MIS284N_Unstructured_Data_Test_3B.ipynb

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      "toc_visible": true,
      "collapsed_sections": [
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      "authorship_tag": "ABX9TyNrR+DlLtrFeqceD3Gwx+q/",
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      "name": "python3",
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    "language_info": {
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/nikhilnair31/MIS284N-UD-YT-Shorts-Generator/blob/main/MIS284N_Unstructured_Data_Test_3B.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup"
      ],
      "metadata": {
        "id": "fz1jmQFQlGCq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Install and Import"
      ],
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        "id": "V3abnKXazsvN"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
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          "height": 1000
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        "outputId": "663d00a4-e6bf-496c-d3b5-8b291798a73e"
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      "outputs": [
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          "name": "stdout",
          "text": [
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            "  systemd-hwe-hwdb udev\n",
            "Suggested packages:\n",
            "  apparmor-profiles-extra apparmor-utils fuse3 zenity | kdialog\n",
            "The following NEW packages will be installed:\n",
            "  apparmor chromium-browser chromium-chromedriver libfuse3-3 liblzo2-2 snapd\n",
            "  squashfs-tools systemd-hwe-hwdb udev\n",
            "0 upgraded, 9 newly installed, 0 to remove and 29 not upgraded.\n",
            "Need to get 26.3 MB of archives.\n",
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            "Created symlink /etc/systemd/system/sysinit.target.wants/apparmor.service → /lib/systemd/system/apparmor.service.\n",
            "Setting up liblzo2-2:amd64 (2.10-2build3) ...\n",
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            "invoke-rc.d: could not determine current runlevel\n",
            "invoke-rc.d: policy-rc.d denied execution of start.\n",
            "Setting up libfuse3-3:amd64 (3.10.5-1build1) ...\n",
            "Setting up snapd (2.58+22.04.1) ...\n",
            "Created symlink /etc/systemd/system/multi-user.target.wants/snapd.aa-prompt-listener.service → /lib/systemd/system/snapd.aa-prompt-listener.service.\n",
            "Created symlink /etc/systemd/system/multi-user.target.wants/snapd.apparmor.service → /lib/systemd/system/snapd.apparmor.service.\n",
            "Created symlink /etc/systemd/system/multi-user.target.wants/snapd.autoimport.service → /lib/systemd/system/snapd.autoimport.service.\n",
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            "Created symlink /etc/systemd/system/multi-user.target.wants/snapd.recovery-chooser-trigger.service → /lib/systemd/system/snapd.recovery-chooser-trigger.service.\n",
            "Created symlink /etc/systemd/system/multi-user.target.wants/snapd.seeded.service → /lib/systemd/system/snapd.seeded.service.\n",
            "Created symlink /etc/systemd/system/cloud-final.service.wants/snapd.seeded.service → /lib/systemd/system/snapd.seeded.service.\n",
            "Unit /lib/systemd/system/snapd.seeded.service is added as a dependency to a non-existent unit cloud-final.service.\n",
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            "=> Installing the chromium snap\n",
            "==> Checking connectivity with the snap store\n",
            "===> System doesn't have a working snapd, skipping\n",
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            "update-alternatives: using /usr/bin/chromium-browser to provide /usr/bin/x-www-browser (x-www-browser) in auto mode\n",
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            "Installing collected packages: websockets, pydantic, jedi, elevenlabs\n",
            "  Attempting uninstall: pydantic\n",
            "    Found existing installation: pydantic 2.2.1\n",
            "    Uninstalling pydantic-2.2.1:\n",
            "      Successfully uninstalled pydantic-2.2.1\n",
            "Successfully installed elevenlabs-0.2.24 jedi-0.19.0 pydantic-1.10.12 websockets-11.0.3\n",
            "Collecting pydub\n",
            "  Downloading pydub-0.25.1-py2.py3-none-any.whl (32 kB)\n",
            "Installing collected packages: pydub\n",
            "Successfully installed pydub-0.25.1\n",
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            "  Downloading ffmpeg_python-0.2.0-py3-none-any.whl (25 kB)\n",
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            "Installing collected packages: ffmpeg-python\n",
            "Successfully installed ffmpeg-python-0.2.0\n",
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            "Installing collected packages: replicate\n",
            "Successfully installed replicate-0.11.0\n",
