Learnopia is a comprehensive e-learning platform built with Node.js and Express.js for the server-side functionality. The application utilizes MongoDB for data storage, Cloudinary for image media storage, and AWS S3 for course video storage. In addition, a course recommendation model has been implemented using machine learning algorithms with FastAPI.

• Features
• Technologies Used
• Setup Instructions
• Node.js APIs
• Course Recommendation Model
• Usage
• Contributing
• License

• User authentication and authorization
• Course creation and management
• Image and video uploads
• Course recommendations based on user input

• Node.js - JavaScript runtime for the server-side
• Express.js - Web framework for building APIs
• MongoDB - NoSQL database for data storage
• Cloudinary - Image and media storage
• AWS S3 - Cloud storage for videos
• FastAPI - Web framework for building APIs in Python
• scikit-learn - Machine learning library for Python
• Pandas - Data manipulation and analysis library for Python

1. Clone the repository:

   git clone <repository-url>
   cd E-school-Server

2. Install dependencies:

   npm install

3. Configure environment variables: Create a .env file in the root directory and add the required environment variables (e.g., database URI, Cloudinary credentials).

4. Run the server:

   npm start

1. Clone the repository:

   git clone <repository-url>
   cd ML-Model-Server

2. Install Python dependencies:

   pip install -r requirements.txt

3. Configure environment variables: Create a .env file in the root directory and add the required environment variables.

4. Run the FastAPI server:

   uvicorn main:app --reload

• POST /register: Register a new user.
• POST /login: Authenticate user and issue token.
• GET /logout: Log the user out.
• GET /current-user: Get current user info (requires sign-in).
• POST /forgot-password: Initiate password reset.
• POST /reset-password/:id: Reset user password.
• GET /user/get-courses: Fetch user’s enrolled courses (requires sign-in).
• GET /user/mycart: Retrieve user’s cart (requires sign-in).

• GET /courses: List all courses.
• GET /courseView/:slug: View course details.
• POST /course/upload-image: Upload course image (requires sign-in).
• POST /course/create-course: Create a new course (requires sign-in, instructor).
• GET /course/:slug: Fetch course details.
• POST /course/video-upload/:id: Upload course video (requires sign-in).
• POST /course/lesson/:slug/:id: Add lesson to course (requires sign-in).
• PUT /course/publish/:courseId: Publish a course.
• GET /check-enrollment/:courseId: Check course enrollment status (requires sign-in).

• POST /make-instructor: Promote user to instructor (requires sign-in).
• POST /instructor-image: Upload instructor profile image (requires sign-in).
• GET /instructor-courses: Get instructor’s courses (requires sign-in).
• GET /instructor-payments: View instructor payment records (requires sign-in).

• GET /products: List all products.
• POST /products/upload-image: Upload product image (requires sign-in).
• POST /products/createproduct: Create new product (requires sign-in, instructor).
• POST /products/addtocart: Add product to cart (requires sign-in).

• Technology: Built using FastAPI and scikit-learn.

• Functionality:

  • Data Loading: Loads course data from a CSV file.
  • Preprocessing: Cleans the dataset by removing unnecessary columns and filling NaNs.
  • Text Vectorization: Uses TF-IDF to convert course subjects into a numerical format for analysis.
  • Similarity Calculation: Computes similarity scores between courses using a sigmoid kernel.

• Key Function:

  • recommend_course(course): Takes a course title as input and returns the top 10 similar courses based on content similarity.

• Access the e-learning platform at the designated URL after starting the Node.js server.
• Use the /recommend endpoint on the FastAPI server to get course recommendations by sending a course title as input.

Contributions are welcome! Please feel free to submit a pull request or open an issue to discuss changes.