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BananaCheck - MLOps Learning Project

A modern ML project showcasing MLOps best practices with banana ripeness classification.

Live Demo: https://daystobananadeath.streamlit.app/

Project Goals

This project was built as a learning exercise to master key MLOps technologies:

  • Configuration Management with Hydra
  • Experiment Tracking & Model Versioning with Weights & Biases
  • API Development with FastAPI
  • Model Deployment with ONNX Runtime
  • Web Interface with Streamlit

Features

  • AI-powered banana ripeness estimation (days remaining before spoilage)
  • REST API for programmatic access
  • Web interface for easy image upload and prediction
  • Model versioning and artifact management via W&B
  • Production-ready deployment with Docker support

Tech Stack

Core ML/MLOps

  • PyTorch Lightning - Training framework
  • Hydra - Configuration management
  • Weights & Biases - Experiment tracking, model versioning
  • ONNX Runtime - Model inference optimization

API & Web

  • FastAPI - REST API with automatic documentation
  • Streamlit - Interactive web interface
  • Uvicorn - ASGI server

Data & Vision

  • Albumentations - Image augmentation pipeline
  • PIL/OpenCV - Image processing

Model Performance

  • Architecture: ResNet50 & Vision Transformer (ViT-B/16)
  • Dataset: 20K+ banana images across 5 ripeness stages
  • Classes: unripe, ripe, overripe, rotten, unknowns
  • Deployment: ONNX optimized for CPU inference

Quick Start

API Usage

# Start the API
uvicorn src.api:app --host 0.0.0.0 --port 8000

# Test prediction
curl -X POST "http://localhost:8000/predict" \
     -H "Content-Type: multipart/form-data" \
     -F "file=@banana.jpg"

Web Interface

# Start Streamlit app
streamlit run streamlit_app.py

# Navigate to http://localhost:8501

Environment Setup

pip install -r requirements.txt

# For W&B model loading
export WANDB_API_KEY=your_api_key

Project Structure

├── src/
│   ├── api.py              # FastAPI REST endpoint
│   ├── model.py            # Model architectures
│   ├── train.py            # Training pipeline
│   └── inference.py        # Inference utilities
├── configs/                # Hydra configuration files
├── streamlit_app.py        # Web interface
├── requirements.txt        # Python dependencies
└── Procfile               # Deployment configuration

Key Learning Outcomes

Hydra Configuration Management

  • Structured config files for different components (model, data, training)
  • Easy experiment switching with config overrides
  • Environment-specific configurations

W&B MLOps Pipeline

  • Automated experiment tracking with metrics logging
  • Model artifact versioning and storage
  • Seamless model deployment from artifacts
  • Training curve visualization and comparison

Production API Design

  • RESTful API with proper error handling
  • Automatic model loading from W&B artifacts
  • Health checks and monitoring endpoints
  • ONNX optimization for fast inference

Dataset Sources

Built using established vision datasets:

Deployment

The project includes production-ready deployment configurations:

  • Heroku/Railway: via Procfile
  • Docker: containerized deployment
  • Environment variables: for secure API key management

Built to explore modern MLOps practices and deployment strategies