Skip to content

Commit

Permalink
Merge pull request #17 from Dixtawi/main
Browse files Browse the repository at this point in the history
main dans CI
  • Loading branch information
Tenefix authored Jul 5, 2024
2 parents 951886e + ddf8440 commit fda5b92
Show file tree
Hide file tree
Showing 4 changed files with 393 additions and 24 deletions.
23 changes: 0 additions & 23 deletions .github/workflows/ci.yml
Original file line number Diff line number Diff line change
@@ -1,23 +0,0 @@
name: Commit Message Check

on: [push]

jobs:
commit-message-check:
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v2

- name: Make script executable
run: chmod +x check-commit-msg.sh

- name: Run commit message check
run: |
# Get the list of commits
commits=$(git log --format=%H ${{ github.event.before }}..${{ github.sha }})
# Check each commit message
for commit in $commits; do
git show -s --format=%B $commit | ./check-commit-msg.sh /dev/stdin
done
8 changes: 7 additions & 1 deletion README.md
Original file line number Diff line number Diff line change
@@ -1,5 +1,11 @@
# PFEE : CV Relevance Prediction


## Introduction
In the current recruitment context, companies receive a large number of applications for each job opening. Manually evaluating these resumes is a time-consuming task and prone to biases. Predicting the relevance of resumes to job postings using machine learning can greatly improve the efficiency and fairness of the recruitment process.

However, the use of sensitive personal data in machine learning models raises concerns regarding data privacy and security. This is where our project comes in. We have explored advanced techniques such as Privacy-Preserving Machine Learning (PPML) and Fully Homomorphic Encryption (FHE) to develop a model capable of predicting the relevance of resumes while ensuring the confidentiality of candidate data.

## About This Project
### Project Overview
As part of our engineering school curriculum, we undertook a capstone project (PFEE - Projet de Fin d'Études Encadré) in collaboration with Zama, a pioneering company in the field of homomorphic encryption. Our primary objective was to delve into the functionalities of Privacy-Preserving Machine Learning (PPML), Fully Homomorphic Encryption (FHE), and Concrete ML.
Expand Down Expand Up @@ -51,4 +57,4 @@ pip install -r requirements.txt
<a href="https://github.com/zama-ai/concrete-ml/releases"><img src="https://img.shields.io/github/v/release/zama-ai/concrete-ml?style=flat-square"></a>
<a href="LICENSE"><img src="https://img.shields.io/badge/License-BSD--3--Clause--Clear-%23ffb243?style=flat-square"></a>
<a href="https://github.com/zama-ai/bounty-program"><img src="https://img.shields.io/badge/Contribute-Zama%20Bounty%20Program-%23ffd208?style=flat-square"></a>
</p>
</p>
12 changes: 12 additions & 0 deletions ci/commit-msg
Original file line number Diff line number Diff line change
@@ -0,0 +1,12 @@
#!/bin/sh

# Pattern pour valider le message de commit
COMMIT_MSG_REGEX="^:?(feat|fix|docs|style|refactor|test|chore): ?.+"

commit_message=$(cat "$1")

if ! echo "$commit_message" | grep -qE "$COMMIT_MSG_REGEX"; then
echo "Error: The commit message does not follow conventions."
echo "The message must begin with one of the following keywords: feat, fix, docs, style, refactor, test, chore."
exit 1
fi
374 changes: 374 additions & 0 deletions src/main.ipynb

Large diffs are not rendered by default.

0 comments on commit fda5b92

Please sign in to comment.