AI Interview Backend

This is the backend for the AI Interview Platform, built with FastAPI and MongoDB. It provides APIs for user authentication, managing interview sessions, handling AI interactions, and managing questions.

Technologies Used

• FastAPI: A modern, fast (high-performance) web framework for building APIs with Python 3.7+ based on standard Python type hints.
• MongoDB: A NoSQL document database used for data storage.
• Motor: Asynchronous Python driver for MongoDB.
• Uvicorn: An ASGI web server, used to run the FastAPI application.
• python-dotenv: For loading environment variables from a .env file.
• CORS Middleware: Enabled for cross-origin resource sharing.
• Static Files: Serves uploaded files from the /uploads directory.

Project Structure

graph TD
    A[AI Interview Backend] --> B[main.py]
    A --> C[config/]
    A --> D[controllers/]
    A --> E[middlewares/]
    A --> F[models/]
    A --> G[routes/]
    A --> H[utils/]
    A --> I[uploads/]

    C --> C1[database.py]
    D --> D1[ai_controller.py]
    D --> D2[auth_controller.py]
    D --> D3[question_controller.py]
    D --> D4[session_controller.py]
    E --> E1[auth_middlewares.py]
    F --> F1[question_model.py]
    F --> F2[session_model.py]
    F --> F3[user_model.py]
    G --> G1[ai_routes.py]
    G --> G2[auth_routes.py]
    G --> G3[question_routes.py]
    G --> G4[session_routes.py]
    G --> G5[upload_routes.py]
    H --> H1[auth.py]
    H --> H2[hash.py]
    H --> H3[helper.py]
    H --> H4[prompt.py]

Loading

• main.py: The main application file and entry point for the FastAPI application.
• config/: Contains configuration files, such as database.py for MongoDB connection.
• controllers/: Implements the business logic for various API endpoints.
• middlewares/: Contains custom middleware, such as authentication middleware.
• models/: Defines the data models and schemas for MongoDB documents.
• routes/: Defines the API routes and links them to the respective controller functions.
• utils/: Provides utility functions for common tasks like authentication, hashing, and helper functions.
• uploads/: Directory for storing static files uploaded through the application.

Getting Started

Prerequisites

• Python 3.7+
• MongoDB Instance (local or cloud-based)

Installation

1. Clone the repository:

   git clone <repository_url>
   cd AI-Interview-Backend

2. Create a virtual environment:

   python -m venv myenv

3. Activate the virtual environment:

   • Windows:

     .\myenv\Scripts\activate

   • macOS/Linux:

     source myenv/bin/activate

4. Create necessary directories: You must create the uploads directory for static file serving:

   mkdir uploads

5. Install dependencies:

   pip install -r requirements.txt

   (Note: You might need to create a requirements.txt file first if it doesn't exist by running pip freeze > requirements.txt)

6. Configure Environment Variables: Create a file named .env in the root directory and populate it with the required variables:

   MONGO_URI=mongodb://localhost:27017/<YOUR PROJECT NAME>
   JWT_SECRET=<YOUR_SECRET_KEY>
   GEMINI_API_KEY=<YOUR GEMINI API KEY>

   Note: Replace <YOUR_SECRET_KEY> with a strong, randomly generated string. The GEMINI_API_KEY is commented out as it may not be universally required.

Running the Application

To run the FastAPI application using Uvicorn:

uvicorn main:app --reload

This will start the server, typically on port 8000 (as configured in .env). The --reload flag enables auto-reloading on code changes during development.

API Documentation

Once the server is running, you can access the interactive API documentation (Swagger UI) at:

http://127.0.0.1:8000/docs

Or the ReDoc documentation at:

http://127.0.0.1:8000/redoc