DEV Community: M. Abdullah Bin Aftab

Amazon Q CLI: Rock Paper Scissor with Python 🕹️

M. Abdullah Bin Aftab — Wed, 04 Jun 2025 04:35:40 +0000

In this blog post, I'll walk you through my journey of creating a fun Rock Paper Scissors game using Python and Pygame, with the assistance of Amazon Q CLI. I'll cover everything from setting up the development environment to implementing game mechanics and generating custom assets.

🔧 Installing Amazon Q CLI on macOS

Amazon Q CLI is a powerful tool that helped me throughout the development process. Here's how I installed it on my macOS:

First install the AWS CLI using Homebrew:

brew install awscli

Then I configured my AWS credentials:

aws configure

Next, I installed the Amazon Q CLI:

pip install amazon-q-cli

After installation, I verified it was working:

q --version

I started using Amazon Q by running:

q chat

In the chat, I asked:

Can you help me structure a Rock Paper Scissors game using Pygame?

Amazon Q CLI became my coding companion, helping me generate code snippets, debug issues, and even create project documentation.

🎮 Designing the Game Structure

Based on Amazon Q's suggestions, I designed the game with three main states:

Menu State: The main menu where players can start the game.
Playing State: Where players select their move (Rock, Paper, or Scissors).
Result State: Displaying the outcome and allowing players to play again.

I created the main game file structure with Amazon Q's help:

q chat

I asked:

Can you help me create a main.py file for my Rock Paper Scissors game with state management?

🎯 Implementing Game Mechanics

For the core game mechanics, I needed to implement:

Player choice selection
Computer random choice generation
Winner determination logic

🎁 Final Thoughts

Building this Rock Paper Scissors game was a fun and educational experience.

Amazon Q CLI significantly accelerated my development process by:

Providing code snippets and structure
Helping debug issues quickly
Generating documentation
Offering best practices and optimization tips

The final game includes:

An intuitive user interface
Smooth animations and transitions
Visual feedback with particle effects
Custom-generated game assets
Multiple game states for a complete experience
If you're developing games or any software project, I highly recommend giving Amazon Q CLI a try. It's like having an expert developer by your side, ready to help whenever you need it.

🕹️ To run the game yourself:

git clone https://https://github.com/heighterses/rock-paper-scissor-Q-CLI
cd rock-paper-scissor-Q-CLI
pip install pygame
python main.py

Enjoy playing Rock Paper Scissors! 🎮

Case Study: Creating an ETL Data Pipeline using AWS Services - Real-World Problem

M. Abdullah Bin Aftab — Fri, 06 Dec 2024 18:04:47 +0000

Architecture

Overview

This ETL pipeline leverages several AWS services to fetch, process, and store YouTube videos with translated subtitles. The core components include:

AWS Lambda for processing video and audio
Step Functions for Orchestrating Workflows
S3 Buckets for storing raw and processed data
AWS Transcribe for converting audio to text
AWS Translate for translating the text into desired languages

Pipeline Steps

Fetching Video from YouTube

Video is fetched from YouTube and sent to an initial Lambda Function.
This function splits the video into separate audio and video files.

Step Functions Orchestration

AWS Step Functions orchestrate all the following processes.
The workflow controls each step, passing data between services and managing the pipeline flow.

Storing Raw Video and Audio

Audio and video files are stored separately here.
The bucket sends an email notification to confirm the files were successfully stored.

Merging Video and Audio Files

This Lambda function combines the audio and video files into a single file.
Error Handling: If merging fails, the pipeline stores the failed files in an Error Bucket.

Transcribing Audio to Text

The merged video’s audio is sent to AWS Transcribe to convert audio into text.
The resulting text is passed to the next Lambda function.

Proofreading Transcribed Text

This Lambda function checks the transcription for accuracy and readability.
If the text is poor quality, it can be flagged for manual review or re-transcription.

Translating Text into Target Language

The proofread text is sent to AWS Translate to convert it into a chosen language (e.g., Arabic, Italian, or Spanish).
The translated text is then passed to the next Lambda function.

Generating Subtitles and Merging Paragraphs

This function formats the translated text as subtitles and merges paragraphs if needed.
The final file is prepared for storage.

Storing Processed Video with Subtitles

The fully processed video with subtitles in the desired language is stored here.
This is the final output location where the processed video is accessible.

Error Handling

Error Bucket for Failed Merges: If audio and video files fail to merge, the Lambda Merge Function sends them to a designated Error Bucket.
Transcription and Proofreading Quality Check: Poor transcription quality detected by the Proofread Function can trigger a flag for manual review or re-transcription.

Notifications

**Email Notifications **for File Arrival in S3:After the raw video and audio files are stored in the S3 Raw Video and Audio Bucket, an email notification is sent to confirm successful storage.

