Welcome to AI Fellowship 2025 Session

Welcome to the AI Fellowship 2025! This project demonstrates a minimal FastAPI application for classifying Iris flower species. The app follows the 12-Factor App methodology and is ideal for learning how to deploy scalable, maintainable machine learning APIs.

Python Ecosystem: Building a Twelve-Factor App

Demo Application

Click here to access the Iris Classification FastAPI App

This link opens the live API with Swagger documentation, where you can test the /predict and /predictions endpoint.

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Project Overview

This is a FastAPI-based microservice for predicting Iris flower species based on the sepal and petal measurements.

Local Setup

# Cloning the repository
git clone https://github.com/fuseai-fellowship/12factorAppML.git
cd 12factorAppML

# Creating and Activating Virtual Environment
python -m venv env
source env/bin/activate

# Installing dependencies
pip install -r requirements.txt

# Running docker-compose for building the application
docker compose up --build

#.env
# Add this to the .env file
DATABASE_URL = sqlite:///./data/iris.db
LOG_LEVEL = DEBUG
ENVIRONMENT = development # Staging and Production
MODEL_PATH = ./models/iris_dtree.joblib
PORT = 9000
HOST = 0.0.0.0