18.172.134.24, 18.172.134.4, ...\n",
            "Connecting to huggingface.co (huggingface.co)|18.172.134.88|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://cdn-lfs.huggingface.co/repos/30/e3/30e3aca7233f7337633262ff6d59dd98559ecd8982e7419b39752c8d0daae1ca/3bfdde943555c78294626a6ccd40184162d066d39774bd2c98dae24943d32cc3?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27llama-2-7b-chat.ggmlv3.q8_0.bin%3B+filename%3D%22llama-2-7b-chat.ggmlv3.q8_0.bin%22%3B&response-content-type=application%2Foctet-stream&Expires=1701700291&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcwMTcwMDI5MX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy8zMC9lMy8zMGUzYWNhNzIzM2Y3MzM3NjMzMjYyZmY2ZDU5ZGQ5ODU1OWVjZDg5ODJlNzQxOWIzOTc1MmM4ZDBkYWFlMWNhLzNiZmRkZTk0MzU1NWM3ODI5NDYyNmE2Y2NkNDAxODQxNjJkMDY2ZDM5Nzc0YmQyYzk4ZGFlMjQ5NDNkMzJjYzM%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qJnJlc3BvbnNlLWNvbnRlbnQtdHlwZT0qIn1dfQ__&Signature=p1Grc%7ErDhy4%7EHz30WcAzFWSaPGj2qL-4TZhHe9RUcRgvhr3B4J03yS1B4%7EKTiyaYEmpxgVg%7E6Zs%7EydZUllhorO5zm0p4Q8BQ-uS%7Ejcn6OwTR6tlsryrD54B32MSLh2umfVpPuUGJGd-py4iBsRWDXWrQzkBgNaUyyAvaGOHOkNG%7E%7EQqvquoHx3Q400zeKarc4Ad2Hz29FAXuV0srhcCFrrvCtbxdqQKlLGl4DLxjsq2X0wsj3-lWjwW29FqMdtxXai5uRzVg9DhUGw2vQs5ludaRjrVgrILCA2K1UDk7mxbmJDxEg4WiuKB50EkXovEFleklRnyNCe6WeW1mzHkKLA__&Key-Pair-Id=KVTP0A1DKRTAX [following]\n",
            "--2023-12-01 14:31:31--  https://cdn-lfs.huggingface.co/repos/30/e3/30e3aca7233f7337633262ff6d59dd98559ecd8982e7419b39752c8d0daae1ca/3bfdde943555c78294626a6ccd40184162d066d39774bd2c98dae24943d32cc3?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27llama-2-7b-chat.ggmlv3.q8_0.bin%3B+filename%3D%22llama-2-7b-chat.ggmlv3.q8_0.bin%22%3B&response-content-type=application%2Foctet-stream&Expires=1701700291&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcwMTcwMDI5MX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy8zMC9lMy8zMGUzYWNhNzIzM2Y3MzM3NjMzMjYyZmY2ZDU5ZGQ5ODU1OWVjZDg5ODJlNzQxOWIzOTc1MmM4ZDBkYWFlMWNhLzNiZmRkZTk0MzU1NWM3ODI5NDYyNmE2Y2NkNDAxODQxNjJkMDY2ZDM5Nzc0YmQyYzk4ZGFlMjQ5NDNkMzJjYzM%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qJnJlc3BvbnNlLWNvbnRlbnQtdHlwZT0qIn1dfQ__&Signature=p1Grc%7ErDhy4%7EHz30WcAzFWSaPGj2qL-4TZhHe9RUcRgvhr3B4J03yS1B4%7EKTiyaYEmpxgVg%7E6Zs%7EydZUllhorO5zm0p4Q8BQ-uS%7Ejcn6OwTR6tlsryrD54B32MSLh2umfVpPuUGJGd-py4iBsRWDXWrQzkBgNaUyyAvaGOHOkNG%7E%7EQqvquoHx3Q400zeKarc4Ad2Hz29FAXuV0srhcCFrrvCtbxdqQKlLGl4DLxjsq2X0wsj3-lWjwW29FqMdtxXai5uRzVg9DhUGw2vQs5ludaRjrVgrILCA2K1UDk7mxbmJDxEg4WiuKB50EkXovEFleklRnyNCe6WeW1mzHkKLA__&Key-Pair-Id=KVTP0A1DKRTAX\n",
            "Resolving cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)... 108.156.107.62, 108.156.107.69, 108.156.107.116, ...\n",
            "Connecting to cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)|108.156.107.62|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7160799872 (6.7G) [application/octet-stream]\n",
            "Saving to: ‘llama-2-7b-chat.ggmlv3.q8_0.bin’\n",
            "\n",
            "llama-2-7b-chat.ggm 100%[===================>]   6.67G   111MB/s    in 40s     \n",
            "\n",
            "2023-12-01 14:32:10 (173 MB/s) - ‘llama-2-7b-chat.ggmlv3.q8_0.bin’ saved [7160799872/7160799872]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/resolve/main/llama-2-7b-chat.ggmlv3.q8_0.bin"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Build Llama cpp\n",
        "!CMAKE_ARGS=\"-DLLAMA_CUBLAS=on\" FORCE_CMAKE=1 pip install llama-cpp-python==0.1.78"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YNcwL5t_vmnL",
        "outputId": "140dcede-616b-432c-8821-f80984858066"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting llama-cpp-python==0.1.78\n",
            "  Downloading llama_cpp_python-0.1.78.tar.gz (1.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m20.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: typing-extensions>=4.5.0 in /usr/local/lib/python3.10/dist-packages (from llama-cpp-python==0.1.78) (4.5.0)\n",
            "Requirement already satisfied: numpy>=1.20.0 in /usr/local/lib/python3.10/dist-packages (from llama-cpp-python==0.1.78) (1.23.5)\n",
            "Requirement already satisfied: diskcache>=5.6.1 in /usr/local/lib/python3.10/dist-packages (from llama-cpp-python==0.1.78) (5.6.3)\n",
            "Building wheels for collected packages: llama-cpp-python\n",
            "  Building wheel for llama-cpp-python (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for llama-cpp-python: filename=llama_cpp_python-0.1.78-cp310-cp310-linux_x86_64.whl size=5822224 sha256=149e83be7717b27cbd926ca2a178360328ac753b54cf79406b443c4cdae4a320\n",
