test.ipynb

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      "cell_type": "code",
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "zyFH3otTch2P",
        "outputId": "bd7f2d03-0485-44a2-8aa3-acaa650b03cf"
      },
      "outputs": [],
      "source": [
        "# story generation\n",
        "!pip install langchain \n",
        "!pip install guardrails-ai \n",
        "!pip install openai \n",
        "!pip install python-dotenv \n",
        "!pip install diffusechain\n",
        "#audio generatin\n",
        "!pip install elevenlabs \n",
        "!pip install pyttsx3\n",
        "\n",
        "#video generation\n",
        "!pip3 install moviepy \n",
        "!pip3 install ffmpeg \n",
        "!pip3 install ffmpeg-python\n",
        "\n",
        "!pip freeze > requirements.txt "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5gU2aaGzrZE-",
        "outputId": "feb358f3-1497-48c8-fe08-24deea1482d6"
      },
      "outputs": [],
      "source": [
        "import getpass\n",
        "\n",
        "try:\n",
        "    print(\"OPEN AI API KEY\")\n",
        "    p = getpass.getpass()\n",
        "except Exception as error:\n",
        "    print('ERROR', error)\n",
        "else:\n",
        "    OPENAI_API_KEY = p\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uBIY0qVu8zC0",
        "outputId": "4db3d91a-8764-4d17-ef28-ad16110fef7d"
      },
      "outputs": [],
      "source": [
        "try:\n",
        "    print(\"ELEVEN LABS API KEY\")\n",
        "    e = getpass.getpass()\n",
        "except Exception as error:\n",
        "    print('ERROR', error)\n",
        "else:\n",
        "    ELEVENLABS_API = e"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CdYOMNnlUtfK"
      },
      "outputs": [],
      "source": [
        "body={\n",
        "    \"plot\":\"boy going to the moon\",\n",
        "    \"tone\":\"\",\n",
        "    \"duration\":.5\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LoPPWWeWTXPe"
      },
      "source": [
        "### Story Generation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XCnflLO5EqwF"
      },
      "outputs": [],
      "source": [
        "from langchain.chat_models import ChatOpenAI\n",
        "from langchain.schema import HumanMessage, SystemMessage\n",
        "import os\n",
        "from dotenv import load_dotenv\n",
        "load_dotenv()\n",
        "import datetime\n",
        "\n",
        "import json\n",
        "import requests\n",
        "import io\n",
        "import base64\n",
        "from PIL import Image, PngImagePlugin\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jETvWGpZE7QO"
      },
      "outputs": [],
      "source": [
        "# story customization\n",
        "# describe the story you want to tell\n",
        "plot = body[\"plot\"]\n",
        "\n",
        "# describe the tone of the story\n",
        "tone = body[\"tone\"]\n",
        "\n",
        "# how long do you want the story to be?\n",
        "duration_in_mins = body[\"duration\"]\n",
        "number_of_words = duration_in_mins * 150\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D_8JOpSgE8t3",
        "outputId": "ccf4358c-50f5-48f8-8924-fe251c933494"
      },
      "outputs": [],
      "source": [
        "chat = ChatOpenAI(temperature=0.9 , model=\"gpt-3.5-turbo-16k\", client=any , openai_api_key=OPENAI_API_KEY)\n",
        "\n",
        "messages = [\n",
        "    SystemMessage(\n",
        "        content=\"In a world of boundless creativity, you are an AI storyteller who weaves intriguing tales with depth. Join us on a journey where words come alive, characters thrive, and stories leave an everlasting impact. Let's create something magical together.\",\n",
        "    ),\n",
        "    HumanMessage(\n",
        "        content=\n",
        "        f\"\"\" Write a story with the following plot : {plot}\n",
        "            with the following tone : {tone}\n",
        "            The story should be {number_of_words} words long.\n",
        "            Instead of pronouns, use the names of the characters.\n",
        "        \"\"\",\n",
        "    ),\n",
        "]\n",
        "story = chat(messages).content\n",
        "lenght_of_ai_story = len(story.split(\" \"))\n",
        "with open(\"story.txt\", \"w\") as file:\n",
        "  file.write(story)\n",
        "\n",
        "print(f\"AI story is {lenght_of_ai_story} words long\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NCxVLCZnE-Lf",
        "outputId": "ff5063f8-9018-4a64-81b7-d4e2bc170c4e"
      },
