Note that instead of importing all classes globally, we can move the imports inside their respective functions.

This will speed up the bot’s startup and reduce memory usage in situations where not all classes are needed.

For example, if we only need to run one mode, the bot won’t import all other classes.

After declaring functions for each mode, we'll define a function that runs the full pipeline.

This function will sequentially execute all steps by calling the previously defined functions one by one.

Next, we'll declare the main function.

In this function, we'll declare a variable project_folder and assign it the project folder name.

We'll also create a menu here to choose the bot's operation mode.

We can implement this in two ways:

1. By parsing command-line arguments (argparse), allowing the bot to be launched from the command line.
2. By using a simple numeric input in the console.

We'll go with the simpler second option for now. I'll demonstrate the first option separately later.

So, let's first print the selection menu, and then get the user's choice using input.

Then, we'll run the corresponding mode based on the user's choice.

All that's left is to create the entry point ifname at the bottom of the file, with a call to the main() function.

This line ensures that the main() function is executed only when this file is run as the main program, not when it's imported as a module.

Example of bot control implementation via command line argument parsing (argparse)

How it works:

1. argparse is used to get the mode argument from the command line. This allows the user to choose which mode to run.

2. Depending on the value of args.mode, the corresponding functions are called (e.g., run_script_writer, run_script_divider, etc.).

3. Run it through the console, providing the required mode (e.g., full): python main.py full

The development of the bot control logic is now complete.

You can now run the bot in any mode and check if the constructor of the base class works.

After running, the project folders should be created automatically. This means that everything is working correctly.

You can create folders for another project by assigning a new value to the project_folder variable in the main function of the main.py file, and then run the bot again.

* Run the main.py file.

As we finish working on the bot’s basis, let’s think about what we haven’t taken care of yet.

Storing API keys and other project settings.

To address this, we will follow the standard practice and create a config.py file to store such data.

In this file, we will later create a settings class, where we will store these data in its attributes.

We will read the API keys from environment variables. This approach protects the data from accidentally being exposed in the code and repository.

So, let's create two files in the root of the project:

1. config.py - this will contain the settings class
2. .env - this is where we'll store API keys

To easily work with environment variables, we'll use the python-dotenv library. Install it via the terminal:

pip install python-dotenv

In the .env file, we'll place the necessary API keys (e.g., for OpenAI, Elevenlabs, Pexels) in the following format:

API_KEY_1=your_api_key_1

Next, we'll load these variables from the file using dotenv.

Next, in the config.py file:

1. Import load_dotenv.
2. Call the load_dotenv() function to load variables from the .env file.
3. Declare a Config class, where we will store API keys and other settings in its attributes.
4. Declare class attributes corresponding to the required API keys and retrieve their values using os.getenv.
5. Additionally, you can store other settings, such as video parameters, in the attributes of this class.

* Pay attention to this class. Since this class is only needed for storing data, there's no need to create a class constructor here; we can simply store the data in class attributes, as we have done in this case.

Also, note that attributes created outside the class constructor (the init method) are called class attributes, not instance attributes. Class attributes are the same for all instances of the class (objects), unlike instance attributes, which are created in the constructor and can be unique for each object.

Now, to use these parameters in other parts of the code, simply import the Config class and access its attributes.

For example, we import this class in the main.py file, retrieve the value of the Config.OPENAI_API_KEY attribute, and pass it to the ScriptWriter class constructor when creating an object.

* Note that in this case, we are accessing the attributes of the Config class directly without creating an instance of this class: Config.OPENAI_API_KEY

This is possible because OPENAI_API_KEY is a class attribute in the Config class, and we can access it through the class itself rather than through an instance.

However, we can also create an object and then access the class attributes through the object:

config = Config()
api_key = config.OPENAI_API_KEY

But if the OPENAI_API_KEY attribute were declared in the constructor of the Config class, we would only be able to access it through an instance (object) of the class.

The foundation of our bot is now complete.

We've done a lot of work and learned a lot. Now let's take a break for 1-2 days and then start putting meat on the skeleton of our Shorts Monster.

We'll fully develop the ScriptWriter class. After that, I'll give you recommendations for the remaining classes and set you free to swim on your own, so you can finish the rest independently.

☝️ The basis of the bot we've written can serve as the basis for many other projects. Especially those where multi-project capability is important.

For example, for generating WordPress websites.

So, let's begin developing the ScriptWriter class.

How can we implement this task?

1. By using the API of one of the AI assistants, such as ChatGPT or Claude. We'll choose ChatGPT.

2. We'll also add the option to use the g4f library, which allows access to various AI models for free.

Let's start by installing the necessary packages:

pip install openai
pip install g4f

In the ScriptWriter class, we have declared the execute method. This will be the central hub method where we build a pipeline to run other helper methods that we will write now.

To begin, let's decompose the tasks of the execute method into smaller steps.

We need to:

1. Accept the initial data in the method (a prompt for the request to the AI assistant).
2. Send a request to the AI assistant's API and save the response to a variable.
3. Write the result to a CSV file named script.csv and save it in the project folder.

But before we develop the method, let's refine the class constructor.

What arguments should it accept besides project_folder?

First, we need to pass the OpenAI API key. To do this, we'll accept an api_key argument.

We'll also set a model parameter to specify the OpenAI model, with a default value of "gpt-4".

* I will provide the code a bit later