            "Requirement already satisfied: google-auth in /usr/local/lib/python3.10/dist-packages (2.17.3)\n",
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            "Collecting google-auth\n",
            "  Downloading google_auth-2.22.0-py2.py3-none-any.whl (181 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m181.8/181.8 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Collecting urllib3<2.0 (from google-auth)\n",
            "  Downloading urllib3-1.26.16-py2.py3-none-any.whl (143 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.1/143.1 kB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Installing collected packages: urllib3, google-auth\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 2.0.4\n",
            "    Uninstalling urllib3-2.0.4:\n",
            "      Successfully uninstalled urllib3-2.0.4\n",
            "  Attempting uninstall: google-auth\n",
            "    Found existing installation: google-auth 2.17.3\n",
            "    Uninstalling google-auth-2.17.3:\n",
            "      Successfully uninstalled google-auth-2.17.3\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires google-auth==2.17.3, but you have google-auth 2.22.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed google-auth-2.22.0 urllib3-1.26.16\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "google",
                  "urllib3"
                ]
              }
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "!pip install praw openai elevenlabs replicate\n",
        "!pip install opencv-python-headless moviepy\n",
        "!pip install google-auth google-auth-oauthlib\n",
        "!pip install --upgrade google-auth"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "import os\n",
        "import sys\n",
        "import json\n",
        "import time\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from datetime import datetime\n",
        "\n",
        "from google.colab import drive\n",
        "from IPython.display import Image, display"
      ],
      "metadata": {
        "id": "hpKbMj-wdscB"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Filter Warnings"
      ],
      "metadata": {
        "id": "vIgNz7Rizg3d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "\n",
        "warnings.filterwarnings(\"ignore\", category=UserWarning, message=\"It appears that you are using PRAW in an asynchronous environment.\")\n",
        "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)"
      ],
      "metadata": {
        "id": "1CoMPzjZzhvc"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Load Drive and Env Vars"
      ],
      "metadata": {
        "id": "bzldGXLLz2oE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Mount Google Drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qWsPTapNd-K4",
        "outputId": "95bf5816-51e5-4d61-9245-d6b3ba91193e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Specify the path to the shell script file\n",
        "script_path = \"/content/drive/MyDrive/Colab Notebooks/UD/env_vars.sh\"\n",
        "\n",
        "# Read the shell script file\n",
        "with open(script_path) as file:\n",
        "    script_content = file.readlines()\n",
        "\n",
        "# Extract environment variables and assign them to os.environ\n",
        "for line in script_content:\n",
        "    if line.startswith(\"export \"):\n",
        "        key, value = line[len(\"export \"):].strip().split(\"=\")\n",
        "        os.environ[key] = value.strip('\"')"
      ],
      "metadata": {
        "id": "BAv8ir2jqcEt"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "os.environ['REDDIT_USER_AGENT']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "PTrjewUEqcID",
        "outputId": "88ecad97-92ec-4df0-e7c1-a920129163bd"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Test UT'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Reddit Shorts Generation"
      ],
      "metadata": {
        "id": "_OIUgwxPwNn5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Reddit Scrape"
      ],
      "metadata": {
        "id": "P-q-WZt0gc6W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import praw"
      ],
      "metadata": {
        "id": "fZ8jtxPwz_Hu"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a Reddit API instance\n",
        "reddit = praw.Reddit(\n",
        "    client_id=os.environ['REDDIT_CLIENT_ID'],\n",
        "    client_secret=os.environ['REDDIT_CLIENT_SECRET'],\n",
        "    user_agent=os.environ['REDDIT_USER_AGENT']\n",
        ")"
      ],
      "metadata": {
        "id": "LIFbkiSY0Ank"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "total_media_to_retrieve = 35\n",
        "results = []\n",
        "params = {'limit': 7}  # Specify the number of items per request\n",
        "\n",
        "while len(results) < total_media_to_retrieve:\n",
        "    remaining_to_retrieve = total_media_to_retrieve - len(results)\n",
        "    current_limit = min(remaining_to_retrieve, params['limit'])\n",
        "\n",