      "outputs": [],
      "source": [
        "def convert_to_list(string):\n",
        "    words = string.split()  # Split the string into a list of words\n",
        "    result = [' '.join(words[i:i+15]) for i in range(0, len(words), 15)]  # Split the list into strings of six words each\n",
        "    return result\n",
        "# Example usage\n",
        "\n",
        "word_lists = convert_to_list(story)\n",
        "print(word_lists)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Character Extraction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "from langchain.output_parsers import GuardrailsOutputParser\n",
        "\n",
        "from langchain.prompts import PromptTemplate\n",
        "from langchain.llms import OpenAI\n",
        "\n",
        "character_rail = \"\"\"\n",
        "<rail version=\"0.1\">\n",
        "\n",
        "<output>\n",
        "    <list name=\"characters\" description=\"Generate a list of characters\">\n",
        "        <object>\n",
        "            <string name=\"character_name\" description=\"The characters name\"/>\n",
        "            <string name=\"character_description\" description=\"character description in words\"/>\n",
        "            <string name=\"character_prompts\" description=\"describe how the character looks physically in words the word should be a physical trait\" format=\"word-list:1\"/>\n",
        "        </object>\n",
        "    </list>\n",
        "</output>\n",
        "\n",
        "\n",
        "<prompt>\n",
        "\n",
        "given the following story, please extract a list of characters and generate their description\n",
        "\n",
        "{{story}}\n",
        "\n",
        "@complete_json_suffix_v2\n",
        "</prompt>\n",
        "</rail>\n",
        "\"\"\"\n",
        "\n",
        "output_parser = GuardrailsOutputParser.from_rail_string(character_rail)\n",
        "\n",
        "prompt = PromptTemplate(\n",
        "    template=output_parser.guard.base_prompt,\n",
        "    input_variables=output_parser.guard.prompt.variable_names,\n",
        ")\n",
        "\n",
        "model = OpenAI(temperature=0.9 , openai_api_key=OPENAI_API_KEY) # type: ignore\n",
        "# model = OpenAI(temperature=0)\n",
        "print(story)\n",
        "\n",
        "characters = model(prompt.format_prompt(story=story).to_string())\n",
        "characters = output_parser.parse(characters)\n",
        "print(characters)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LELzYoCBVQbx",
        "outputId": "980dc7cb-55e6-4ba1-c32f-e5ec688cdb18"
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      "source": [
        "chat = ChatOpenAI(temperature=0.9 , model=\"gpt-3.5-turbo-16k\", client=any , openai_api_key=OPENAI_API_KEY)\n",
        "\n",
        "image_description_list = []\n",
        "start_time = datetime.datetime.now()\n",
        "filename = start_time.strftime(\"%Y%m%d%H%M%S\") + \".txt\"\n",
        "with open(filename, \"w\") as file:\n",
        "  for index, phrase in enumerate(word_lists):\n",
        "    messages = [\n",
        "        SystemMessage(\n",
        "            content=\"You are an AI which is straight to the point and tell out 4 to 5 word description of the image.\",\n",
        "        ),\n",
        "        HumanMessage(\n",
        "            content=\n",
        "            f\"\"\"section: {phrase}\n",
        "                and here are the characters: {characters}\n",
        "                \n",
        "                Here is what you have to do\n",
        "                -dont use names only use descriptions of the characters and physical traits Never use names of the characters because the image generator will not understand them\n",
        "                -remeber the action thats happening in the image is really important so emphasis on the action a lot and less on the character description\n",
        "                -give most importance to that section of that story and generate description only and only based on that section\n",
        "                -you should generate a 10 word description of the image which the image generator will understand\n",
        "                -dont mention he , him , her ,they and any other pronouns the image generator will not understand them\n",
        "                -dont use names at all\n",
        "                \n",
        "                here are some examples\n",
        "                section:Leo guided Sammy through the wilderness, sharing his wisdom and protecting him from danger. As gratitude\n",
