NADRA-User-Registration 🪪

This project demonstrates a FastAPI application using Beanie ODM (Object Document Mapper) for MongoDB. The project is containerized using Docker and Docker Compose for easy setup and deployment. Its also deployed on Docker-Hub 🐳🙂

Project Demonstration 📹

FlowChart of Microservices:

Project Structure 🧱

• protos/user.proto: Defines the overall structure of the app schema and functionalities in a universal syntax. Its the very first step when working with grpcs. After defining the user.proto file, run the command (given in user.proto file) in the terminal to generate compiler constructed user_pb2.py and user_pb2_grpc.py files.
• routes/auth_handler.py: Defines the encoding and decoding of JWT Tokens.
• routes/auth_bearer.py: Defines the JWT_Bearer Class and verifies JWT Tokens.
• routes/app.py: Contains the API end points, JWT Authentication, and proper API Validations for handling User APIS.
• server/models.py: Defines the User and UserModel and UserUpdateModel models using Beanie and Pydantic.
• app/server/db.py: Contains the database initialization logic.
• server/app.py: Contains the gRPC calls, DB initiallization on startup, and User Data storage in DB from the endpoints.
• main.py: The entry point for running the Uvicorn server.
• Dockerfile: The Dockerfile for building the routes/server images.
• docker-compose.yml: Docker Compose file for setting up the FastAPI and MongoDB services.
• requirements.txt: Python dependencies required for the project.

Note: Most of the files are redundant and not defined explicitly above e.g. routes/models.py is not defined above as same file with same code is present in the server/models.py

Features Loaded ⚙️

1. User Registration CRUD Operations: • Create new user registration. • Read user information. • Update user information. • Delete user registration.

2. JWT Authentication:

• Implemented secure JWT authentication for user login and access control.

3. Database Integration:

• Used Beanie (MongoDB ODM) to store and manage user information.

4. Dockerization:

• Containerized the application using Docker for easy deployment and scalability. Its also deployed on Docker-Hub 🐳🙂

5. Swagger Documentation:

• Provided comprehensive Swagger documentation for the API.

6. PEP8 Standards of code:

• Followed PEP8 Standards of Python syntax

Major Tasks Performed: 🛠️

1. Setup FastAPI Project:

• Initialized a new FastAPI project.
• Configured the project structure for microservices.

2. User Registration CRUD Endpoints:

• Implemented endpoints for creating, reading, updating, and deleting user registrations.
• Ensured validation and error handling.

3. JWT Authentication:

• Implemented JWT authentication for secure access.
• Created login and token generation endpoints.
• Secured CRUD endpoints using JWT authentication.

4. Database Configuration:

• Setup MongoDB connection.
• Created pydantic models and schemas for user information.
• Integrated MongoDB with FastAPI for data operations.

5. Dockerization:

• Created Dockerfile for the FastAPI application.
• Setup Docker Compose for multi-container setup (including MongoDB).

6. Swagger Documentation:

• Added Swagger documentation for all the endpoints.
• Ensured all routes, models, and authentication are properly documented.

7. Code Structure:

• Ensured modularity while coding.
• Followed PEP8 standards of coding throughout the project.

Getting Started 🏃

Prerequisites 📋

• Docker Desktop installed and up and running on your machine.

Installation 🛠️

1. Clone the repository:

   git clone https://github.com/cyber-evangelists/NADRA-User-Registration-Backend-Task/new/main?filename=README.md
   cd project-dir

2. Build and start the Docker containers:

   docker-compose up --build

3. The FastAPI application will be available at http://localhost:8000.

API Endpoints ⚡

Model Schemas 🧩

Notes 📒

• Ensure MongoDB is running and accessible for the FastAPI application to function correctly.
• The mongo_url in server/db.py is set to connect to a MongoDB instance at host.docker.internal.

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This project was made with 💖 by Saad Abdur Razzaq under the supervision of Sir Husnain