            "  Stored in directory: /root/.cache/pip/wheels/61/f9/20/9ca660a9d3f2a47e44217059409478865948b5c8a1cba70030\n",
            "Successfully built llama-cpp-python\n",
            "Installing collected packages: llama-cpp-python\n",
            "Successfully installed llama-cpp-python-0.1.78\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install weaviate-client\n",
        "!pip install aiohttp\n",
        "!pip install logging\n",
        "!pip install aiosqlite\n",
        "!pip install pennylane\n",
        "!pip install summa"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "62I0es6yvz7P",
        "outputId": "99d7e6d3-32be-42c8-ed86-f7abc38f722d"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting weaviate-client\n",
            "  Downloading weaviate_client-3.25.3-py3-none-any.whl (120 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/120.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m \u001b[32m112.6/120.3 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m120.3/120.3 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests<3.0.0,>=2.30.0 in /usr/local/lib/python3.10/dist-packages (from weaviate-client) (2.31.0)\n",
            "Collecting validators<1.0.0,>=0.21.2 (from weaviate-client)\n",
            "  Downloading validators-0.22.0-py3-none-any.whl (26 kB)\n",
            "Collecting authlib<2.0.0,>=1.2.1 (from weaviate-client)\n",
            "  Downloading Authlib-1.2.1-py2.py3-none-any.whl (215 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m215.3/215.3 kB\u001b[0m \u001b[31m13.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: cryptography>=3.2 in /usr/local/lib/python3.10/dist-packages (from authlib<2.0.0,>=1.2.1->weaviate-client) (41.0.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.30.0->weaviate-client) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.30.0->weaviate-client) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.30.0->weaviate-client) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.30.0->weaviate-client) (2023.7.22)\n",
            "Requirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.10/dist-packages (from cryptography>=3.2->authlib<2.0.0,>=1.2.1->weaviate-client) (1.16.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.12->cryptography>=3.2->authlib<2.0.0,>=1.2.1->weaviate-client) (2.21)\n",
            "Installing collected packages: validators, authlib, weaviate-client\n",
            "Successfully installed authlib-1.2.1 validators-0.22.0 weaviate-client-3.25.3\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (3.8.6)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp) (23.1.0)\n",
            "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp) (3.3.2)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp) (6.0.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp) (4.0.3)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp) (1.9.2)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp) (1.4.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp) (1.3.1)\n",
            "Requirement already satisfied: idna>=2.0 in /usr/local/lib/python3.10/dist-packages (from yarl<2.0,>=1.0->aiohttp) (3.4)\n",
            "Collecting logging\n",
            "  Downloading logging-0.4.9.6.tar.gz (96 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m96.0/96.0 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
            "  \n",
            "  \u001b[31m×\u001b[0m \u001b[32mpython setup.py egg_info\u001b[0m did not run successfully.\n",
            "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
            "  \u001b[31m╰─>\u001b[0m See above for output.\n",
            "  \n",
            "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25herror\n",
            "\u001b[1;31merror\u001b[0m: \u001b[1mmetadata-generation-failed\u001b[0m\n",
            "\n",
            "\u001b[31m×\u001b[0m Encountered error while generating package metadata.\n",
            "\u001b[31m╰─>\u001b[0m See above for output.\n",
            "\n",
            "\u001b[1;35mnote\u001b[0m: This is an issue with the package mentioned above, not pip.\n",
            "\u001b[1;36mhint\u001b[0m: See above for details.\n",
            "Collecting aiosqlite\n",
            "  Downloading aiosqlite-0.19.0-py3-none-any.whl (15 kB)\n",
            "Installing collected packages: aiosqlite\n",
            "Successfully installed aiosqlite-0.19.0\n",
            "Collecting pennylane\n",
            "  Downloading PennyLane-0.33.1-py3-none-any.whl (1.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.5/1.5 MB\u001b[0m \u001b[31m18.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pennylane) (1.23.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from pennylane) (1.11.3)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from pennylane) (3.2.1)\n",