        "    batch = list(reddit.subreddit(\"shortscarystories\").top(limit=current_limit, params=params, time_filter=\"week\"))\n",
        "\n",
        "    if not batch:\n",
        "        break\n",
        "\n",
        "    results.extend(batch)\n",
        "\n",
        "    params = {'limit': params['limit'], 'after': batch[-1].fullname}"
      ],
      "metadata": {
        "collapsed": true,
        "id": "coZrij2Hgfyl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a dictionary to store post data\n",
        "posts_dict = {}\n",
        "\n",
        "# Iterate through the top posts\n",
        "for i, post in enumerate(results):\n",
        "    posts_dict[i] = {\n",
        "        \"Title\": post.title,\n",
        "        \"Content\": post.selftext,\n",
        "        # \"URL\": post.url,\n",
        "        \"Timestamp\": datetime.utcfromtimestamp(post.created_utc).strftime('%Y-%m-%d %H:%M:%S UTC'),\n",
        "        \"Upvotes\": post.ups,\n",
        "        \"Total Comments\": post.num_comments,\n",
        "    }"
      ],
      "metadata": {
        "id": "EcMxBtWWgfym"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a DataFrame from the dictionary\n",
        "test_df = pd.DataFrame(posts_dict).T\n",
        "# Convert \"Timestamp\" column to datetime\n",
        "test_df[\"Timestamp\"] = pd.to_datetime(test_df[\"Timestamp\"])\n",
        "# Check post content length\n",
        "test_df[\"Post Length\"] = test_df['Content'].apply(len)\n",
        "\n",
        "test_df.head()"
      ],
      "metadata": {
        "id": "Vw8c2E-bgfyn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "937806f7-07cd-4721-f195-40d439cebba7"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               Title  \\\n",
              "0  I deserved the divorce. But no one deserves wh...   \n",
              "1                                 How to cheat Death   \n",
              "2  My girlfriend says I’m becoming the man of her...   \n",
              "3  I ran into my old roommate for the first time ...   \n",
              "4                                   “Lucky” Survivor   \n",
              "\n",
              "                                             Content  \\\n",
              "0  Alimony bleeds me dry every paycheck, but that...   \n",
              "1  *Cheating Death is easy— People do it unconsci...   \n",
              "2  The day I bought DreamSync was the first time ...   \n",
              "3  While walking down the street, I ran into my o...   \n",
              "4  I was a “lucky” survivor.\\n\\nIt was a sunny da...   \n",
              "\n",
              "                  Timestamp Upvotes Total Comments  Post Length  \n",
              "0 2023-08-25 18:01:30+00:00    1065             62         2562  \n",
              "1 2023-08-28 03:13:08+00:00    1045             37         2474  \n",
              "2 2023-08-28 15:55:50+00:00     758             32         2685  \n",
              "3 2023-08-23 16:23:17+00:00     745             33         2758  \n",
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          "metadata": {},
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    {
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        "min_test_df = test_df.sort_values(by=['Post Length'], ascending=[True])[:7]\n",
        "min_test_df"
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              "                                               Title  \\\n",
              "33  I'm never telling anyone about my son's \"quirks\"   \n",
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              "23                    My Daughter's Brush With Death   \n",
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              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-26ef98fb-5fcf-444b-a55e-f8d7e9ce2f03 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Seed Content"
      ],
      "metadata": {
        "id": "vGPO46ZAeFF9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess_text(text):\n",
        "    # Remove newlines and extra spaces\n",
        "    text = re.sub(r'\\n', ' ', text)\n",
        "    text = re.sub(r'\\s+', ' ', text).strip()\n",
        "\n",
        "    # Remove special characters and numbers\n",
        "    # text = re.sub(r'[^a-zA-Z\\s]', '', text)\n",
        "\n",
        "    # Remove asterisks (*) and backslashes (\\)\n",
        "    text = re.sub(r'[\\*\\\\]', '', text)\n",
        "\n",
        "    # Remove digits at the beginning of lines\n",
        "    text = re.sub(r'^\\d+\\s+', '', text, flags=re.MULTILINE)\n",
        "\n",
        "    return text\n",
        "\n",
        "pd.set_option('display.max_colwidth', None)\n",
        "seed_content = str(min_test_df['Content'].iloc[1])\n",
        "preprocessed_content = preprocess_text(seed_content)\n",
        "print(f'original text\\n{seed_content}\\n\\nprocessed text\\n{preprocessed_content}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OI2qapjLxTVr",
