SignLingo: Sign Language Learning Platform

SignLingo is an interactive web application designed to make learning sign language accessible and engaging. The platform utilizes a variety of multimedia components, including video tutorials, image-based quizzes, and a real-time, AI-powered hand sign recognition game that provides instant feedback using a device's webcam.

Features

• User Authentication System: Secure user registration, login, logout, and password recovery.
• Profile Management: Users can view and edit their personal information (name, age, email) and change their password.
• Centralized Learning Dashboard: A personalized hub that greets users and provides an at-a-glance overview of their learning progress.
• Interactive Learning Activities:
  • Video Lessons: Instructional videos for foundational knowledge.
  • Multiple-Choice Quizzes: Tests knowledge with image-based questions, immediate feedback, and sound effects.
  • AI Hand Sign Recognition: A real-time practice environment that uses a machine learning model to analyze a user's signs via their webcam.
• Gamification & Progress Tracking:
  • Dynamic Progress Monitoring: Visual progress bars and lesson statuses (Completed, Current, Not Started) track user advancement.
  • Motivational Elements: A daily streak counter encourages consistent practice.
• Responsive and Animated UI: The interface provides smooth visual feedback on user interactions, with animations on buttons, links, and other elements.

Technology Stack

• Backend: Python, Flask, SQLAlchemy
• Database: SQLite (with Flask-Migrate for schema migrations)
• Frontend: HTML, CSS, JavaScript
• Machine Learning: TensorFlow/Keras, OpenCV, MediaPipe
• Containerization: Docker, Docker Compose

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How to Run This Project

There are two methods to run this application: using Docker (easiest and most reliable) or setting it up locally in a Python virtual environment.

Method 1: Running with Docker (Recommended)

This is the easiest way to run the application, as it handles all dependencies and setup within a self-contained environment.

1. Prerequisite:

• You must have Docker Desktop installed and running on your machine.

2. Build and Run the Application: Open your terminal or command prompt, navigate to the project's root directory (the one containing docker-compose.yml), and run this single command:

docker compose up --build

• The --build flag will build the Docker image from the Dockerfile the first time you run it. This might take several minutes as it downloads the Python image and installs all dependencies, including TensorFlow.
• Once the build is complete, the container will start, and you will see server logs in your terminal.

3. Access the Application: Open your web browser and navigate to:

http://localhost:5001

(Note: We use port 5001 because the docker-compose.yml file maps it from the container's port 5000 to avoid potential conflicts on the host machine).

4. Stopping the Application: To stop the application, go back to your terminal and press Ctrl+C.

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Method 2: Local Setup (Without Docker)

If you prefer to run the application directly on your machine, follow these steps.

1. Prerequisites:

• Python 3.10
• Git

2. Setup Instructions:

• a. Clone the Repository:

  git clone [https://github.com/AnangAyman/Software-engineering-S4.git](https://github.com/AnangAyman/Software-engineering-S4.git)
  cd Software-engineering-S4

• b. Create and Activate a Virtual Environment:

  • On macOS/Linux:

    python3 -m venv venv
    source venv/bin/activate

  • On Windows:

    python -m venv venv
    .\venv\Scripts\activate

• c. Install Dependencies:

  pip install -r requirements.txt

  (Note: This step may take a significant amount of time due to the size of the machine learning libraries.)

• d. Set Up the Database: This command will apply the migration scripts to create the users.sqlite file and all necessary tables.

  flask db upgrade

• e. Seed the Database with Initial Data: This command populates the database with the lessons.

  flask seed_lessons

• f. Run the Application:

  flask run

• g. Access the Application: Open your web browser and navigate to: http://127.0.0.1:5000

Using the Application

1. Navigate to the site and click Sign Up to create an account.
2. Log In with your new credentials.
3. You will be directed to the Dashboard, where you can start learning.
4. For the Hand Sign Recognition game, your browser will ask for permission to use your webcam. You must Allow it for the feature to work.