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ATS Resume Screening

AI-powered ATS resume screening monorepo with a Streamlit MVP and a SaaS-ready FastAPI + Next.js foundation.

Branch Strategy

This repository is now intended to be used as a single monorepo branch, not as two long-lived branches with different products.

  • main: canonical branch for the full monorepo. It contains the Streamlit MVP, shared Python packages, FastAPI backend, Next.js frontend, tests, docs, and infrastructure files.
  • Feature branches: create short-lived branches from main for changes, for example feature/scoring-tuning, fix/parser-docx, or docs/streamlit-deploy, then merge them back into main.
  • dev: no longer needed as a separate architecture branch. If it exists remotely from an earlier workflow, treat it as historical or delete it after confirming all useful work has been merged into main.

Keeping everything on main makes the MVP easy to deploy on Streamlit Community Cloud while still preserving the SaaS-ready backend and frontend code in the same repository.

Repository Layout

app.py               Root Streamlit entrypoint for local/cloud deployment
apps/streamlit-app   Streamlit MVP UI
apps/api             FastAPI backend
apps/web             Next.js 15 frontend
packages/ai-engine   Embedding and LLM provider interfaces
packages/resume-parser PDF/DOCX/text parsing and extraction
packages/scoring-engine Modular scoring strategies
packages/shared-types Shared dataclasses
datasets             Sample resumes and job descriptions
docs                 Architecture, API examples, ADR notes
infra                Docker, nginx, scripts
tests                Python unit/API tests

Quick Start

cp .env.example .env
python -m pip install -e .[api,dev]
streamlit run app.py

Or run the Dockerized stack:

docker compose up --build

Services:

Streamlit Community Cloud Deployment

Use Streamlit Community Cloud to test the end-to-end MVP workflow quickly. The hosted app only needs the root Streamlit entrypoint plus the Python packages used by the MVP; the FastAPI, Next.js, Docker, PostgreSQL, and Redis services stay in the repo for SaaS development but are not required for the Community Cloud app.

What to upload

Upload/push the whole monorepo to GitHub on the main branch. Streamlit Community Cloud will read the files it needs from the repository:

app.py                         Required root entrypoint selected in Streamlit Cloud
requirements.txt               Required Python dependency list
apps/streamlit-app/app.py      Required Streamlit MVP implementation
packages/shared-types/         Required shared screening dataclasses
packages/resume-parser/        Required resume parsing package
packages/ai-engine/            Required embedding/scoring provider package
packages/scoring-engine/       Required scoring package
datasets/sample_resumes/       Optional sample resumes for demos
datasets/sample_jobs/          Optional sample job descriptions for demos

Do not upload only apps/streamlit-app/app.py; the app imports code from the packages/ directories and uses the root app.py wrapper as the cloud-friendly entrypoint.

Deploy steps

  1. Push this repository to GitHub with the monorepo content on main.

  2. Open https://share.streamlit.io/ and choose Create app.

  3. Select the GitHub repository and branch: main.

  4. Set Main file path to:

    app.py

  5. Leave advanced settings empty for the default deterministic TF-IDF workflow. Add secrets only if you later wire in hosted LLM or embedding providers.

  6. Deploy the app.

  7. In the app, upload one or more resumes, paste a job description, tune the scoring weights, and click Run screening.

Easy workflow test

For a quick smoke test after deployment:

  1. Open datasets/sample_jobs/senior_ml_engineer.md in GitHub and copy its contents into the app's Job description box.
  2. Download the sample resume text files from datasets/sample_resumes/ and upload them in the app.
  3. Click Run screening.
  4. Review Screening Results, Candidate Deep Dive, Analytics, and the CSV/JSON export buttons.

If deployment fails while installing dependencies, first check the Streamlit build logs. The largest dependencies are sentence-transformers and spacy; the default app path uses TF-IDF, so those can be made optional later if Community Cloud resource limits become an issue.

MVP Workflow

  1. Upload multiple PDF/DOCX/TXT resumes.
  2. Paste or upload a job description.
  3. Adjust semantic, keyword, skills, and experience weights.
  4. Review ranked candidates, explanations, extracted fields, analytics, and CSV/JSON exports.

SaaS Foundation

The backend includes normalized SQLAlchemy models for organizations, users, jobs, resumes, screenings, and screening results; JWT primitives; upload validation; CORS; metrics; and OpenAPI docs. The frontend includes dashboard, jobs, candidates, screening results, analytics, settings, signup, and login routes with Tailwind styling and client state foundations.

AI Pipeline

sequenceDiagram
  participant U as Recruiter
  participant A as API/UI
  participant P as Parser
  participant E as Embedding Provider
  participant S as Scoring Engine
  U->>A: Upload resumes + job description
  A->>P: Extract text and entities
  P->>E: Generate normalized vectors
  E->>S: Semantic similarity
  S->>A: Scores, missing skills, explanations
Loading

Default local embedding mode is TF-IDF for fast reproducible tests. Set EMBEDDING_PROVIDER=sentence-transformers to use sentence-transformers/all-MiniLM-L6-v2.

Testing

pytest -q
ruff check .
mypy packages apps/api