YouTube Video Automation Bot

This project is inspired by Filipe Deschamps' series and combines multiple technologies to create a bot that automatically generates and uploads videos to YouTube. Using IBM Watson, Adobe After Effects, Wikipedia, Google Images API, Algorithmia, JavaScript, and Node.js, this bot performs the following automated tasks:

• Collects text from Wikipedia.
• Downloads and resizes images from Google Images.
• Creates videos using After Effects templates in a headless environment.
• Fully uploads to YouTube, including title, description, tags, and thumbnail.

Technologies Used

• IBM Watson: Text-to-speech processing and data analysis.
• Adobe After Effects: Video editing and rendering via script.
• Wikipedia API: Source of text for the videos.
• Google Images API: Retrieves images related to the content.
• Algorithmia: Automated image and data processing.
• JavaScript/Node.js: Core logic of the bot and process automation.

Features

1. Data Collection: The bot uses the Wikipedia API to gather text about a specific topic.
2. Image Downloading: With the Google Images API, it fetches relevant images and resizes them as needed.
3. Video Creation: The images and text are inserted into an After Effects template, which is rendered in a headless environment via script.
4. YouTube Upload: The generated video is automatically uploaded to YouTube, including title, description, tags, and a custom thumbnail.

Requirements

• Node.js
• Adobe After Effects with headless scripting support
• API Key for Google Images and Wikipedia
• IBM Watson account for text-to-speech (optional)

Installation

1. Clone this repository:

   git clone https://github.com/Pedro-Estevao/bot-maker.git
   cd bot-maker

2. Install dependencies:

   npm install  

API: Algorithmia

To test the bots, you need to create your access key. Visit Algorithmia and sign up. After logging into your account, go to the Dashboard, find API Keys in the menu, and copy your key.

In the project, navigate to the ./credentials folder and create a file named algorithmia.json. Then, add your API key as follows:

{
   "apiKey": "YOUR_API_KEY_HERE"
}

API: Watson

Similarly, go to IBM and obtain your Watson credentials.

Once logged in, go to Catalog > AI > Natural Language Understanding. Click Create on the new page. Then, in the side menu, look for Service Credentials.

With the credentials in hand, create a watson-nlu.json file inside ./credentials:

{
   "apikey": "...",
   "iam_apikey_description": "...",
   "iam_apikey_name": "...",
   "iam_role_crn": "...",
   "iam_serviceid_crn": "...",
   "url": "..."
}

Setup: Google Cloud Platform

Before creating the APIs, you must link your Google account with Google Cloud Platform. Log into Google Cloud Platform, click Start Free Trial, and accept the Terms and Conditions.

Note: Some Google Cloud Platform resources require payment information, but we will only use the free resources.

Creating a Project

Now, let's create a project to link the APIs. Click Select Project at the top of the page, then New Project. Name the project and click Create.

Once the project is created, it will appear in a menu where you can select it.

API: Custom Search API

With the project created, let's enable and configure the API. Click on APIs & Services > Library in the left menu.

Search for Custom Search API, click Enable, and wait for activation.

Once activated, a message will prompt you to create API credentials. Click Create Credentials.

Select Custom Search API from the dropdown and click What credentials do I need?.

Copy your API Key, click Done, and return to the project folder. Navigate to ./credentials and create a new file called google-search.json with the following content:

{
   "apiKey": "YOUR_API_KEY_HERE"
}

API: Custom Search Engine

Now, let's configure a custom Google search engine. Visit Custom Search Engine, enter google.com as the site to search, choose your preferred language, and click Advanced Options. Select the most generic Thing schema and click Create.

Note: Learn more about schemas at schema.org.

Next, go to Control Panel, enable Image Search, and click Copy to Clipboard.

Back in google-search.json, add a new property with the search engine ID, resulting in:

{
   "apiKey": "YOUR_API_KEY_HERE",
   "searchEngineId": "YOUR_SEARCH_ENGINE_ID"
}

API: YouTube

Now, let's set up the YouTube API. The process is similar to Custom Search API.

Go to Google Cloud and enable the YouTube API. Click APIs & Services > Library, search for YouTube, and click Enable.

Note: In the tutorial, it is recommended to create a new project specifically for the YouTube API.

Click OAuth Consent Screen and enter only the Application Name (you can customize other fields later).

Go to Create Credentials > OAuth Client ID, select Web Application, choose an Application Name, and enter the following:

• Authorized JavaScript origins: http://localhost:5000
• Authorized redirect URIs: http://localhost:5000/oauth2callback

Click Create.

A window will appear with your credentials. Click OK, then download the credentials file, rename it to google-youtube.json, and save it inside the ./credentials folder.

How to Use

1. Run the bot:

   node index.js

2. The bot will automatically collect data, create the video, and upload it to your YouTube channel.

Contribution

Contributions are welcome! Feel free to open issues and submit pull requests.

License

This project is licensed under the MIT License.