        "outputId": "6d0c5d92-93e5-4779-ac0e-1224d0c99e36"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "original text\n",
            "*One...*\n",
            "\n",
            "A methed up woman, screaming something about a demon?\n",
            "\n",
            "*Two...*\n",
            "\n",
            "I remember the first time I killed someone. The shock didn't last long, as another man ran at me, screaming incoherently. Never could tell the difference between late stage mental illness or developmental delays...\n",
            "\n",
            "*Three...*\n",
            "\n",
            "Third one in ten minutes? Must be heading into the bad part of town.\n",
            "\n",
            "They call me in to put down...undesirables.\n",
            "\n",
            "*Four...*\n",
            "\n",
            "You know the types. Hard drug users, the severely mentally disabled, the ones with behavior problems, spoiled people who need to learn the word 'no' the hard way, the works.\n",
            "\n",
            "*Five...*\n",
            "\n",
            "Rich fucks don't want to get their hands dirty; politicians need the dregs of society to go away in order to get votes. Not that I care, I see it as a win-win-win. The more shots I fire off, the more I get paid. The more shots that I fire off in a short amount of time, means I'm about to eat good that week.\n",
            "\n",
            "*Six...*\n",
            " \n",
            "I's an easy job, once the disregard for human life sets in. Lucrative as fuck too. My only advice: pack more ammo than you think you'll need.\n",
            "\n",
            "*Seven...*\n",
            "\n",
            "The roughest parts of my job, though...\n",
            "\n",
            "*EIGHT! NINE! TEN!*\n",
            "\n",
            "Are the kids.\n",
            "\n",
            "*chk-CHK*\n",
            "\n",
            "processed text\n",
            "One... A methed up woman, screaming something about a demon? Two... I remember the first time I killed someone. The shock didn't last long, as another man ran at me, screaming incoherently. Never could tell the difference between late stage mental illness or developmental delays... Three... Third one in ten minutes? Must be heading into the bad part of town. They call me in to put down...undesirables. Four... You know the types. Hard drug users, the severely mentally disabled, the ones with behavior problems, spoiled people who need to learn the word 'no' the hard way, the works. Five... Rich fucks don't want to get their hands dirty; politicians need the dregs of society to go away in order to get votes. Not that I care, I see it as a win-win-win. The more shots I fire off, the more I get paid. The more shots that I fire off in a short amount of time, means I'm about to eat good that week. Six... I's an easy job, once the disregard for human life sets in. Lucrative as fuck too. My only advice: pack more ammo than you think you'll need. Seven... The roughest parts of my job, though... EIGHT! NINE! TEN! Are the kids. chk-CHK\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Generating Title and Description"
      ],
      "metadata": {
        "id": "59FwLmWZeNoi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import openai"
      ],
      "metadata": {
        "id": "Bt7LXfuDrLka"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "openai.api_key = os.environ['OPENAI_API_KEY']"
      ],
      "metadata": {
        "id": "Mk1iQvsHevYw"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_title(prompt, script):\n",
        "    response = openai.ChatCompletion.create(\n",
        "        model=\"gpt-3.5-turbo\",\n",
        "        messages=[{\"role\": \"system\", \"content\": prompt},\n",
        "                  {\"role\": \"user\", \"content\": script}],\n",
        "        temperature=1,\n",
        "        max_tokens=256,\n",
        "        top_p=1,\n",
        "        frequency_penalty=0,\n",
        "        presence_penalty=0\n",
        "    )\n",
        "\n",
        "    return response.choices[0].message['content']"
      ],
      "metadata": {
        "id": "qxfN8ekhevgD"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "shorts_title = generate_title(\"Generate a very short title for a YouTube Short based on the script. Make it similar to the examples below. Examples: - Scary things hidden in normal photos Part#28 - This is the scariest video on the internet... - Scary video😳 #shorts\", preprocessed_content)\n",
        "shorts_description = generate_title(\"Generate a very short description for a YouTube Short\", preprocessed_content)\n",
        "\n",
        "shorts_title = shorts_title.replace(\"\\\"\",\"\")\n",
        "shorts_description = shorts_description.replace(\"\\\"\",\"\")\n",
        "\n",
        "print(f'Title: {shorts_title}\\nDescription: {shorts_description}\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HGLJkPV5M95C",
