https://github.com/lykourgoss/iris-ml-model-microservice
A FastAPI microservice for predicting iris species based on sepal and petal dimensions. This project leverages machine learning to classify iris flowers into their respective species. (mpsp)
https://github.com/lykourgoss/iris-ml-model-microservice
docker fastapi iris-classification machine-learning-model microservice
Last synced: 5 months ago
JSON representation
A FastAPI microservice for predicting iris species based on sepal and petal dimensions. This project leverages machine learning to classify iris flowers into their respective species. (mpsp)
- Host: GitHub
- URL: https://github.com/lykourgoss/iris-ml-model-microservice
- Owner: LykourgosS
- License: gpl-3.0
- Created: 2024-10-17T17:34:09.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-10-23T11:44:33.000Z (over 1 year ago)
- Last Synced: 2025-10-12T08:07:30.359Z (5 months ago)
- Topics: docker, fastapi, iris-classification, machine-learning-model, microservice
- Language: Python
- Homepage:
- Size: 739 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Iris Machine Learning Model Microservice
[](https://conventionalcommits.org)
A FastAPI microservice for predicting iris species based on sepal and petal dimensions. This project leverages machine learning to classify iris flowers into their respective species.
## Table of Contents
- [Installation](#installation)
- [Usage](#usage)
- [API Endpoints](#api-endpoints)
- [Example Request and Response](#example-request-and-response)
- [Dependencies](#dependencies)
- [License](#license)
- [Attribution](#attribution)
## Installation
To set up the project locally, you can choose between two methods: using Docker or a Python virtual environment.
### Method 1: Using Docker
1. Clone the repository:
```bash
git clone https://github.com/LykourgosS/iris-ml-model-microservice.git
cd iris-ml-model-microservice
```
2. Build the Docker image:
```bash
docker build -t iris-ml-model-microservice .
```
3. Run the Docker container:
```bash
docker run -d -p 8000:8000 iris-ml-model-microservice
```
This will start the server at `http://localhost:8000`. You can then access the API documentation at `http://localhost:8000/docs`.
### Method 2: Using Virtual Environment
1. Clone the repository:
```bash
git clone https://github.com/LykourgosS/iris-ml-model-microservice.git
cd iris-ml-model-microservice
```
2. Create a virtual environment (optional but recommended):
```bash
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
3. Install the required dependencies:
```bash
pip install -r requirements.txt
```
4. Train the model and save it as LRClassifier.pkl:
```bash
python .\app\generate-model.py
```
5. Start the FastAPI server:
```bash
uvicorn app.main:app --reload
```
This will also start the server at `http://localhost:8000`, and you can access the API documentation at `http://localhost:8000/docs`.
## Usage
Once the server is running (via Docker or virtual environment), you can interact with the API at `http://localhost:8000`, as mentioned above.
## API Endpoints
### Predict Iris Species
- **Endpoint:** `/predict`
- **Method:** `POST`
- **Request Body:**
```json
{
"sepal_length": ,
"sepal_width": ,
"petal_length": ,
"petal_width":
}
```
- **Response:**
```json
{
"prediction": ,
"probability":
}
```
## Example Request and Response
Here's an example of how to make a request to the `/predict` endpoint:
### Request
- **Using** `curl`:
```bash
curl -X POST "http://localhost:8000/predict" -H "Content-Type: application/json" -d '{
"sepal_length": 5.1,
"sepal_width": 3.5,
"petal_length": 1.4,
"petal_width": 0.2
}'
```
- **Using** `python`:
```python
import requests
new_measurement = {
"sepal_length": 5.1,
"sepal_width": 3.5,
"petal_length": 1.4,
"petal_width": 0.2
}
response = requests.post('http://127.0.0.1:8000/predict', json=new_measurement)
print(response.content)
```
### Response
And here is the response:
```json
{
"prediction": "Iris-setosa",
"probability": 0.9793734738197287
}
```
## Dependencies
This project requires the following Python packages:
- pandas
- numpy
- pickle
- scikit-learn
- FastAPI
- Uvicorn
You can find the complete list of dependencies with their versions in the [requirements.txt](/requirements.txt) file.
> [!NOTE]
> Pickle module is a Python built-in module, meaning there is no need to install it separately.
## License
This project is licensed under the GNU General Public License v3.0 - see the [LICENSE](LICENSE) file for details.
## Attribution
This project is based on the following blog post:
- [How to deploy machine learning models as a microservice using fastapi](https://ashutoshtripathi.com/2021/02/15/how-to-deploy-machine-learning-models-as-a-microservice-using-fastapi/) by [Ashutosh Tripathi](https://ashutoshtripathi.com/author/ashutoshtripathi19/)
Special thanks to the author for their guide on deploying a machine learning model as a microservice.