flask-ml 0.0.3

A Flask extension for running machine learning code

Flask-ML

Flask-ML helps users easily deploy their ML models as a web service. Flask-ML Server, similar to Flask, allows the user to specify web services using the decorator pattern. But Flask-ML allows users to specify the input type being expected by the ML function and provides helper classes to form response objects for the outputs produced by the ML function.

Installation

To install Flask-ML

pip install flask-ml

Usage examples

Server

from flask_ml.flask_ml_server import MLServer
from flask_ml.flask_ml_server.constants import DataTypes
from flask_ml.flask_ml_server.models import ResponseModel, TextResult


# Create a dummy ML model
class DummyModel:
    def predict(self, data: list) -> list:
        return list(range(len(data)))  # Return 0 to len(data) - 1


# create an instance of the model
model = DummyModel()

# Create a server
server = MLServer(__name__)


# Create an endpoint
@server.route("/dummymodel", DataTypes.TEXT)
def process_text(inputs: list, parameters: dict) -> dict:
    results = model.predict(inputs)
    results = [TextResult(text=e["text"], result=r) for e, r in zip(inputs, results)]
    response = ResponseModel(results=results)
    return response.get_response()


# Run the server (optional. You can also run the server using the command line)
server.run()

# Expected request json format:
# {
#     "inputs": [
#         {"text": "Text to be classified"},
#         {"text": "Another text to be classified"}
#     ],
#     "data_type": "TEXT",
#     "parameters": {}
# }

Client

from flask_ml.flask_ml_client import MLClient
from flask_ml.flask_ml_server.constants import DataTypes

url = "http://127.0.0.1:5000/dummymodel"  # The URL of the server
client = MLClient(url)  # Create an instance of the MLClient object

inputs = [
    {"text": "Text to be classified"},
    {"text": "Another text to be classified"},
]  # The inputs to be sent to the server
data_type = DataTypes.TEXT  # The type of the input data

response = client.request(inputs, data_type)  # Send a request to the server
print(response)  # Print the response