Ruoka Bot

Description

Below is an example of an application (telegram bot) running on the AWS architecture. The functionality of this application is to collect information (menu) from local restaurants (Kehruuhuone, Wolkoff, The Kitchen) in the city of Lappeenranta 🇫🇮 once a week on Mondays, translate them in three languages, save it to a dedicated storage and, at the request of the user in the Telegram application, send a response with the current menu.

To access the bot functionality please visit:

List of available commands for the Bot:

• start - starts the bot
• info - provides additional information
• stop - stops the bot

For communication with a but please use available keyboard buttons, it doesn't reply if the command is unknown.

App Architecture

AWS Configuration

0. Create Lambda Function in AWS

more info:

• AWS. Lambda getting started

1. Copy the repository to your local machine:

git clone https://github.com/makarovm-m/Ruoka.git

Here is a simple structure of the repository:

Ruoka
├─ LottaBot
│  ├─ .env
│  ├─ bot.py
│  ├─ dynamodb_states.py
│  └─ requirements.txt
├─ README.md
├─ ScrapingFood
│  ├─ lambda_function.py
│  └─ layer_1
│     └─ requirements.txt
│     ├─ .env
│     ├─ scrapper_kehru_toCSV.py
│     ├─ scrapper_kitchen_toCSV.py
│     └─ scrapper_wolkoff_toCSV.py
├─ RuokaPerm.pem
└─ pics

• LottaBot folder which contains the script bot.py which should be launched at the AWS instance, requirements.txt and .env (don't forget to put) file which should contain token to your Telegram bot. Additionaly, I decided to save inputs from users to monitor how many users are using this service that is why dynamodb_states.py file was created. All content can be packed in a zip file and uploaded afterwards to the inctance.
• ScrappingFood folder contains AWS Lambda function lambda_function.py (will be used in AWS), environment variables, scraping scripts and it's dependencies.
• RuokaPerm.pem permission certificate to your AWS instance. It required for copying files from your local machine.

2. Optional. Create and activate virtual environment if you want to run and update the code.

python3 -m venv venv
source venv/bin/activate

3. Run the following commands to prepare your local env for further deployment:

# preparing layer for lambda function
cd layer_1                                  
mkdir python                                 
pip uninstall numpy                          
pip install -r requirements.txt -t ./python 

make sure that you don't have numpy 1.2.5. in your lambda layer, if exists then delete folders related to numpy. It can cause an error with Python v 3.9.

4. run the following commands to prepare your local env for further deployment:

# move files from layer_1 to python dir
mv scrapper_kehru_toCSV.py python/
mv scrapper_kitchen_toCSV.py python/
mv scrapper_wolkoff_toCSV.py python/
mv .env python
# create zip
zip -r ../layer_1.zip .

when zip package is prepared it can be uploaded to Layers in AWS Lambda.

5. Create layer in AWS and upload using ZIP file:

here some useful links about python packages in AWS:

• AWS. Python package

6. Attach layers to your Lambda function:

• layer that you have added
• layer that exists in AWS (AWSSDKPandas-Python39-Arm64) where Python and Numpy compatibility is configured

here are some useful links about layers in AWS:

• AWS. Configuration layers
• YouTube. How to use Layers in AWS lambda
• Medium. Getting started with aws lambdalayers

7. Create S3 bucket and give all nececcary permisions (read and write) to Lambda for reading and writing in AWS S3 service

• Towardsdatascience. Additing policies in AWS
• AWS. Adding policies to AWS lambda

8. The resulting view

when all done the resulting view should be like at the pic below:

9. Amazon EventBridge setup

Lastly we should trigger our AWS Lambda function by schedule. For this purpose we will be using Amazon EventBridge.

Here is a configuration for Modays at 8.15 AM:

10. Create a bot using BotFather

Short description provided here:

• Medium. Create a telegram bot

11. Create AWS instance and grant access to S3 and DynamoDB

Create role in AWS IAM

Attache all permissions reqired to that role (EC2 instance should read S3 and have full access to DynamoDB)

Assign new role created to your EC2 instance

More information available here:

• Towardsdatascience. Run your python scripts in amazon ec2
• AWS. EC2 instance access s3 bucket

12. ZIP your bot folder and upload to EC2 instance

# create zip package in local terminal
zip -r LottaBot.zip Ruoka/LottaBot
# upload your package to AWS instance 
scp -i /<YOUR_PATH>/Ruoka/RuokaPerm.pem /.../Ruoka/LottaBot.zip ec2-user@<YOUR_IP>:~

Where <YOUR_PATH> is your local path and <YOUR_IP> is your public inctance IP.

After that you need to create folders:

# in EC2 terminal 
sudo -i
mkdir home
cd home
mkdir ec2-user
cd home/ec2-user/
unzip LottaBot.zip
cd LottaBot

And install dependencies:

# in EC2 terminal 
sudo yum update
sudo yum install python3-pip
pip3 --version
pip install -r requirements.txt

When all done you can test your bot with the following command:

# in EC2 terminal 
python3 bot.py

For the future updates these commands can be useful:

# in your local terminal 
scp -i /<YOUR_PATH>/RuokaPerm.pem /<YOUR_PATH>/Ruoka/LottaBot/bot.py ec2-user@<IP>:/home/ec2-user/LottaBot
scp -i /<YOUR_PATH>/RuokaPerm.pem /<YOUR_PATH>/Ruoka/LottaBot/dynamodb_states.py ec2-user@<IP>:/home/ec2-user/LottaBot

Where <YOUR_PATH> is your local path and <YOUR_IP> is your public inctance IP.

13. Create service at your instance

It might happen that your bot can stop after termination of the terminal. To create a system service for your Python script, you need to create a service configuration file. Here's an example of how to create and configure the service file:

# in EC2 terminal 
sudo nano /etc/systemd/system/bot.service

In the text editor, add the appropriate service configuration content. Here's an example:

# in EC2 terminal 

[Unit]
Description=Your Bot Service
After=network.target

[Service]
User=ec2-user
ExecStart=/usr/bin/python3 /path/to/bot.py
WorkingDirectory=/path/to/working/directory
Restart=always

[Install]
WantedBy=multi-user.target

Replace /path/to/bot.py with the actual path to your bot.py script and /path/to/working/directory with the directory where your script resides.

Save the file (Ctrl + O), exit the editor (Ctrl + X).

Once you've confirmed that the bot.service file exists in the correct directory, try enabling the service again:

# in EC2 terminal 
sudo systemctl enable bot.service

# in EC2 terminal 
sudo systemctl start bot.service

You should now have your Python script running as a system service. You can check the status of the service using:

# in EC2 terminal 
sudo systemctl status bot.service

If there are any errors, you can view the logs using:

# in EC2 terminal, read last 100 logs
sudo journalctl -u bot.service -n 100

Remember to ensure that the bot.py script has executable permissions (chmod +x bot.py) and that it specifies the Python interpreter on the first line (e.g., #!/usr/bin/python3) to ensure it can be executed properly.