This will require git to be installed on your device. If you have not installed git. and follow the instructions.

You will need to clone this repository to your local machine to colaborate on the project.

To do this, above the section that shows all the files and directories of the repostory you will see this: Click on the green code button then you should see a dropdown appear

Https is the easiest way to copy the repository so click the button next to the URL to copy to your clipboard or highlight the url and ctrl+v

Now in your device open up command prompt / terminal. You can check which version of git is installed using git --version.

Navigate to the directory where you want the project to be placed using cd d:/path/to/directory/.

Use git clone https://github.com/Kalani91/Project_bot.git to clone. You will be prompted to authenticate either through Github or username & password. This will create a new folder that has the name of the repository, and will set up the connection so that you can push changes to the repository on Github.

All done with setup.

Note: You should never modify files when in the main\master branch as this is considered the most stable point of the project.

Most IDE's come with source control management inbuilt which should automatically detect version control being used in project directory, makes creating branches, commiting, pushing, and pulling simpler with GUI and buttons.

python3 -m pip install -r requirements.txt

To be written

Streamlit:

• $ pip install streamlit

Graphing libraries:

• $ pip install plotly==4.14.3
• $ pip install wordcloud
• $ python -m pip install -U matplotlib

Spacy (for nlp_summary.py):

• $ pip install -U pip setuptools wheel
• $ pip install -U spacy
• $ python -m spacy download en_core_web_sm

Other:

• $ pip install pandas
• $ pip install nltk

Database connection:

• $ pip install mysql-connector-python

Must use command line.

1. On first run, go to dashboard.py and uncomment line 12 to install the NLTK Punkt Tokenizer Model when the file is run. It does not automatically install via pip. Afterwards you can comment it out again.
2. Navigate to src folder and execute the following command: streamlit run dashboard.py
3. There may be errors relating to the nlp_summary.py if you're unable to install the Spacy dependencies mentioned above. If so, comment out lines 11, 172, and 174. The rest of the dashboard should show up as normal.

MySQL Connector/Python is available on PyPI:

$ pip install mysql-connector-python

# import db_instance from connector.py
from connector import db_instance

# connect to database
db_instance.connect()

# close database connection
db_instance.close()

# import db_instance from connector.py
# import discord_channel table, violated_message table, clean_message table, falgged_message table from tables.py
from connector import db_instance
from tables import discord_channel, violated_message, clean_message, flagged_message

# connect to database
db_instance.connect()

# get connection object
db_instance.get_connect()
# for each table object, there are functions you can call, for example
# get_schema() to get schema of violated_message table
violated_message.get_schema()

# get all the records from violated_message table
violated_message.select()

# get any number of records from violated_message table
violated_message.select(size=3)

# insert data to violated_message table
violated_message.insert(message_id, user_id, user_name, channel_id, violation_content, message_content, created_on)

# count numbers of violation for a particular user with given user id
violated_message.count_numbers_of_violation(user_id)

# close database connection
db_instance.close()

PyYAML is available on PyPI:

$ pip install PyYAML

This logging module has defined three different levels of logging.

• INFO: can be used for general information purpose, such as confirmation information
• DEBUG: can be used for debugging purpose, such as critical variable during a loop
• ERROR: can be used for critical error

INFO level output will be displayed in the terminal and stored in src/logs/debug.log.

DEBUG level output will be stored in src/logs/debug.log.

ERROR level output will be stored in src/logs/error.log.

At development stage, all three levels of logging are enabled. Once our app went to production version, only ERROR level will be enabled. So please make sure to use ERROR logging when you want to log any error while our bot is online.

In the main python file

import utils.logger

if __name__ == "__main__":
  # initialise logger with customized configuration
  utils.logger.setup_logging()

At any sub python file which you want to use logging function, for exmaple: connector.py

import logging

# at the global scope or any scope you want to include logging function
# get main logger, you can also put .current_file name after main. By doing this, logger can log your file name as well
logger = logging.getLogger("main.connector")

# you can pass string or variable
logger.info("Confirmation")

logger.debug(some_variable)

# please note if you want to also log the traceback information of an error, please use logger.exception()
logger.error(error_message)
logger.exception(error_message)