Benefits of This Pipeline

Automation: Streamlines the entire video processing workflow without manual intervention.
Scalability: AWS services like Lambda and Step Functions allow the pipeline to handle many videos.
Language Support: AWS Translate enables easy translation to multiple languages, broadening the video’s reach.
Error Management: Dedicated error-handling buckets and flags ensure issues are logged and handled efficiently.

How to Import Pandas(library) in AWS Lambda Functions - AWS Lambda Layers

M. Abdullah Bin Aftab — Wed, 16 Oct 2024 19:13:43 +0000

Imagine you need to run a Python script on the AWS Lambda function and you get this error 🔻

{
  "errorMessage": "Unable to import module 'lambda_function': No module named 'pandas',
  "errorType": "Runtime.ImportModuleError"
  ...
}

Don't worry this is a common error and I am not going to make this long

How do you import Pandas in AWS Lambda Functions?

There are several ways but I am going to give you the easiest way to import pandas in AWS Lambda Function is to add Lambda Layer 🥪

What is AWS Lambda Layer?

It is a 🧀 cheese layer in Lambda Function containing additional code like libraries, dependencies, etc.

In Simple Words

AWS Lambda Layers are like building blocks for your functions. Imagine you need extra tools (like the Pandas library) to complete a project.

Instead of packing all those tools inside every single project (which wastes space and time), AWS allows you to create layers of tools (libraries, dependencies, or shared code).

These layers sit outside your main function but are always available when your function needs them.

In short, Lambda Layers help you:

Separate the main logic from the extra libraries to save space in your code. You can reuse libraries and code across multiple Lambda functions.

Easily update or manage your dependencies without changing your core function code.

Think of layers as an extra storage box attached to your Lambda function, holding everything your function needs to work smoothly. You can stack multiple layers on your function without cluttering your main code.

Steps to add Lambda Function Layer and Import Pandas

It takes only 3 steps to run Pandas in your Lambda Function successfully

Step 1 - Open Lambda Function through your AWS Management Console

As you can see we have an option Layers under the name of our Lambda Function, in my case, it's "import-pandas-function" and the Layers count is 0

Step 2- Add Script in your AWS Lambda Function

This step is further divided into two steps because we need to add a Python script that contains some Pandas code and write a test event in JSON to verify whether the code is running correctly.

2.1 - Add Python script - you can copy this code 🡻

import json
import pandas as pd

def lambda_handler(event, context):
    data = event.get('data', [])

    df = pd.DataFrame(data)

    if not df.empty:
        mean_value = df['column_name'].mean()

        result = {
            "mean_value": mean_value,
            "data_shape": df.shape,
            "summary": df.describe().to_dict()
        }
    else:
        result = {
            "message": "Empty DataFrame"
        }

    # Return the response
    return {
        'statusCode': 200,
        'body': json.dumps(result)
    }

2.2 - Add Test script in Json in the test tab - you can copy this code 🡻

{
  "data": [
    {"column_name": 10, "other_column": "A"},
    {"column_name": 20, "other_column": "B"},
    {"column_name": 30, "other_column": "C"},
    {"column_name": 40, "other_column": "D"}
  ]
}

Press the test button you probably got the 🔴error:-
"errorMessage": "Unable to import module 'lambda_function': No module named 'pandas', "errorType": "Runtime.ImportModuleError" ...

Step 3 - Add AWS Lambda Layer to Successfully run the Pandas in your Code

Scroll down to your Lambda Function, you probably can see the "Layers" separate section at the end of the page

3.1 - Click "Add a Layer"

After Clicking the "Add a Layer" you can see the page which has a couple of sections "Function runtime settings" and "Choose a layer"

3.2 - Click "AWS layers"

You can see three options in the "Choose a layer" section click the "AWS layers".

3.3 - Choose "AWS layers"

After selecting the AWS layers you can see the dropdown under "AWS layers".

3.4 - Choose "AWS layers" and "Version"

In a dropdown of "AWS layers" select -> AWSSDKPandas-Python312

In a dropdown of "Version" select -> 13(select the most one)

click the "Add" button

3.5 - Make sure the "Function Overview"

When your page is directed to the function overview you can see the layer is added below the function name "import-pandas-function"

Step 4 - Test the Function

You've successfully got the Response "statusCode": 200

{
  "statusCode": 200,
  "body": "{\"mean_value\": 25.0, \"data_shape\": [4, 2], \"summary\": {\"column_name\": {\"count\": 4.0, \"mean\": 25.0, \"std\": 12.909944487358056, \"min\": 10.0, \"25%\": 17.5, \"50%\": 25.0, \"75%\": 32.5, \"max\": 40.0}}}"
}

Keep Coding 😎