        "outputId": "70011ba7-6225-4286-a517-fdaafc42bee0"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Title: Chilling Encounters in the Night #shorts\n",
            "Description: Intense thriller: A hired professional's chilling encounters in a dangerous job\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "folder_title = shorts_title.replace(\"\\\"\",\"\").split()[0]\n",
        "folder_title"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "vh1VpRxIzqyA",
        "outputId": "3df51598-5d37-4c41-c544-5ddd0a895d52"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Chilling'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Image Prompts"
      ],
      "metadata": {
        "id": "oov31bAReZEZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_image_prompts(gpt_type, prompt, script):\n",
        "    response = openai.ChatCompletion.create(\n",
        "        model=gpt_type,\n",
        "        messages=[{\"role\": \"system\", \"content\": prompt},\n",
        "                  {\"role\": \"user\", \"content\": script}],\n",
        "        temperature=1,\n",
        "        max_tokens=1024,\n",
        "        top_p=1,\n",
        "        frequency_penalty=0,\n",
        "        presence_penalty=0\n",
        "    )\n",
        "\n",
        "    image_prompts_str = response.choices[0].message['content']\n",
        "    image_prompts_str = re.sub(r'[^\\w\\s]', '', image_prompts_str)\n",
        "    image_prompts_str = re.sub(r'[^a-zA-Z\\s]', '', image_prompts_str)\n",
        "    image_prompts = image_prompts_str.lower().split('\\n')\n",
        "    image_prompts = list(map(lambda prompt: f\"{prompt.strip()}\", image_prompts))\n",
        "\n",
        "    return image_prompts"
      ],
      "metadata": {
        "id": "V_lKAVADfOqf"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gpt_type = \"gpt-3.5-turbo\"\n",
        "gpt_prompt = \"\"\"\n",
        "  Generate 6 short one liner prompts for DALLE-2/Midjourney/StableDiffusion prompt image generation. The prompt I give will be the script for a video.\n",
        "  Prompts should be very short but descriptive with no NSFW content.\n",
        "\"\"\"\n",
        "image_prompts = generate_image_prompts(gpt_type, gpt_prompt, preprocessed_content)\n",
        "image_prompts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KLtIVo2Uwc-_",
        "outputId": "e89015a8-a556-4f87-de88-860905a41727"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['a frantic woman desperately cries about a relentless demon',\n",
              " 'memories of my first kill haunt me as another man charges towards me lost in his own world',\n",
              " 'tension rises as i enter a dangerous neighborhood to deal with societal outcasts',\n",
              " 'tasked with eliminating those deemed undesirables including drug users and behaviorally challenged individuals',\n",
              " 'profitdriven politics collides with my morally detached mindset as i take down the unwanted',\n",
              " 'with a numb conscience i find wealth and success in a job that requires ample ammunition',\n",
              " 'in the midst of all the chaos its the innocent children that strike a chord within me',\n",
              " 'eight nine ten the stakes escalate as my targets multiply kids unwittingly caught in the crossfire']"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Image Generation"
      ],
      "metadata": {
        "id": "iIm8bflgeccm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import replicate"
      ],
      "metadata": {
        "id": "JnFI2kaGr3jF"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "client = replicate.Client(api_token=os.environ[\"STABLEDIFFUSION_API_KEY\"])"
      ],
      "metadata": {
        "id": "YXj4SKuvfXoU"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_stablediffusion_image(image_prompt):\n",
        "    output = client.run(\n",
        "        \"stability-ai/stable-diffusion:ac732df83cea7fff18b8472768c88ad041fa750ff7682a21affe81863cbe77e4\",\n",
        "        input = {\n",
        "            \"prompt\": image_prompt,\n",
        "            \"height\": 1024,\n",
        "            \"width\": 576,\n",
        "            \"num_outputs\": 1,\n",
        "            \"num_inference_steps\": 50,\n",
        "            \"guidance_scale\": 7.5,\n",
        "            \"scheduler\": 'DPMSolverMultistep',\n",
        "            \"seed\": 42\n",
        "        }\n",
        "    )\n",
        "    return output[0]"
      ],
      "metadata": {
        "id": "RuS4rV4ZfY2d"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generated_images = [generate_stablediffusion_image(prompt) for prompt in image_prompts]\n",
        "generated_images"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PI7tzs5v2NUr",
        "outputId": "9704705c-4c3f-4e5c-82e9-fc71eb4a2331"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['https://pbxt.replicate.delivery/S77SOYl5n3IcDN6PwfKoMxpUGMX3VLPe0cLHBuPo9PIfRgeFB/out-0.png',\n",
              " 'https://pbxt.replicate.delivery/SDSPcrORtbYfVK7pdDQUe0EMMkqBjeNwsxNHfJ6TPPgMkA9FB/out-0.png',\n",