            "Collecting rustworkx (from pennylane)\n",
            "  Downloading rustworkx-0.13.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m82.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: autograd in /usr/local/lib/python3.10/dist-packages (from pennylane) (1.6.2)\n",
            "Requirement already satisfied: toml in /usr/local/lib/python3.10/dist-packages (from pennylane) (0.10.2)\n",
            "Requirement already satisfied: appdirs in /usr/local/lib/python3.10/dist-packages (from pennylane) (1.4.4)\n",
            "Collecting semantic-version>=2.7 (from pennylane)\n",
            "  Downloading semantic_version-2.10.0-py2.py3-none-any.whl (15 kB)\n",
            "Collecting autoray>=0.6.1 (from pennylane)\n",
            "  Downloading autoray-0.6.7-py3-none-any.whl (49 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.9/49.9 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: cachetools in /usr/local/lib/python3.10/dist-packages (from pennylane) (5.3.2)\n",
            "Collecting pennylane-lightning>=0.33 (from pennylane)\n",
            "  Downloading PennyLane_Lightning-0.33.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (14.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.0/14.0 MB\u001b[0m \u001b[31m95.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from pennylane) (2.31.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from pennylane) (4.5.0)\n",
            "Requirement already satisfied: future>=0.15.2 in /usr/local/lib/python3.10/dist-packages (from autograd->pennylane) (0.18.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->pennylane) (3.3.2)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->pennylane) (2023.7.22)\n",
            "Installing collected packages: semantic-version, rustworkx, autoray, pennylane-lightning, pennylane\n",
            "Successfully installed autoray-0.6.7 pennylane-0.33.1 pennylane-lightning-0.33.1 rustworkx-0.13.2 semantic-version-2.10.0\n",
            "Collecting summa\n",
            "  Downloading summa-1.2.0.tar.gz (54 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.9/54.9 kB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: scipy>=0.19 in /usr/local/lib/python3.10/dist-packages (from summa) (1.11.3)\n",
            "Requirement already satisfied: numpy<1.28.0,>=1.21.6 in /usr/local/lib/python3.10/dist-packages (from scipy>=0.19->summa) (1.23.5)\n",
            "Building wheels for collected packages: summa\n",
            "  Building wheel for summa (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for summa: filename=summa-1.2.0-py3-none-any.whl size=54388 sha256=4db544d8e395fea7fed2fae8fb7bafdd725f883ece05ead483f8b29203adc7ef\n",
            "  Stored in directory: /root/.cache/pip/wheels/4a/ca/c5/4958614cfba88ed6ceb7cb5a849f9f89f9ac49971616bc919f\n",
            "Successfully built summa\n",
            "Installing collected packages: summa\n",
            "Successfully installed summa-1.2.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "import torch\n",
        "import asyncio\n",
        "import logging\n",
        "import aiosqlite\n",
        "import random\n",
        "import numpy as np\n",
        "import pennylane as qml\n",
        "from summa import summarizer\n",
        "from llama_cpp import Llama\n",
        "import weaviate\n",
        "import re\n",
        "import aiohttp\n",
        "from concurrent.futures import ThreadPoolExecutor\n",
        "\n",
        "dev = qml.device('default.qubit', wires=20)\n",
        "\n",
        "@qml.qnode(dev)\n",
        "def quantum_circuit(amplitudes, color_codes):\n",
        "    for i, amp in enumerate(amplitudes):\n",
        "        qml.RY(amp * np.pi, wires=i)\n",
        "    for i, code in enumerate(color_codes):\n",
        "        qml.RZ(code * np.pi, wires=i + 10)\n",
        "    for i in range(19):\n",
        "        qml.CNOT(wires=[i, i + 1])\n",
        "    return qml.state()\n",
        "\n",
        "logging.basicConfig(level=logging.DEBUG)\n",
        "logger = logging.getLogger(__name__)\n",
        "\n",
        "llm = Llama(\n",
        "    model_path=\"llama-2-7b-chat.ggmlv3.q8_0.bin\",\n",
        "    n_gpu_layers=-1,\n",
        "    n_ctx=3900,\n",
        ")\n",
        "\n",
        "executor = ThreadPoolExecutor(max_workers=3)\n",
        "\n",
        "client = weaviate.Client(\n",
        "    url=\"https://upward-enough-rabbit.ngrok-free.app/\",\n",
        ")\n",
        "\n",
        "async def retrieve_movie_frames_by_theme(theme):\n",
        "    query = {\n",
