https://github.com/jospin6/iris-flowers

A complete machine learning system for classifying iris flowers using a Multi-Layer Perceptron (MLP) neural network implemented with PyTorch.
https://github.com/jospin6/iris-flowers

fastapi huggingface pytorch streamlit

Last synced: about 2 months ago
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A complete machine learning system for classifying iris flowers using a Multi-Layer Perceptron (MLP) neural network implemented with PyTorch.

• Host: GitHub
• URL: https://github.com/jospin6/iris-flowers
• Owner: Jospin6
• Created: 2025-08-25T05:13:21.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2025-08-25T08:52:17.000Z (10 months ago)
• Last Synced: 2025-08-25T09:22:15.603Z (10 months ago)
• Topics: fastapi, huggingface, pytorch, streamlit
• Language: Python
• Homepage: https://iris-flowers-project.streamlit.app/
• Size: 211 KB
• Stars: 1
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Iris Flowers Classification

[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)

[![PyTorch](https://img.shields.io/badge/PyTorch-1.13+-ee4c2c.svg)](https://pytorch.org/)

[![FastAPI](https://img.shields.io/badge/FastAPI-0.100+-009688.svg)](https://fastapi.tiangolo.com/)

[![Streamlit](https://img.shields.io/badge/Streamlit-1.24+-ff4b4b.svg)](https://streamlit.io/)

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

[![Hugging Face](https://img.shields.io/badge/🤗-Hugging%20Face-yellow.svg)](https://huggingface.co/)

[![Render](https://img.shields.io/badge/Deployed-Render-5fddc6.svg)](https://render.com/)

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.

![screenshot](./screenshot1.png)

![screenshot](./screenshot2.png)

## ✨ 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](https://iris-flowers-project.streamlit.app/)

- **Model Repository**: [Hugging Face Model](https://huggingface.co/jospin6/iris-classification)

## 🛠️ 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**

   ```bash

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

   cd iris-flowers

2. **Install dependencies with Pipenv**

    ```bash

    pip install -r requirements.txt

### Running Locally

1. **Start the API server**

    ```bash

    cd api

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

2. **Start the Streamlit app**

    ```bash

    cd frontend

    streamlit run app.py

### 👨‍💻 Author

Built with ❤️ by Jospin Ndagano