        "                character: Sammy : squirrel , Leo : Lion\n",
        "                output description: Lion guided squirrel through the wilderness, sharing Lion's wisdom and protecting squirrel from danger. As gratitude\n",
        "                \n",
        "                always return a  word description of the image\n",
        "            \"\"\",\n",
        "        ),\n",
        "    ]\n",
        "    image_description = chat(messages).content\n",
        "    image_description_list.append(str(image_description))\n",
        "    progress = (index + 1) / len(word_lists) * 100\n",
        "    print(f\"Progress for image generation prompts: {progress:.2f}%\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r8-pjxROTo4G"
      },
      "source": [
        "### Audio Generation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xwPifmZ-E_T1"
      },
      "outputs": [],
      "source": [
        "from elevenlabs import generate, play, set_api_key, save\n",
        "set_api_key(ELEVENLABS_API)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o5J7IIPYFAZV"
      },
      "outputs": [],
      "source": [
        "audio = generate(\n",
        "  text=f\"{story}\",\n",
        "  voice=\"Bella\",\n",
        "  model=\"eleven_monolingual_v1\"\n",
        ")\n",
        "# play(audio)\n",
        "save(audio,'test.mp3') # type: ignore"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import pyttsx3\n",
        "\n",
        "engine = pyttsx3.init()\n",
        "voices = engine.getProperty('voices')\n",
        "engine.setProperty('voice', voices[1].id)\n",
        "engine.save_to_file(f\"{story}\" , f'audio_{1}.mp3')\n",
        "engine.runAndWait()\n",
        "\n",
        "# for i, voice in enumerate(voices, start=1):\n",
        "    # engine.setProperty('voice', voice.id)\n",
        "    # engine.say('The quick brown fox jumped over the lazy dog.')\n",
        "    \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7WNNQ-mXTvQO"
      },
      "source": [
        "### Image Generation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from PIL import Image, ImageFont, ImageDraw\n",
        "\n",
        "# adding subtitle\n",
        "\n",
        "def add_text_to_image(image_path, text):\n",
        "    # Open the image\n",
        "    image = Image.open(image_path)\n",
        "\n",
        "    # Calculate the font size based on the image size\n",
        "    width, height = image.size\n",
        "    font_size = int(height * 0.06)  # Adjust this multiplier to control the font size\n",
        "\n",
        "    # Load the font\n",
        "    font = ImageFont.truetype('arial.ttf', font_size)\n",
        "\n",
        "    # Estimate the text size by getting the bounding box\n",
        "    text_bbox = font.getbbox(text)\n",
        "    text_width = text_bbox[2] - text_bbox[0]\n",
        "    text_height = text_bbox[3] - text_bbox[1]\n",
        "\n",
        "    # Adjust the font size if the text is wider than the image\n",
        "    while text_width > width:\n",
        "        font_size -= 1\n",
        "        font = ImageFont.truetype('arial.ttf', font_size)\n",
        "        text_bbox = font.getbbox(text)\n",
        "        text_width = text_bbox[2] - text_bbox[0]\n",
        "        text_height = text_bbox[3] - text_bbox[1]\n",
        "\n",
        "    # Calculate the position of the text at the bottom center of the image\n",
        "    text_x = (width - text_width) // 2\n",
        "    text_y = height - text_height - int(height * 0.05)  # Adjust this multiplier to control the vertical position\n",
        "\n",
        "    # Create a translucent background for the text\n",
        "    background_color = (0, 0, 0, 128)  # RGBA value with alpha transparency\n",
        "    text_background = Image.new('RGBA', (text_width, text_height), background_color)\n",
        "\n",
        "    # Draw the text on the background\n",
        "    draw = ImageDraw.Draw(text_background)\n",
        "    draw.text((0, 0), text, font=font, fill=(255, 255, 255))\n",
        "\n",
        "    # Paste the text background onto the image\n",
        "    image.paste(text_background, (text_x, text_y), mask=text_background)\n",
        "\n",
        "    # Save the resulting image\n",
        "    image.save(image_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from diffusechain import Automatic1111\n",
        "\n",
