Iris Flowers Classification

A complete machine learning system for classifying iris flowers using a Multi-Layer Perceptron (MLP) neural network implemented with PyTorch. The project includes a trained model, a FastAPI backend, and a Streamlit frontend—all deployed and accessible online.

✨ Features

• Machine Learning Model: MLP classifier trained on the classic Iris dataset
• RESTful API: FastAPI backend with comprehensive endpoints
• Web Interface: Interactive Streamlit UI for real-time predictions
• Cloud Deployment: Fully deployed on modern platforms:
  • Model hosted on Hugging Face
  • API deployed on Render
  • UI deployed on Streamlit Cloud

🚀 Live Demos

• Web Interface: Streamlit App
• Model Repository: Hugging Face Model

🛠️ Tech Stack

• Machine Learning: PyTorch, Scikit-learn, Pandas, NumPy
• Backend: FastAPI, Uvicorn, Pydantic
• Frontend: Streamlit
• Deployment: Hugging Face Hub, Render, Streamlit Cloud
• Environment Management: Pipenv

📦 Getting Started

Prerequisites

• Python 3.8 or higher
• Pipenv (recommended) or pip

Installation

1. Clone the repository

   git clone https://github.com/Jospin6/iris-flowers.git
   cd iris-flowers
   

2. Install dependencies with Pipenv

   pip install -r requirements.txt
   

Running Locally

1. Start the API server

   cd api
   uvicorn main:app --reload --host 0.0.0.0 --port 8000
   

2. Start the Streamlit app

   cd frontend
   streamlit run app.py
   
   

👨‍💻 Author

Built with ❤️ by Jospin Ndagano