Stage 4 - Hiring Prediction FastAPI App

This repository contains a FastAPI application that predicts hiring outcomes based on candidate data. The app uses a trained machine learning model to evaluate candidates and provide scores and notes.

API Endpoints

POST /score

Predicts hiring outcome for a candidate.

Headers:

• x-api-key: API key for authentication

Request Body:

{
  "name": "string",
  "interview_score": 100,
  "skill_score": 100,
  "personality_score": 100,
  "education_level": "string",
  "recruitment_strategy": "string",
  "experience_level": "string",
  "status": "string"
}

Response:

{
    "ai_score": 78,
    "ai_notes": "Skor dihitung dari Interview=80, Skill=75, Personality=70, Edu=Bachelor, Exp=Junior.",
    "proba": 0.78,
    "passed": true
}

Prerequisites

• Python 3.11+
• Docker (optional, for containerization)
• Required Python packages listed in requirements.txt

Docker Setup

The application can be containerized using Docker. The Dockerfile includes:

• Python 3.11 slim base image
• Non-root user setup for security
• Automatic dependency installation
• Application code and model artifact copying
• Exposed port 7860 (default for Hugging Face Spaces)

Building and Running with Docker

# Build the Docker image
docker build -t hiring-prediction-app .

# Run the container
docker run -p 7860:7860 hiring-prediction-app

The application will be accessible at http://localhost:7860

Docker Environment Variables

No additional environment variables are required. The application uses default configurations specified in the code.

Docker Security Notes

• Uses non-root user for enhanced security
• Minimal base image to reduce attack surface
• Proper file ownership with --chown

Hugging Face Spaces Configuration

The application is configured to run on Hugging Face Spaces

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Local Setup

To run the application locally, follow these steps:

# Clone the repository
git clone https://github.com/4Kings-Rakamin/Stage4_FastAPI_Deployment.git

cd Stage4_FastAPI_Deployment

# Create a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

# Install dependencies
pip install -r requirements.txt

# Run the FastAPI app
uvicorn main:app --reload --host

The application will be accessible at http://localhost:8000

Docs

API documentation is automatically generated by FastAPI and can be accessed at:

• Swagger UI: https://agunghari-temanhire-fix.hf.space/docs