              " 'https://pbxt.replicate.delivery/IMRvEbTzrmLHGla9vS2jr0GAPPjXISWzLRg2uCz1CzfjEovIA/out-0.png',\n",
              " 'https://pbxt.replicate.delivery/55tfN9XIEPXzDy81KEpqmi1nYlSp1t5kPSHc11dt2vglEovIA/out-0.png',\n",
              " 'https://pbxt.replicate.delivery/xZQAjjWB2W5pCVnStGg179v9jvHOKihNpDjPoClXQc2TC0XE/out-0.png',\n",
              " 'https://pbxt.replicate.delivery/T45DfhH2tet4VkVE6LVfQqVXO5XtnIxzk7TYCkoQk4BmSgeFB/out-0.png',\n",
              " 'https://pbxt.replicate.delivery/F9qfpcfh2wsKKkmsBMcqQKUxvdP6CNef9Ot0hMjvWgqhlA9FB/out-0.png',\n",
              " 'https://pbxt.replicate.delivery/Bztf2VmdXIxJOqj9fqVcD6RW9EXRpSwTYCffeVGRMVDiLB6LC/out-0.png']"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import urllib.request\n",
        "\n",
        "# Set the directory path to save the images\n",
        "directory_path = f\"/content/drive/MyDrive/Colab Notebooks/UD/{folder_title}/img\"\n",
        "\n",
        "# Create the directory if it does not exist\n",
        "if not os.path.exists(directory_path):\n",
        "    os.makedirs(directory_path)\n",
        "\n",
        "# Save the images to the folder\n",
        "for i, image_url in enumerate(generated_images):\n",
        "    file_path = f\"{directory_path}/image_{i+1}.jpg\"\n",
        "    urllib.request.urlretrieve(image_url, file_path)\n",
        "    print(f\"Image {i+1} saved successfully at {file_path}.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b306128-86a9-4651-cfbc-668a2022b223",
        "id": "VfwhTKrLxwVc"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image 1 saved successfully at /content/drive/MyDrive/Colab Notebooks/UD/Chilling/img/image_1.jpg.\n",
            "Image 2 saved successfully at /content/drive/MyDrive/Colab Notebooks/UD/Chilling/img/image_2.jpg.\n",
            "Image 3 saved successfully at /content/drive/MyDrive/Colab Notebooks/UD/Chilling/img/image_3.jpg.\n",
            "Image 4 saved successfully at /content/drive/MyDrive/Colab Notebooks/UD/Chilling/img/image_4.jpg.\n",
            "Image 5 saved successfully at /content/drive/MyDrive/Colab Notebooks/UD/Chilling/img/image_5.jpg.\n",
            "Image 6 saved successfully at /content/drive/MyDrive/Colab Notebooks/UD/Chilling/img/image_6.jpg.\n",
            "Image 7 saved successfully at /content/drive/MyDrive/Colab Notebooks/UD/Chilling/img/image_7.jpg.\n",
            "Image 8 saved successfully at /content/drive/MyDrive/Colab Notebooks/UD/Chilling/img/image_8.jpg.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Audio Generation"
      ],
      "metadata": {
        "id": "gIOLbbV0efjx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from elevenlabs import set_api_key, generate, play"
      ],
      "metadata": {
        "id": "EaKvFviChB4j"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "set_api_key(os.environ[\"ELEVENLABS_API_KEY\"])"
      ],
      "metadata": {
        "id": "cXnkw3-tsWDp"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate audio using 'elevenlabs' package\n",
        "audio = generate(\n",
        "    text=preprocessed_content,\n",
        "    voice=\"Nicole\",\n",
        "    model=\"eleven_monolingual_v1\"\n",
        ")\n",
        "\n",
        "# Set the directory path to save the images\n",
        "directory_path = f\"/content/drive/MyDrive/Colab Notebooks/UD/{folder_title}/aud\"\n",
        "\n",
        "# Create the directory if it does not exist\n",
        "if not os.path.exists(directory_path):\n",
        "    os.makedirs(directory_path)\n",
        "\n",
        "audio_file_path = os.path.join(directory_path, f\"audio_0.wav\")\n",
        "\n",
        "# Save the generated audio to the temporary directory\n",
        "with open(audio_file_path, 'wb') as f:\n",
        "    f.write(audio)"
      ],
      "metadata": {
        "id": "Snmv08Bzx5t-"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Compositing"
      ],
      "metadata": {
        "id": "FlJ3hPsVehWw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from moviepy.editor import ImageClip, concatenate_videoclips\n",
        "from moviepy.editor import AudioFileClip\n",
        "\n",
        "# Paths to images and audio\n",
        "images_folder = f'/content/drive/MyDrive/Colab Notebooks/UD/{folder_title}/img/'\n",
        "audio_file_path = f'/content/drive/MyDrive/Colab Notebooks/UD/{folder_title}/aud/audio_0.wav'\n",
        "\n",
        "# Get image files from the folder. Sort image files by name. Create a list of image paths\n",
        "image_files = [f for f in os.listdir(images_folder) if f.endswith('.jpg')]\n",
        "image_files.sort()\n",
        "image_paths = [os.path.join(images_folder, f) for f in image_files]\n",
        "\n",
        "# Load audio clip\n",
        "audio_clip = AudioFileClip(audio_file_path)\n",
        "\n",
        "# Calculate duration per image\n",
        "duration_per_image = audio_clip.duration / len(image_paths)\n",
        "\n",
