AWS S3 and Rekognition Image Object Detection with Streamlit Frontend

This Python application utilizes AWS S3 for storing images and AWS Rekognition for object detection in those images. It provides a Streamlit frontend for easy interaction.

Installation

Clone the repository:

git clone https://github.com/your_username/your_repository.git

Navigate to the project directory:
```
cd your_repository
```
Install the required dependencies using pip:
```
pip install -r requirements.txt
```

Configuration

Before running the application, ensure you have configured your AWS credentials properly. You can do this by either setting environment variables or using AWS CLI.

export AWS_ACCESS_KEY_ID='your_access_key_id'
export AWS_SECRET_ACCESS_KEY='your_secret_access_key'
export AWS_DEFAULT_REGION='your_aws_region'

Alternatively, you can configure AWS CLI by running:

aws configure

Usage

To run the application, execute the following command:

streamlit run app.py

This will start the Streamlit application, which you can access through your web browser.

Features

Upload images to AWS S3 bucket.
Detect objects in uploaded images using AWS Rekognition.
Display detected objects along with confidence scores.

File Structure

./
├── app.py                  # Streamlit application script
├── requirements.txt        # List of Python dependencies
└── README.md               # Project README file

Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue if you encounter any problems or have suggestions for improvements.