Team Project repo

🧠 AcePrep — AI-Powered Interview Preparation Platform

AcePrep is an AI-driven interview preparation portal designed to help candidates practice coding, system design, and behavioral interviews through personalized, company-specific experiences.
Built with a modern tech stack combining Django, PostgreSQL, and Google Gemini AI, it offers intelligent evaluation, real-time feedback, and structured analytics for each interview round.

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🚀 Features Overview

Category	Key Features
🧩 Core Backend	Django-based REST API with authentication, PostgreSQL ORM, and Celery for background jobs
💻 Frontend Dashboard	Django Templates + Bootstrap UI for smooth navigation across interview stages
🤖 AI-Powered Evaluation	Gemini AI integration for resume fit scoring, coding question evaluation, and rubric feedback
🗣️ Behavioral & System Design	WebSocket-ready setup for live LLM-based interview simulations
⚙️ DevOps Integration	Travis CI for build automation and AWS EC2 for deployment
📊 Analytics Dashboard	Visual feedback on candidate performance and AI evaluation reports

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🏗️ System Architecture

Frontend (Bootstrap + JS) ↓ Django Backend (REST Framework) ↓ PostgreSQL ←→ Celery + Redis ↓ Google Gemini AI (via google-generativeai)

Each component interacts through REST APIs and WebSocket channels, ensuring real-time feedback and scalable performance.

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⚙️ Tech Stack

Backend

• Django 5.2.7
• Django REST Framework 3.14.0
• Django Channels 4.0.0 (ASGI + WebSocket)
• PostgreSQL (psycopg2-binary 2.9.7)
• Celery 5.3.4 + Redis 5.0.1
• Daphne 4.0.0 (ASGI server)

AI / ML

• Google Gemini 2.5 Flash (Text + Audio Models)
• OpenAI API (Configured, optional)
• PyPDF2 for resume parsing
• python-magic for file type validation

Frontend

• Django Templates
• Bootstrap 5.3.0
• Font Awesome 6.0.0
• Vanilla JavaScript (WebSocket client + DOM handling)

Deployment & DevOps

• Travis CI (Build/Test)
• AWS EC2 (Deployment)
• WhiteNoise (Static file hosting)
• python-decouple (Environment variables)

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🧩 Core Modules

Module	Description
accounts/	Custom User model, authentication, and JWT management
interviews/	Handles interview flow: coding, system design, and behavioral sessions
resumes/	Upload, parse, and analyze resumes using AI
ai_feedback/	AI evaluation, feedback, and scoring system
companies/	Company profiles, document uploads, and question generation
core/	Settings, Celery configuration, ASGI/WSGI apps

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🧠 AI Capabilities

Feature	Description
Resume Analysis	Extracts and evaluates resume content against job profiles
Coding Evaluation	Evaluates correctness, time complexity, and code quality
Behavioral Interview	Generates live, contextual questions with Gemini Audio
System Design Review	Analyzes architecture and scalability of user submissions
Feedback Rubrics	Produces detailed reports with strengths, weaknesses, and scores

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🧪 Example: Coding Evaluation Flow

Question:

Longest Substring with At Most K Distinct Characters

Evaluation Results:

• ✅ Correctness: Passed
• 💡 Score: 95 / 100
• 💬 Feedback: “Excellent use of sliding window optimization and clear code structure.”

AI Notes:

• Robust against edge cases
• Suggested improvements for C++ header modularity

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🗂️ Directory Structure

AcePrep/ ├── accounts/ ├── ai_feedback/ ├── companies/ ├── core/ ├── interviews/ ├── resumes/ ├── static/ ├── templates/ ├── manage.py ├── requirements.txt └── .travis.yml

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🧰 Installation Guide

1. Clone the Repository

Clone the repository from GitHub and navigate to the project folder.

git clone https://github.com/your-username/aceprep.git
cd aceprep

2. Create a Virtual Environment

Create a new Python virtual environment and activate it.

python3 -m venv venv
source venv/bin/activate

3. Install Dependencies

Install all required dependencies from requirements.txt.

pip install -r requirements.txt

4. Configure Environment Variables

Create a .env file in the root directory with the following variables: SECRET_KEY=your_secret_key DEBUG=True DB_NAME=aceprep_db DB_USER=postgres DB_PASSWORD=your_password DB_HOST=localhost REDIS_URL=redis://localhost:6379/0 GEMINI_API_KEY=your_gemini_api_key OPENAI_API_KEY=optional

5. Run Migrations

Apply database migrations to initialize your PostgreSQL schema.

python manage.py migrate

6. Start the Services

1. Start Redis
   redis-server

2. Start Celery worker
   celery -A core worker -l info

3. Start Django server
   python manage.py runserver

The application will be available locally at:
http://127.0.0.1:8000

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🚀 Deployment Guide

Travis CI Automation

• Lints code using flake8 and black
• Runs automated unit tests
• Deploys to AWS EC2 on successful build

Manual Deployment

To manually deploy to AWS EC2:

1. Push code to the main branch
   git push origin main
2. Restart server instance
   sudo systemctl restart aceprep

Access the deployed instance via:
http://3.134.77.211:8001

🧭 Project Progress (ZenHub Summary)

Pipeline	Status
✅ Done	Backend setup, coding round, Travis CI integration
⚙️ In Progress	Resume parsing, AI evaluation
📝 Sprint Backlog	Feedback rubric system, dashboard UI
🧊 Icebox	Behavioral + System Design interview modules

🧾 License

This project is licensed under the MIT License.