SpringForge – Deployment-ready Code Quality ML Service

📌 Overview

This repository contains a deployment-ready machine learning model artifact developed as part of the SpringForge Code Quality Analysis initiative. The project provides a Python-based service that loads a pre-trained Architecture-Aware Anti-Pattern Classification Model and exposes it through a structured application layer.

The focus of this repository is model readiness for deployment, including clean code organization, model loading, schema definitions, and dependency management.

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🎯 Objectives

• Load and serve a pre-trained machine learning model
• Ensure clear separation between application logic and model artifacts
• Provide a clean, extensible structure suitable for deployment
• Maintain reproducibility and maintainability using Python best practices

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🧠 Model Description

• Model Type: Architecture-Aware Anti-Pattern Classification Model
• Format: Serialized using joblib
• Purpose: Detect and classify software architecture anti-patterns to support code quality analysis

The trained model is stored separately from the application logic to support scalability and easy updates.

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📁 Project Structure

SpringForge-CodeQuality-ML-Service/
│
├── app/
│   ├── main.py              # Application entry point
│   ├── model_loader.py      # Model loading logic
│   ├── schemas.py           # Data schemas and validation
│   ├── __init__.py
│
├── models/
│   └── architecture_aware_antipattern_model.joblib
│
├── requirements.txt         # Python dependencies
└── Dockerfile               # Present but not used in this setup

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⚙️ Technology Stack

• Programming Language: Python 3.x
• Libraries: NumPy, Pandas, Scikit-learn, Joblib
• Environment: Python Virtual Environment (venv)
• Version Control: Git & GitHub

⚠️ Note: Docker is not used in the current execution or deployment of this project.

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🚀 Setup Instructions (Without Docker)

1️⃣ Clone the Repository

git clone https://github.com/<your-username>/<repository-name>.git
cd <repository-name>

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2️⃣ Create a Virtual Environment

python -m venv venv

Activate it:

Windows

venv\Scripts\activate

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3️⃣ Install Dependencies

pip install -r requirements.txt

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4️⃣ Run the Application

python -m app.main

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✅ Deployment Readiness

This repository is considered deployment-ready because:

• The trained model is serialized and versioned
• Application logic is modular and clean
• Dependencies are explicitly defined
• Environment isolation is supported via virtual environments
• The project can be easily integrated into a larger deployment pipeline

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📌 Usage Context

This repository is intended for:

• Academic evaluation
• Demonstration of ML deployment readiness
• Integration into backend services
• Further containerization or cloud deployment (optional in future)

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🔮 Future Enhancements

• Add REST API layer (FastAPI)
• Enable Docker-based deployment
• Add CI/CD pipeline
• Add monitoring and logging
• Extend support for additional code quality metrics

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📄 License

This project is developed for academic and research purposes.