        "        \"query\": {\n",
        "            \"nearText\": {\n",
        "                \"concepts\": [theme],\n",
        "                \"certainty\": 0.7\n",
        "            }\n",
        "        }\n",
        "    }\n",
        "    result = await client.query.get(\"MovieFrame\", [\"frame_text\", \"summary\"]).with_near_text(query).do()\n",
        "    return result['data']['Get']['MovieFrame'] if 'data' in result and 'Get' in result['data'] and 'MovieFrame' in result['data']['Get'] else []\n",
        "\n",
        "async def initialize_db():\n",
        "    async with aiosqlite.connect(\"movie_frames.db\") as db:\n",
        "        await db.execute(\"CREATE TABLE IF NOT EXISTS POOLDATA (frame_num INTEGER PRIMARY KEY, frame_text TEXT, summary TEXT);\")\n",
        "        await db.commit()\n",
        "\n",
        "async def insert_into_db(frame_num, frame_text, summary):\n",
        "    async with aiosqlite.connect(\"movie_frames.db\") as db:\n",
        "        await db.execute(\"INSERT INTO POOLDATA (frame_num, frame_text, summary) VALUES (?, ?, ?)\", (frame_num, frame_text, summary))\n",
        "        await db.commit()\n",
        "\n",
        "async def generate_html_color_code(text):\n",
        "    attempts = 0\n",
        "    color_code = None\n",
        "\n",
        "    while not color_code and attempts < 10:  # Limit the number of attempts to avoid infinite loops\n",
        "        try:\n",
        "            prompt = f\"\"\"\n",
        "            Based on the sentiment of the following text, suggest an HTML color code in hex format (e.g., #FF5733 for excitement, #3498DB for calmness).\n",
        "            Text: '{text}'\n",
        "            Suggested Color Code:\"\"\"\n",
        "\n",
        "            response = llm(prompt, max_tokens=10)\n",
        "            if response and response['choices']:\n",
        "                color_code_match = re.search(r'#[0-9A-Fa-f]{6}', response['choices'][0]['text'])\n",
        "                if color_code_match:\n",
        "                    color_code = color_code_match.group()\n",
        "        except Exception as e:\n",
        "            logger.error(f\"Error in generating color code: {e}\")\n",
        "\n",
        "        attempts += 1\n",
        "\n",
        "    if not color_code:\n",
        "        # Log that a random color is being used\n",
        "        logger.warning(\"Failed to generate color code. Using a random color.\")\n",
        "        # Generate a random hex color\n",
        "        color_code = \"#{:06x}\".format(random.randint(0, 0xFFFFFF))\n",
        "\n",
        "    return color_code\n",
        "\n",
        "\n",
        "\n",
        "async def vectorized_sentiment_analysis(text):\n",
        "    url = \" https://upward-enough-rabbit.ngrok-free.app/v1/modules/text2vec-contextionary/vectorize\"\n",
        "    headers = {\"Content-Type\": \"application/json\"}\n",
        "    data = {\"text\": text}\n",
        "\n",
        "    async with aiohttp.ClientSession() as session:\n",
        "        async with session.post(url, headers=headers, json=data) as response:\n",
        "            if response.status == 200:\n",
        "                return await response.json()\n",
        "            else:\n",
        "                # Handle error or return a default value\n",
        "                return None\n",
        "\n",
        "async def generate_and_summarize_frame(last_frame, frame_num, frames, theme, past_summaries):\n",
        "    sentiment_vector_tasks = [vectorized_sentiment_analysis(summary) for summary in past_summaries]\n",
        "    sentiment_vectors = await asyncio.gather(*sentiment_vector_tasks)\n",
        "\n",
        "    # Replace None with a default value, e.g., a zero vector\n",
        "    sentiment_vectors = [vec if vec is not None else {\"vector\": [0.0] * 300} for vec in sentiment_vectors]\n",
        "\n",
        "    # Extract the actual vectors from the results\n",
        "    sentiment_vectors = [vec[\"vector\"] for vec in sentiment_vectors]\n",
        "\n",
        "    # Asynchronously gather color codes\n",
        "    color_code_tasks = [generate_html_color_code(summary) for summary in past_summaries]\n",
        "    color_codes = await asyncio.gather(*color_code_tasks)\n",
        "    color_codes = [int(code.lstrip('#'), 16) for code in color_codes]  # Strip '#' and convert to integers\n",
        "\n",
        "    quantum_state = quantum_circuit([np.linalg.norm(vec) for vec in sentiment_vectors], color_codes)\n",
        "    quantum_influence = np.mean(quantum_state)\n",
