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Streamer-AI

Python based AI that uses Deep Neural Networks, Neuroevolution and Streamlabs APIs to live stream games while commentating over them at the same time.

Running The program

Usage:

python3 main.py --gen [generations] --file [file_name] \
    --config [config]

This writes to a file called say.txt.

--gen

Specify the number of generations to run the evolution for

--file

File to load the checkpoint from. checkpoints/ has saved checkpoints for generation 2492 and 2284.

--config

Configuration file for NEAT. neat.config contains the one used to train.

Requirements

Install the requirements with

sudo make

Alternative

Install the python requirements with

pip install -r requirements.txt

and install mpg123, fceux and add them to PATH.

Getting the API Key

  • Go to streamlabs.com then sign in using your account. Then go to API settings, create a new app and copy the client ID and client Secret. Click the Sample Authentication URL below and then copy the code in the URL.

  • Call a POST request to /token. Make sure to use the same Redirect URI as set up in the app. Example:

curl --request POST \
     --url 'https://streamlabs.com/api/v1.0/token' \
     -d 'grant_type=grant_type&client_id=client_id&client_secret=client_secret&redirect_uri=redirect_uri'

Example return:

{
 access_token: 'loXk8FTOFwKfrLP3bGCnJldBxuGX03a03iQdxR8A',
 token_type: 'Bearer',
 refresh_token: 'IXCGDha46Q4eHBKrijmAqUwScbsMSuBy9IopXp80'
}
  • Authorize this access_token for the scope socket.token using /authorize. Example:
curl --request GET \
 --url 'https://streamlabs.com/api/v1.0/authorize?response_type=response_type&client_id=client_id&redirect_uri=redirect_uri&scope=socket.token'
  • Use the access_token to get a socket_token. Call a GET request to /socket/token. Example:
 curl --request GET \
  --url 'https://streamlabs.com/api/v1.0/socket/token?access_token=access_token'

You can also use python's requests library instead of curl

  • Install with pip install requests
  • Example to get socket_token:
import requests

url = "https://streamlabs.com/api/v1.0/socket/token"

querystring = {"access_token":"access_token"}

response = requests.request("GET", url, params=querystring)

print(response.text)

Put the socket_token in a file called config, with the following format, shown in config.example:

socket_token: eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIxMjM0NTY3ODkwIiwibmFtZSI6IkpvaG4gRG9lIiwiYWRtaW4iOnRydWV9.TJVA95OrM7E2cBab30RMHrHDcEfxjoYZgeFONFh7HgQ

How it works

For an explanation for how the game runs, watch this video.

Short Explanation

How the game is played

This program uses a mathematical model, called a neural network, which simulates the brain of a human being. A neural network works by taking inputs and outputting probabilities for each of the outputs. This can be accomplished by using a sigmoid function.

Sigmoid

Neuroevolution of Augmenting Topologies, or NEAT is what this project uses. The way standard neuroevolution works is by randomly initializing a population of neural networks and using survival of the fittest to get the best model. The best networks in each generations are bred and some mutations are introduced. NEAT introduces features like speciation to make a much more effective neuroevolution model. Neuroevolution is known to do better than standard reinforcement learning models.

How the commentary works

Using a socketio client for python, we can establish a connection to Streamlab's Socket API. This API returns every alert in JSON format. We can decode these JSONs to return statements thanking the donator, subscriber, member, superchat donator, etc.
Example:

import socketio

URL = "http://example.com/socket-api" # Change this to whatever Socket API you are using
sio = socketio.Client()

@sio.on('connect')
def connect():
    # This function is called when the connection is established
    print("Connected")
    
# The event in quotes depends on your API, check the documentation
@sio.on("event")
def event(data):
    print(data)

# Connect to the URL specified above
sio.connect(URL)

The rest of the audio uses random number generations to generate the sentences. A sentence is picked from a list of sentences, stored in get_sentences.py.

Playing the audio

The audio is converted to speech using gtts, Google's text-to-speech converter. This is far more superior than pyttsx, which bugs out quite a bit.
The audio is then played using mpg123, called by os.system.

Future Work

  • Integrate the streaming directly into the program, preventing the need for OBS.
  • Prevent the game reopening after each genome.
  • Improve commentary in general.