🚀 AWS Automation Dashboard using Python, Boto3, and Streamlit

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📌 Overview

This project is a fully functional AWS Automation Dashboard, built using Streamlit and Boto3, designed to automate infrastructure tasks like launching EC2 instances, uploading files to S3, and managing RDS databases.

✅ Web-based interface for AWS tasks
✅ Secure .env file for credentials
✅ Modular structure with clean Python code
✅ Deployed and tested on Amazon EC2

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🔧 Features

🖥️ EC2 Management

• Launch EC2 instance using button click

☁️ S3 Management

• Upload files to any S3 bucket from your browser

🗃️ RDS Management

• Create, delete RDS instance
• Take and restore snapshots
• Modify database size

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🗂️ Project Structure

AWS_MINI_PROJECT/
├── app.py                    # Streamlit Web Interface
├── .env                      # AWS keys and configuration
├── EC2/
│   └── create_ec2_instance_file.py
├── RDS/
│   ├── create_rds_instance.py
│   ├── delete_rds_instance.py
│   ├── create_snapshot.py
│   ├── modify_rds_instance.py
│   └── restore_from_snapshot.py
├── S3/
│   └── upload_to_s3.py
├── screenshots/
│   ├── ec2.png
│   ├── s3.png
│   └── rds.png
├── architecture_diagram.png
├── requirements.txt
└── README.md

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🖼️ Architecture Diagram and 📹 Demo Output video

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⚙️ Setup Instructions (Step-by-Step Guide)

✅ 1. Prerequisites

Install Python 3.8+ and pip

You must have AWS CLI installed and configured with valid credentials.

Create an IAM user with the following permissions:

- AmazonEC2FullAccess

- AmazonS3FullAccess

- AmazonRDSFullAccess

Ensure an EC2 key pair and valid AMI ID is available in your selected region.

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📁 2. Clone the Repository

Open terminal or Git Bash and run:

git clone https://github.com/22MH1A42G1/Aws-Automation-With-Boto3.git
cd Aws-Automation-With-Boto3

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📝 3. Create the .env File

Create a .env file in the root directory and add the following content:

AWS_ACCESS_KEY_ID=your_access_key
AWS_SECRET_ACCESS_KEY=your_secret_key
AWS_DEFAULT_REGION=ap-south-1

AMI_ID=ami-0ded8326293d3201b
KEY_PAIR_NAME=PemKey
EC2_INSTANCE_NAME=MyEC2Instance

RDS_USERNAME=admin
RDS_PASSWORD=Automation123
RDS_DB_INSTANCE=mydbinstance
RDS_SNAPSHOT_NAME=mysnapshot
RDS_RESTORE_INSTANCE=mynewdbinstance

⚠️ Make sure to replace the placeholders with your actual AWS values.

🚫 DO NOT commit this file to GitHub. Add .env to your .gitignore.

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📦 4. Install Project Dependencies

You can install the required libraries using pip:

pip install -r requirements.txt

Or install manually:

pip install streamlit boto3 python-dotenv

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🧪 5. Run the Application Locally

Use this command to start your Streamlit app:

python -m streamlit run app.py

# Visit the following URL in your browser:
http://localhost:8501

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☁️ 6. Deploy to AWS EC2 Instance

Step-by-step deployment on EC2

a. Launch an EC2 Instance

- Select Ubuntu 20.04 or Amazon Linux 2

- Choose a t2.micro (Free Tier eligible)

- Create or use an existing Key Pair (PemKey)

- Allow inbound traffic on port 8501 (Streamlit) and port 22 (SSH)

b. Connect to the Instance via SSH

# Run this from your local machine:
ssh -i "PemKey.pem" ec2-user@<EC2-Public-IP>

c. Update the instance and install Python

# For Amazon Linux:
sudo yum update -y
sudo yum install python3 git -y

# For Ubuntu:
sudo apt update && sudo apt install python3-pip git -y

d. Upload or Clone Your Project

# Option 1: Clone from GitHub (requires your repo to be public or provide access):
git clone https://github.com/yourusername/aws-automation-dashboard.git

# Option 2: Use SCP to upload files:
scp -i PemKey.pem -r ./aws-automation-dashboard ec2-user@<EC2-Public-IP>:~/project

e. Navigate to your project directory

cd aws-automation-dashboard

f. Recreate .env File

# Paste your .env content again on EC2 manually or upload it via SCP.

#### g. Install Project Requirements on EC2

pip3 install -r requirements.txt

# Or manually:
pip3 install streamlit boto3 python-dotenv

h. Run the App on EC2

streamlit run app.py --server.port 8501 --server.enableCORS false

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🌐 7. Access the App in Browser

# Open a browser on your local machine and visit:
http://<EC2-Public-IP>:8501

🔓 Make sure port 8501 is open in your EC2 security group settings.

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🧹 8. Clean Up (Optional)

To avoid unexpected AWS charges:

• Stop or terminate your EC2 instance
• Delete RDS instances and snapshots
• Remove unused S3 files

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🎉 Done! You have successfully set up the AWS Automation Dashboard.

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📸 Output Screenshots (Evidence)

🔐 Before Login

✅ After Login

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🖥️ EC2 Automation

✅ EC2 Instance Launch via Dashboard

❌ EC2 Instance Terminated via Dashboard

🌐 EC2 Deployed Server Screenshot

📁 Project Files on EC2 Server

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☁️ S3 Bucket Automation

📦 Before Upload (Empty Bucket)

✅ File Upload via Dashboard

📁 After File Upload

❌ S3 Bucket Deleted

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🗃️ RDS Automation

✅ RDS Instance Created

🧩 Snapshot Created

📈 DB Instance Modified

♻️ RDS Restored from Snapshot

❌ RDS Deletion Initiated

🧼 Output: RDS Created

✅ Output: RDS Modified

🧹 Output: RDS Deletion

🔁 Output: RDS Restored

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🔒 IAM User Management

🧾 IAM User Created with Roles & Access Key

🧼 IAM User Deleted

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🧭 UI Navigation Panels

🧠 Project Applications Panel

📋 Guidelines Panel

🧭 Navigation Panel