        "    continuation_prompt = f\"{last_frame} Quantum influence: {quantum_influence}\"\n",
        "    continuation_response = llm(continuation_prompt, max_tokens=200)\n",
        "    new_frame = continuation_response['choices'][0]['text'] if continuation_response['choices'] else None\n",
        "    if new_frame:\n",
        "        summary = summarizer.summarize(new_frame, words=50)\n",
        "        await insert_into_db(frame_num, new_frame, summary)\n",
        "        return new_frame\n",
        "    return None\n",
        "\n",
        "\n",
        "async def start_movie(topic):\n",
        "    await initialize_db()\n",
        "    classes = client.schema.get()\n",
        "    class_names = [cls['class'] for cls in classes['classes']] if 'classes' in classes else []\n",
        "    if \"MovieFrame\" not in class_names:\n",
        "        client.schema.create_class({\"class\": \"MovieFrame\", \"properties\": [{\"name\": \"frame_text\", \"dataType\": [\"text\"], \"vectorizer\": \"text2vec-contextionary\"}, {\"name\": \"summary\", \"dataType\": [\"text\"], \"vectorizer\": \"text2vec-contextionary\"}], \"vectorIndexType\": \"hnsw\", \"vectorizer\": \"text2vec-contextionary\"})\n",
        "    initial_prompt_response = llm(f\"Write a book about {topic}\", max_tokens=700)\n",
        "    initial_prompt = initial_prompt_response['choices'][0]['text'] if initial_prompt_response['choices'] else \"\"\n",
        "    frames = {\"0\": initial_prompt}\n",
        "    last_frame = initial_prompt\n",
        "    for i in range(1, 10000):\n",
        "        frame_num = i * 10\n",
        "        past_summaries = list(frames.values())[-10:]\n",
        "        last_frame = await generate_and_summarize_frame(last_frame, frame_num, frames, topic, past_summaries) or last_frame\n",
        "    sanitized_topic = ''.join(e for e in topic if e.isalnum())[:50]\n",
        "    with open(f\"{sanitized_topic}_movie_frames.json\", \"w\") as f:\n",
        "        json.dump(frames, f, indent=4)\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    import nest_asyncio\n",
        "    nest_asyncio.apply()\n",
        "    loop = asyncio.get_event_loop()\n",
        "    loop.run_until_complete(start_movie(\"Language Models for Creative Work\"))\n",
        "    del llm\n",
        "    torch.cuda.empty_cache()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "HhfYMgrTvmsP",
        "outputId": "ccbb28b0-eece-4f61-af0f-83a249167e21"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "AVX = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | VSX = 0 | \n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n",
            "Llama.generate: prefix-match hit\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-07d306a60775>\u001b[0m in \u001b[0;36m<cell line: 154>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    156\u001b[0m     \u001b[0mnest_asyncio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    157\u001b[0m     \u001b[0mloop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0masyncio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_event_loop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 158\u001b[0;31m     \u001b[0mloop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_until_complete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart_movie\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Language Models for Creative Work\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    159\u001b[0m     \u001b[0;32mdel\u001b[0m \u001b[0mllm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    160\u001b[0m     \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/nest_asyncio.py\u001b[0m in \u001b[0;36mrun_until_complete\u001b[0;34m(self, future)\u001b[0m\n\u001b[1;32m     91\u001b[0m                 \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_log_destroy_pending\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 93\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     94\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stopping\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m                     \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/nest_asyncio.py\u001b[0m in \u001b[0;36m_run_once\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    127\u001b[0m             \u001b[0mhandle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mready\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpopleft\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cancelled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 129\u001b[0;31m                 \u001b[0mhandle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    130\u001b[0m         \u001b[0mhandle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/asyncio/events.