        "# Create video clips for each image\n",
        "video_clips = []\n",
        "for image_path in image_paths:\n",
        "    img_clip = ImageClip(image_path, duration=duration_per_image).resize((1080, 1920))\n",
        "    video_clips.append(img_clip)\n",
        "\n",
        "# Concatenate video clips. Set audio for the concatenated video. Set the FPS for the final video\n",
        "concatenated_clips = concatenate_videoclips(video_clips, method=\"compose\")\n",
        "final_clip = concatenated_clips.set_audio(audio_clip)\n",
        "final_clip.fps = 24\n",
        "\n",
        "# Set the directory path to save the images. Create the directory if it does not exist\n",
        "directory_path = f\"/content/drive/MyDrive/Colab Notebooks/UD/{folder_title}/output\"\n",
        "if not os.path.exists(directory_path):\n",
        "    os.makedirs(directory_path)\n",
        "\n",
        "# Export the video\n",
        "formatted_timestamp = datetime.now().strftime('%Y%m%d%H%M%S')\n",
        "output_video_path = directory_path+f'/output_{formatted_timestamp}_reddit.mp4'\n",
        "final_clip.write_videofile(output_video_path, codec='libx264', threads=4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cdd7c6cc-d125-4d7e-826c-f35dfa8319c2",
        "collapsed": true,
        "id": "PIOZR_MOybVm"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Moviepy - Building video /content/drive/MyDrive/Colab Notebooks/UD/Chilling/output/output_20230830142738_reddit.mp4.\n",
            "MoviePy - Writing audio in output_20230830142738_redditTEMP_MPY_wvf_snd.mp3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MoviePy - Done.\n",
            "Moviepy - Writing video /content/drive/MyDrive/Colab Notebooks/UD/Chilling/output/output_20230830142738_reddit.mp4\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Moviepy - Done !\n",
            "Moviepy - video ready /content/drive/MyDrive/Colab Notebooks/UD/Chilling/output/output_20230830142738_reddit.mp4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "from moviepy.editor import VideoFileClip\n",
        "\n",
        "def pipeline(frame, word):\n",
        "    global curr_frame\n",
        "\n",
        "    if curr_frame < frames_per_word and len(word) > 0:\n",
        "        text = word[0]\n",
        "    elif curr_frame >= frames_per_word:\n",
        "        if len(word) > 1:\n",
        "            text = word.pop(0)\n",
        "        else:\n",
        "            text = word[0]\n",
        "\n",
        "        curr_frame = 0\n",
        "\n",
        "    curr_frame += 1\n",
        "\n",
        "    cv2.resize(frame, (1080, 1920))\n",
        "\n",
        "    height, width, _ = frame.shape\n",
        "\n",
        "    (text_width, text_height), _ = cv2.getTextSize(text, cv2.FONT_ITALIC, 4, 50)\n",
        "\n",
        "    text_x = (width - text_width) // 2\n",
        "    text_y = (height + text_height) // 2\n",
        "\n",
        "    cv2.putText(frame, text, (text_x, text_y), cv2.FONT_ITALIC, 4, (0, 0, 0), 50, cv2.LINE_AA)\n",
        "    cv2.putText(frame, text, (text_x, text_y), cv2.FONT_ITALIC, 4, (255, 255, 255), 10, cv2.LINE_AA)\n",
        "\n",
        "    return frame\n",
        "\n",
        "curr_frame = 0\n",
        "words = [word.replace(\",\", \"\").replace(\".\", \"\") for word in preprocessed_content.upper().split()]\n",
        "total_frames = int(cv2.VideoCapture(output_video_path).get(cv2.CAP_PROP_FRAME_COUNT))\n",
        "frames_per_word = int(total_frames / len(words))\n",
        "\n",
        "input_video_path = output_video_path\n",
        "new_output_video_path = directory_path+f\"/output_{formatted_timestamp}_caption_reddit.mp4\"\n",
        "\n",
        "video = VideoFileClip(input_video_path)\n",
        "out_video = video.fl_image(lambda frame: pipeline(frame, words))\n",
        "out_video.write_videofile(new_output_video_path, audio=True, preset='ultrafast')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bwnT_qAtAqaI",
        "outputId": "8547a3e9-9047-4269-dd9c-23e874babeb1"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Moviepy - Building video /content/drive/MyDrive/Colab Notebooks/UD/Chilling/output/output_20230830142738_caption_reddit.mp4.\n",
            "MoviePy - Writing audio in output_20230830142738_caption_redditTEMP_MPY_wvf_snd.mp3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MoviePy - Done.\n",
            "Moviepy - Writing video /content/drive/MyDrive/Colab Notebooks/UD/Chilling/output/output_20230830142738_caption_reddit.mp4\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "t: 100%|█████████▉| 1870/1873 [01:20<00:00, 30.80it/s, now=None]WARNING:py.warnings:/usr/local/lib/python3.10/dist-packages/moviepy/video/io/ffmpeg_reader.py:123: UserWarning: Warning: in file /content/drive/MyDrive/Colab Notebooks/UD/Chilling/output/output_20230830142738_reddit.mp4, 6220800 bytes wanted but 0 bytes read,at frame 1872/1873, at time 78.00/78.03 sec. Using the last valid frame instead.\n",
