Resume Tailoring API

A FastAPI-based backend for uploading resumes, extracting content, and tailoring them to specific job descriptions using NLP and embeddings.

Project Structure

app/
├── api/               # FastAPI routers and dependencies
├── core/              # Application settings and constants
├── db/                # Database session and Base declaration
├── models/            # SQLAlchemy ORM models
├── schemas/           # Pydantic request/response models
├── services/          # Business logic for auth, resumes, and job analysis
└── main.py            # FastAPI application entrypoint

Setup

1. Create and activate a virtual environment

   python -m venv .venv
   source .venv/bin/activate

2. Install dependencies

   pip install fastapi uvicorn[standard] sqlalchemy passlib[bcrypt] python-jose
   pip install python-multipart pdfplumber python-docx spacy sentence-transformers scikit-learn

3. Run database migrations (SQLite is used by default, tables are created on startup)

4. Start the API server

   uvicorn app.main:app --reload

Configuration

Environment variables can override defaults in app/core/config.py:

• DATABASE_URL – database connection string (defaults to SQLite file)
• SECRET_KEY – JWT signing key
• UPLOAD_DIR – path for uploaded resumes
• EMBEDDING_MODEL – SentenceTransformer model name

API Overview

• POST /auth/register – create a new user
• POST /auth/login – obtain a JWT access token
• POST /resumes/upload – upload and parse a resume file (PDF or DOCX)
• POST /jobs/analyze – analyze a job description against the latest or specified resume

Use the token from /auth/login as a Bearer token for protected endpoints.