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     78\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_run\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callback\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     81\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSystemExit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m             \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/asyncio/tasks.py\u001b[0m in \u001b[0;36m__wakeup\u001b[0;34m(self, future)\u001b[0m\n\u001b[1;32m    313\u001b[0m             \u001b[0;31m# instead of `__next__()`, which is slower for futures\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    314\u001b[0m             \u001b[0;31m# that return non-generator iterators from their `__iter__`.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 315\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    316\u001b[0m         \u001b[0mself\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m  \u001b[0;31m# Needed to break cycles when an exception occurs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    317\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/nest_asyncio.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(task, exc)\u001b[0m\n\u001b[1;32m    203\u001b[0m         \u001b[0mcurr_task\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcurr_tasks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_loop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    204\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 205\u001b[0;31m             \u001b[0mstep_orig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    206\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcurr_task\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/asyncio/tasks.py\u001b[0m in \u001b[0;36m__step\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m    230\u001b[0m                 \u001b[0;31m# We use the `send` method directly, because coroutines\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m                 \u001b[0;31m# don't have `__iter__` and `__next__` methods.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 232\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcoro\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    233\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcoro\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-1-07d306a60775>\u001b[0m in \u001b[0;36mstart_movie\u001b[0;34m(topic)\u001b[0m\n\u001b[1;32m    147\u001b[0m         \u001b[0mframe_num\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0mpast_summaries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 149\u001b[0;31m         \u001b[0mlast_frame\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mawait\u001b[0m \u001b[0mgenerate_and_summarize_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlast_frame\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframe_num\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtopic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpast_summaries\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mlast_frame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    150\u001b[0m     \u001b[0msanitized_topic\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0me\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtopic\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misalnum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{sanitized_topic}_movie_frames.json\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"w\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-1-07d306a60775>\u001b[0m in \u001b[0;36mgenerate_and_summarize_frame\u001b[0;34m(last_frame, frame_num, frames, theme, past_summaries)\u001b[0m\n\u001b[1;32m    125\u001b[0m     \u001b[0mquantum_influence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquantum_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m     \u001b[0mcontinuation_prompt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"{last_frame} Quantum influence: {quantum_influence}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 127\u001b[0;31m     \u001b[0mcontinuation_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mllm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontinuation_prompt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_tokens\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    128\u001b[0m     \u001b[0mnew_frame\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcontinuation_response\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'choices'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'text'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcontinuation_response\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'choices'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnew_frame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/llama_cpp/llama.