Smart CV Interview Prep

Table of Contents

• Project Overview
• Features
• Tech Stack
• Architecture
• Installation
• Running Locally
• API Endpoints
• Frontend Usage
• Testing
• Deployment
• Contributing
• License
• Contact

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Project Overview

Smart CV Interview Prep is a full-stack web application designed to assist users in preparing for technical interviews by analyzing and parsing uploaded CVs (PDF format). The system extracts key CV sections (Projects, Experience, Skills, etc.) using a backend parsing service and provides a user-friendly interface to review and prepare for interviews effectively.

This project integrates a React frontend with a FastAPI backend and utilizes advanced file parsing techniques for PDF documents. It supports privacy-preserving and efficient CV processing workflows, aiming to streamline the interview preparation journey.

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Features

• Upload PDF CVs and extract structured CV sections.
• Display parsed CV sections interactively.
• Real-time error handling and validation.
• Support for common CV sections: Projects, Experience, Skills, Extracurricular.
• Responsive, modern UI using React and Tailwind CSS.
• Backend API using FastAPI with async processing.
• Docker-ready and cloud deployment support.

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Tech Stack

Layer	Technology
Frontend	React, Tailwind CSS, Vite
Backend	Python, FastAPI, Uvicorn
Parsing	PDFMiner / PyMuPDF / Custom Logic
Deployment	Heroku / Docker / Local

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Architecture

graph TD;
  User -->|Upload PDF| Frontend(UI)
  Frontend -->|POST /parse-cv| Backend(API)
  Backend -->|Process PDF| Parser
  Parser --> Backend
  Backend -->|Parsed JSON| Frontend
  Frontend -->|Display| User

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Installation

Prerequisites

• Node.js (v16+ recommended)
• Python 3.10+
• Git
• pip for Python packages
• (Optional) Docker

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Clone Repository

Bash

git clone https://github.com/Atishyy27/lastprep.git cd lastprep

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Backend Setup

1. Create and activate a Python virtual environment:

Bash

`cd backend python -m venv venv

On Windows

.\venv\Scripts\activate

On Linux/Mac

source venv/bin/activate`

2. Install dependencies:

Bash

pip install -r requirements.txt

3. Run the backend server:

Bash

uvicorn backend.main:app --host 0.0.0.0 --port 5000 --reload

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Frontend Setup

1. Move to the frontend directory:

Bash

cd ../frontend

2. Install dependencies:

Bash

npm install

3. Start the frontend dev server:

Bash

npm run dev

The app will be available at http://localhost:5173 by default.

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API Endpoints

Endpoint	Method	Description
/parse-cv	POST	Upload PDF and parse CV data
/health	GET	Check backend health status

Export to Sheets

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Frontend Usage

1. Navigate to the Upload page.
2. Select a PDF CV file (only PDFs are allowed).
3. Click "Upload & Parse CV".
4. View extracted CV sections with detailed breakdowns.
5. Use the UI components to review and prepare for interviews.

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Testing

Backend unit tests:

Tests are located in backend/tests/.

Bash

cd backend pytest

Frontend tests:

Tests (if any) can be run with:

Bash

cd frontend npm test

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Deployment

Heroku

1. Ensure a Procfile exists with the following content:Bash

   web: uvicorn backend.main:app --host=0.0.0.0 --port=${PORT:-5000}

2. Commit and push changes to the Heroku remote.

3. Set buildpacks for Python and Node.js in the Heroku dashboard.

4. Deploy and monitor logs:Bash

   heroku logs --tail

Docker (Optional)

• Build docker images for the backend and frontend.
• Use docker-compose to run the multi-container app.

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Contributing

Contributions are welcome! Please fork the repo and open pull requests for new features, bug fixes, or improvements. Follow the existing code style and provide tests where applicable.

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License

This project is licensed under the MIT License - see the LICENSE file for details.