junjiewu0/iris-inference-api

By junjiewu0

•Updated over 1 year ago

FastAPI service for iris flower species inference using scikit-learn's RandomForestClassifier.

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junjiewu0/iris-inference-api repository overview

⁠Iris Inference API 🪻

A FastAPI⁠ service for predicting iris flower species using scikit-learn⁠'s RandomForestClassifier⁠.

The model is trained on the classic Iris flower dataset⁠. By providing flower measurements (sepal and petal dimensions) as input, the API returns the most likely species classification with probability score.

Available as a Docker image⁠.

Source code: https://github.com/junjie-w/ml-iris-inference-fastapi⁠

⁠🪻 API Reference

Endpoint	Method	Description
`/`	GET	API info and available endpoints
`/model/info`	GET	Classifier details
`/predict`	POST	Single iris flower prediction
`/predict/batch`	POST	Batch iris flower predictions

Try all API endpoints with the included script:

./try_api.sh

Example API Requests

⁠Root Endpoint

curl http://localhost:8000/

⁠Model Info Endpoint

curl http://localhost:8000/model/info

⁠Single Prediction

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
  }'

⁠Batch Prediction

curl -X POST http://localhost:8000/predict/batch \
  -H "Content-Type: application/json" \
  -d '{
    "samples": [
      {
        "sepal_length": 5.1,
        "sepal_width": 3.5,
        "petal_length": 1.4,
        "petal_width": 0.2
      },
      {
        "sepal_length": 6.2,
        "sepal_width": 2.9,
        "petal_length": 4.3,
        "petal_width": 1.3
      }
    ]
  }'

⁠🪻 Development Setup

# Clone repo
git clone https://github.com/junjie-w/ml-iris-inference-fastapi.git
cd ml-iris-inference-fastapi

# Install dependencies
pip install -r requirements.txt

# Train model (creates iris_model.pkl)
python model_training.py

# Run the API
python run.py

API base URL: http://localhost:8000⁠
Interactive OpenAPI documentation: http://localhost:8000/docs⁠
OpenAPI specification (JSON): http://localhost:8000/openapi.json⁠

⁠🪻 Docker Usage

⁠Pre-built Image from Docker Hub

# Pull image from Docker Hub
docker pull junjiewu0/iris-inference-api

# For ARM-based machines (Apple Silicon, etc.)
docker pull --platform linux/amd64 junjiewu0/iris-inference-api

# Run container
docker run -p 8000:8000 junjiewu0/iris-inference-api

# For ARM-based machines (Apple Silicon, etc.)
docker run --platform linux/amd64 -p 8000:8000 junjiewu0/iris-inference-api

⁠Build Image Locally

# Build image
docker build -t iris-inference-api .

# Run container
docker run -p 8000:8000 iris-inference-api

⁠🪻 Run Tests

# Run the test suite
pytest

# For test coverage
pytest --cov=app tests/

⁠🪻 Makefile Commands

make run                     # Start the API server
make dev                     # Start the server with auto-reload
make test                    # Run tests
make coverage                # Run tests with coverage report
make train                   # Train the model (creates iris_model.pkl)
make docker-build            # Build the Docker image
make docker-run              # Run container from local image
make docker-pull-remote      # Pull pre-built image from Docker Hub
make docker-run-remote       # Run container from pre-built Docker Hub image

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132.8 MB

Last updated

over 1 year ago

docker pull junjiewu0/iris-inference-api