LovnishVerma/ONNX

🌸 Iris Flower Classifier (Flask + ONNX)

This project is a simple Flask web application that predicts the species of an Iris flower based on its measurements using a pre-trained Logistic Regression model. The model is exported in ONNX (Open Neural Network Exchange) format, allowing for efficient, framework-independent inference.

🚀 Model Training Colab Notebook

https://github.com/lovnishverma/Python-Getting-Started/blob/main/ONNXModelwithYourDataset.ipynb

🚀 Live Demo

📌 Enter sepal length, sepal width, petal length, and petal width 📌 Get predicted Iris species: Setosa, Versicolor, or Virginica

📁 Project Structure

.
├── app.py                  # Flask web app
├── iris_model.onnx         # ONNX-trained model
├── Dockerfile              # Dockerfile For HuggingFace   
├── class_labels.json       # Class label mapping
├── requirements.txt        # Required Python packages
└── templates/
    └── index.html          # HTML form for input

💡 Features

• —🧠 Trained with scikit-learn Logistic Regression
• —🔄 Converted to ONNX for optimized runtime inference
• —🌐 Deployed with Flask and HTML frontend
• —🪄 Clean input form with real-time result display

📦 Setup Instructions

1. 1.Clone the Repository

   git clone https://github.com/lovnishverma/iris-onnx-flask.git
   cd iris-onnx-flask

1. 1.Install Dependencies

   pip install -r requirements.txt

1. 1.Run the Flask App

   python app.py

1. 1.Open in Browser

   http://127.0.0.1:5000

🧪 Test Example

Try the following values:

Sepal Length: 5.7
Sepal Width: 3.2
Petal Length: 5.2
Petal Width: 1.9

Output:

Predicted Flower Type: virginica

🤖 Why ONNX instead of Pickle or Joblib?

✅ ONNX is preferred when deploying machine learning models in production or cross-platform environments.

📚 Requirements

flask
onnxruntime
numpy
gunicorn

📜 License

This project is licensed under the MIT License.

🙋‍♂️ Author

Made with ❤️ by Lovnish Verma