https://github.com/presizhai/iris-predictor-fastapi
A web application for predicting the species of Iris flowers using a machine learning model trained with the Iris dataset, with FastAPI, a modern web framework for building APIs.
https://github.com/presizhai/iris-predictor-fastapi
essemblelearning fastapi python random-forest-classifier scikit-learn uvicorn
Last synced: 6 months ago
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A web application for predicting the species of Iris flowers using a machine learning model trained with the Iris dataset, with FastAPI, a modern web framework for building APIs.
- Host: GitHub
- URL: https://github.com/presizhai/iris-predictor-fastapi
- Owner: presiZHai
- Created: 2024-09-13T07:35:17.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-09-13T08:29:47.000Z (almost 2 years ago)
- Last Synced: 2025-03-12T14:48:06.490Z (over 1 year ago)
- Topics: essemblelearning, fastapi, python, random-forest-classifier, scikit-learn, uvicorn
- Language: Python
- Homepage:
- Size: 606 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Iris Flower Prediction App with FastAPI
This project is a web application for predicting the species of Iris flowers using a machine learning model trained with the Iris dataset. The app is built using FastAPI, a modern web framework for building APIs with Python 3.7+.
## Features
- **Iris Flower Species Prediction**: The app predicts the species of an Iris flower based on its sepal and petal dimensions.
- **Interactive Web Interface**: Users can input flower dimensions via a web form.
- **REST API Endpoint**: The application also provides an API endpoint for programmatic predictions.
## Technology Stack
- **Python**: The programming language used for building the application.
- **FastAPI**: A modern, fast (high-performance), web framework for building APIs with Python.
- **Scikit-Learn**: A popular machine learning library in Python used for training the classification model.
- **Jinja2**: A templating engine for Python, used for rendering HTML templates.
## How to Run the Application
### Prerequisites
- Python 3.7 or higher
- `pip` (Python package installer)
- Virtual environment (recommended)
### Setup Instructions
1. **Clone the repository:**
```bash
git clone https://github.com/presiZHai/iris-predictor-fastapi.git
cd iris-predictor-fastapi
2. **Create and activate a virtual environment**
```bash
conda create --name iris-predictor pythn=3.10 -y
conda activate iris-predictor
```
3. **Install the required dependencies**
```bash
pip install -r requirements.txt
```
4. **Train the model**
Run the following command to train the model: ```python train_model.py```
This will create a file named iris_model.pkl containing the trained model.
5. **Run the application**
You can start the FastAPI application by running: ```python app.py```
The application will be available at http://127.0.0.1:8000.
## API Usage
Endpoint
* POST /predict: Predicts the species of an Iris flower.
Request Body
{
"sepal_length": 5.1,
"sepal_width": 3.5,
"petal_length": 1.4,
"petal_width": 0.2
}
Response
{
"predicted_class": 0,
"predicted_class_name": "setosa"
}
## HTML Templates
* index.html: The main page where users can input flower dimensions for prediction.
* result.html: Displays the prediction result after submitting the form.
## Contributing
* Contributions are welcome! Please feel free to submit a Pull Request.
## License
* This project is licensed under the MIT License. See the LICENSE file for details.
## Acknowledgments
* The Iris dataset is used in this project, which is a popular dataset for classification and machine learning tasks.
* Thanks to the creators of FastAPI and Scikit-Learn for their excellent tools.