Containerized Fruit Recognition App

Project 4 of Software Engineering (CSCI-UA 474) course. This project is a fully containerized system composed of three components:

• A Flask web app to upload images and display classification results
• A machine learning client that performs fruit image classification
• A MongoDB database to store uploaded images and classification metadata

The system uses a webcam to capture fruit images, identifies the fruit via a machine learning model, and fetches a descriptive definition using the Merriam-Webster API. It is designed to be extended with features such as audio feedback, making it useful for visually impaired users or interactive learning environments.

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Prerequisites

Before running this app, make sure the following are installed:

• Docker Desktop

Windows with WSL 2

If you are using Windows with WSL 2, you have two options:

Option 1: Enable Docker Desktop WSL 2 Integration (Recommended)

1. Open Docker Desktop
2. Go to Settings → Resources → WSL Integration
3. Enable integration for your Linux distro (e.g., Ubuntu)
4. Click Apply & Restart

To verify that Docker is available in WSL, run:

docker --version
docker compose version

After installation, verify with:

docker --version
docker compose version

Mac

For macOS users, ensure that Docker Desktop is running before proceeding. You can verify Docker is working with:

docker --version
docker compose version

Running the App

1. Clone the Repository

git clone git@github.com:cl3880/containerized-app-project.git
cd containerized-app-project

2. Set Up Environment Variables

Copy the example .env file and add your Merriam-Webster API key:

cp env.example .env

Edit .env with the following content:

MW_API_KEY=your_mw_api_key_here
MW_URL=https://dictionaryapi.com/api/v3/references/collegiate/json
MONGODB_URI=mongodb://mongodb:27017/containerapp
ML_CLIENT_URI=http://ml_client:5000

Important: You need a valid Merriam-Webster API key for the definition feature to work. You can get a free API key at the Merriam-Webster Developer Center.

3. Build and Start All Containers

docker compose up --build

This command will:

• Build the Flask web app container
• Build the ML client container
• Start the MongoDB container
• Link all three containers together

This project uses docker compose. If you're using legacy Docker, use docker-compose instead.

4. Access the Application

After the build completes, open your browser to:

http://localhost:5001

5. Stopping the Application

To stop the application, press Ctrl+C in the terminal where Docker Compose is running, or run:

docker compose down

Manual Testing

For manual testing (e.g., verifying classification without using the UI), a helper script is provided.

Two test images are included in web-app/tests/test_data/, along with two sample training images in web-app/tests/sample_training_images

You can run the test upload script as follows:

python -m "web-app.tests.test_upload" banana

Replace banana with one of:

• apple
• banana
• train_apple
• train_banana

Team Members

Mahmoud Shehata
Patrick Cao
Chris Leu
Syed Naqvi

License

This project is licensed under the GNU General Public License. See the LICENSE file for details.