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, prompt, suffix, max_tokens, temperature, top_p, logprobs, echo, stop, frequency_penalty, presence_penalty, repeat_penalty, top_k, stream, tfs_z, mirostat_mode, mirostat_tau, mirostat_eta, model, stopping_criteria, logits_processor, grammar)\u001b[0m\n\u001b[1;32m   1400\u001b[0m             \u001b[0mResponse\u001b[0m \u001b[0mobject\u001b[0m \u001b[0mcontaining\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mgenerated\u001b[0m \u001b[0mtext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1401\u001b[0m         \"\"\"\n\u001b[0;32m-> 1402\u001b[0;31m         return self.create_completion(\n\u001b[0m\u001b[1;32m   1403\u001b[0m             \u001b[0mprompt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprompt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1404\u001b[0m             \u001b[0msuffix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msuffix\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/llama_cpp/llama.py\u001b[0m in \u001b[0;36mcreate_completion\u001b[0;34m(self, prompt, suffix, max_tokens, temperature, top_p, logprobs, echo, stop, frequency_penalty, presence_penalty, repeat_penalty, top_k, stream, tfs_z, mirostat_mode, mirostat_tau, mirostat_eta, model, stopping_criteria, logits_processor, grammar)\u001b[0m\n\u001b[1;32m   1351\u001b[0m             \u001b[0mchunks\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIterator\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mCompletionChunk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompletion_or_chunks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1352\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mchunks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1353\u001b[0;31m         \u001b[0mcompletion\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mCompletion\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcompletion_or_chunks\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1354\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcompletion\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/llama_cpp/llama.py\u001b[0m in \u001b[0;36m_create_completion\u001b[0;34m(self, prompt, suffix, max_tokens, temperature, top_p, logprobs, echo, stop, frequency_penalty, presence_penalty, repeat_penalty, top_k, stream, tfs_z, mirostat_mode, mirostat_tau, mirostat_eta, model, stopping_criteria, logits_processor, grammar)\u001b[0m\n\u001b[1;32m    942\u001b[0m         \u001b[0mfinish_reason\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"length\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    943\u001b[0m         \u001b[0mmultibyte_fix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 944\u001b[0;31m         for token in self.generate(\n\u001b[0m\u001b[1;32m    945\u001b[0m             \u001b[0mprompt_tokens\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    946\u001b[0m             \u001b[0mtop_k\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtop_k\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/llama_cpp/llama.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, tokens, top_k, top_p, temp, repeat_penalty, reset, frequency_penalty, presence_penalty, tfs_z, mirostat_mode, mirostat_tau, mirostat_eta, logits_processor, stopping_criteria, grammar)\u001b[0m\n\u001b[1;32m    762\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    763\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 764\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokens\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    765\u001b[0m             token = self.sample(\n\u001b[1;32m    766\u001b[0m                 \u001b[0mtop_k\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtop_k\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/llama_cpp/llama.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, tokens)\u001b[0m\n\u001b[1;32m    502\u001b[0m             )[:] = llama_cpp.llama_get_logits(self.ctx)[: rows * cols]\n\u001b[1;32m    503\u001b[0m             \u001b[0;31m# Update n_tokens\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 504\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_tokens\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mn_tokens\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m     def _sample(\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    }
  ]
}