🌸 Iris Flower Prediction API

---

Overview

This is a Iris Flower Prediction application built with FastAPI and a trained machine learning model.
It allows users to input iris flower features (sepal length, sepal width, petal length, petal width) and predicts the species of the flower: Setosa, Versicolor, or Virginica.

The project includes:

• FastAPI backend with API endpoints
• Frontend HTML form for user interaction
• ML model integration using scikit-learn
• Deployment ready for Render or other cloud platforms

---

Features

• Predicts iris flower species based on user input
• Interactive web form for real-time predictions
• API endpoint for programmatic access
• Simple and modular project structure for easy extension

---

Project Structure

app/ │ ├─ app.py # Main FastAPI app ├─ config.py # Config variables ├─ requirements.txt # Python dependencies ├─ start.sh # Start command for Render │ ├─ model/ # ML model and prediction logic │ ├─ prediction.py │ └─ your_model.pkl # Saved ML model │ ├─ schemas/ # Pydantic schemas for input validation │ └─ userinput.py │ ├─ templates/ # HTML frontend │ └─ index.html ├─ static/ # CSS / JS / images │ ├─ style.css │ └─ script.js └─ .gitignore # Git ignore file

---

Installation

1. Clone the repository

git clone https://github.com/<your-username>/perfect-ml.git
cd perfect-ml
Create and activate a virtual environment

python -m venv myenv
# Windows
myenv\Scripts\activate
# Linux / Mac
source myenv/bin/activate


Install dependencies

pip install -r requirements.txt

Running Locally

Start the FastAPI server:

uvicorn app:app --reload


Open your browser and visit:

http://127.0.0.1:8000/


You can use the form to enter iris flower features and get predictions.

Deployment on Render

Push your project to GitHub.

Create a New Web Service on Render.

Set the following:

Environment: Python 3.12

Build Command: pip install -r requirements.txt

Start Command: ./start.sh
or
uvicorn app:app --host 0.0.0.0 --port $PORT

Deploy and visit your live URL.

Usage

Form Input Example:

Feature	Value
Sepal Length	5.1
Sepal Width	3.5
Petal Length	1.4
Petal Width	0.2

Prediction Output Example:

{
  "prediction": "setosa"
}

Author

Sahil Kumar

GitHub: https://github.com/pythoxb

LinkedIn: https://linkedin.com/in/pythoxb

License

This project is licensed under the MIT License.


---

If you want, I can also **add badges** for:  

- Live demo on Render  
- ML model accuracy  
- Python & FastAPI version  

…to make it look more **professional and portfolio-ready**.  

Do you want me to add those badges?