            "  warnings.warn(\"Warning: in file %s, \"%(self.filename)+\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Moviepy - Done !\n",
            "Moviepy - video ready /content/drive/MyDrive/Colab Notebooks/UD/Chilling/output/output_20230830142738_caption_reddit.mp4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Uploading"
      ],
      "metadata": {
        "id": "vz3k9tFTejV8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import google.auth\n",
        "import google_auth_oauthlib.flow\n",
        "import googleapiclient.discovery\n",
        "import googleapiclient.errors\n",
        "from google.auth.transport.requests import Request\n",
        "from google.oauth2.credentials import Credentials"
      ],
      "metadata": {
        "id": "g-4jxD35iG0W"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load authorized user info from file\n",
        "with open('/content/drive/MyDrive/Colab Notebooks/UD/token.json', 'r') as file:\n",
        "    authorized_user_info = json.load(file)\n",
        "\n",
        "creds = Credentials.from_authorized_user_info(authorized_user_info, ['https://www.googleapis.com/auth/youtube.upload'])\n",
        "creds.refresh(Request())\n",
        "credentials = creds\n",
        "\n",
        "youtube = googleapiclient.discovery.build('youtube', 'v3', credentials=credentials)"
      ],
      "metadata": {
        "id": "FquVmMLzC5OG"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the function to upload the video file\n",
        "def upload_short(video_title, video_description, video_file_path):\n",
        "    request = youtube.videos().insert(\n",
        "        part=\"snippet,status\",\n",
        "        body={\n",
        "            \"snippet\": {\n",
        "                \"title\": video_title,\n",
        "                \"description\": video_description,\n",
        "                \"defaultLanguage\": \"en\",\n",
        "                \"defaultAudioLanguage\": \"en\"\n",
        "            },\n",
        "            \"status\": {\n",
        "                \"privacyStatus\": \"private\",\n",
        "                \"madeForKids\": False,\n",
        "                \"selfDeclaredMadeForKids\": False,\n",
        "                \"embeddable\": True,\n",
        "                \"license\": \"youtube\",\n",
        "                \"publicStatsViewable\": True\n",
        "            },\n",
        "        },\n",
        "        media_body=googleapiclient.http.MediaFileUpload(video_file_path),\n",
        "    )\n",
        "    response = request.execute()\n",
        "    return response"
      ],
      "metadata": {
        "id": "zIFW2nHIiDVx"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Call the upload function with your desired parameters\n",
        "response = upload_short(shorts_title, shorts_description, new_output_video_path)\n",
        "response"
      ],
      "metadata": {
        "id": "PKG41khpiDgZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2211c9cc-d3d0-4a21-96cd-485607ca8c5b"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kind': 'youtube#video',\n",
              " 'etag': 'jZrr0kt2k8zt-V2CJFXXwlXtEa4',\n",
              " 'id': 's4L17sKF5vc',\n",
              " 'snippet': {'publishedAt': '2023-08-30T14:36:36Z',\n",
              "  'channelId': 'UCWdV2EMmhOMenikQpuFdWog',\n",
              "  'title': 'Chilling Encounters in the Night #shorts',\n",
              "  'description': \"Intense thriller: A hired professional's chilling encounters in a dangerous job\",\n",
              "  'thumbnails': {'default': {'url': 'https://i9.ytimg.com/vi/s4L17sKF5vc/default.jpg?sqp=CJSnvacG&rs=AOn4CLC_gOrZQf_U7QSeRUq0KqoJ3Vbk5w',\n",
              "    'width': 120,\n",
              "    'height': 90},\n",
              "   'medium': {'url': 'https://i9.ytimg.com/vi/s4L17sKF5vc/mqdefault.jpg?sqp=CJSnvacG&rs=AOn4CLBGIHfea9BMoc3K3RK9_XNLsNdndA',\n",
              "    'width': 320,\n",
              "    'height': 180},\n",
              "   'high': {'url': 'https://i9.ytimg.com/vi/s4L17sKF5vc/hqdefault.jpg?sqp=CJSnvacG&rs=AOn4CLDrWt10Yes2mCBPTYj11NtklOJdbQ',\n",
              "    'width': 480,\n",
              "    'height': 360}},\n",
              "  'channelTitle': 'Nikhil Nair',\n",
              "  'categoryId': '22',\n",
              "  'liveBroadcastContent': 'none',\n",
              "  'defaultLanguage': 'en',\n",
              "  'localized': {'title': 'Chilling Encounters in the Night #shorts',\n",
              "   'description': \"Intense thriller: A hired professional's chilling encounters in a dangerous job\"},\n",
              "  'defaultAudioLanguage': 'en'},\n",
              " 'status': {'uploadStatus': 'uploaded',\n",
              "  'privacyStatus': 'private',\n",
              "  'license': 'youtube',\n",
              "  'embeddable': True,\n",
              "  'publicStatsViewable': True,\n",
              "  'selfDeclaredMadeForKids': False}}"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    }
  ]
}