        "api = Automatic1111(baseurl='http://localhost:7860')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "seed = -1\n",
        "\n",
        "for index, image_generation_dict in enumerate(image_description_list):\n",
        "    # print(\"Description:\", image_generation_dict[\"description\"])\n",
        "    # print(\"Positive Prompts:\", image_generation_dict[\"positive_prompt\"])\n",
        "    # print(\"Negative Prompts:\", image_generation_dict[\"negative_prompts\"])\n",
        "    # print()\n",
        "    print(image_generation_dict)\n",
        "    \n",
        "    result = api.txt2img(prompt=f\"{image_generation_dict},(((text))),((color)),(shading),background,noise,dithering,gradient,detailed,out of frame,ugly,error,Illustration, watermark,(((vector graphic))),medium detail, oil painting from studio ghibli film, by noriyuki morimoto, digital artist, bright and bold colors, expansive sky, sense of adventure, epic scope studio ghibli\",\n",
        "                    negative_prompt=f\"(((text))),((color)),(shading),background,noise,dithering,gradient,detailed,out of frame,ugly,error,Illustration, watermark, Blurry, ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, extra limbs, disfigured, deformed, body out of frame, bad anatomy, watermark, signature, cut off, Low quality, Bad quality, Long neck\",\n",
        "                    width=512,\n",
        "                    height=512,\n",
        "                    sampler_index=\"DPM++ 2M SDE Karras\",\n",
        "                    sampler_name=\"DPM++ 2M SDE Karras\",\n",
        "                    save_images=True,\n",
        "                    seed=-1,\n",
        "                    steps=30\n",
        "                    )\n",
        "    \n",
        "    seed = result.info[\"seed\"]\n",
        "    result.image.save(f\"image_{index}\")\n",
        "    add_text_to_image(f\"image_{index}\", f'{word_lists[index]}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Qxm3e2qTryW"
      },
      "source": [
        "### Video Compilation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hh4WTIC_c_ZF"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "from moviepy.editor import ImageClip, concatenate_videoclips\n",
        "from moviepy.audio.io.AudioFileClip import AudioFileClip\n",
        "import os\n",
        "from moviepy.editor import ImageSequenceClip, concatenate\n",
        "from moviepy.editor import TextClip\n",
        "from moviepy.editor import CompositeVideoClip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "262jXxBIdATk",
        "outputId": "29bdc1e4-ec65-4f2a-c41d-f215324ec801"
      },
      "outputs": [],
      "source": [
        "def create_video(image_dir, audio_file, subtitle_list, output_file):\n",
        "    # Load the sequence of images\n",
        "    image_files = sorted(os.listdir(image_dir))\n",
        "\n",
        "    # Calculate the duration for each image based on the audio duration\n",
        "    audio = AudioFileClip(audio_file)\n",
        "    duration_per_image = audio.duration / len(image_files)\n",
        "\n",
        "    clips = []\n",
        "    for i, img in enumerate(image_files):\n",
        "        # Create an ImageClip for each image with the specified duration\n",
        "        image_path = os.path.join(image_dir, img)\n",
        "        image = ImageClip(image_path, duration=duration_per_image)\n",
        "\n",
        "        clips.append(image)\n",
        "\n",
        "    # Concatenate the clips to create the final video\n",
        "    final_clip = concatenate_videoclips(clips)\n",
        "\n",
        "    # Set the audio for the final clip\n",
        "    final_clip = final_clip.set_audio(audio)\n",
        "\n",
        "    # Set the frame rate for the final clip\n",
        "    final_clip.fps = 24\n",
        "\n",
        "    # Write the video file\n",
        "    final_clip.write_videofile(output_file, codec='libx264', audio_codec='aac', fps=24)\n",
        "\n",
        "# Provide the image directory, audio file, and output file path\n",
        "# only give the\n",
        "project_dir = './test'\n",
        "image_directory = f'{project_dir}/images'\n",
        "audio_file = f'{project_dir}/audio.mp3'\n",
        "output_file = f'{project_dir}/output_video_1.mp4'\n",
        "subtitle = word_lists\n",
        "\n",
        "\n",
        "# Call the create_video function\n",
        "create_video(image_directory, audio_file, subtitle, output_file)"
      ]
    }
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