Iris Machine Learning 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.

Installation
Usage
API Endpoints
Example Request and Response
Dependencies
License
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

Clone the repository:

git clone https://github.com/LykourgosS/iris-ml-model-microservice.git
cd iris-ml-model-microservice

Build the Docker image:

docker build -t iris-ml-model-microservice .

Run the Docker container:

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

Clone the repository:

git clone https://github.com/LykourgosS/iris-ml-model-microservice.git
cd iris-ml-model-microservice

Create a virtual environment (optional but recommended):

python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

Install the required dependencies:
```
pip install -r requirements.txt
```
Train the model and save it as LRClassifier.pkl:
```
python .\app\generate-model.py
```
Start the FastAPI server:
```
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:

{
    "sepal_length": <float>,
    "sepal_width": <float>,
    "petal_length": <float>,
    "petal_width": <float>
}

Response:

{
    "prediction": <string>,
    "probability": <float>
}

Example Request and Response

Here's an example of how to make a request to the /predict endpoint:

Request

Using curl:

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:

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:

{
  "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 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 file for details.

Attribution

This project is based on the following blog post:

How to deploy machine learning models as a microservice using fastapi by Ashutosh Tripathi

Special thanks to the author for their guide on deploying a machine learning model as a microservice.

LykourgosS/iris-ml-model-microservice