PYTHON

fastapi

pip install fastapi
pip install uvicorn # ASGI server
pip install starlette # lightweight ASGI framework/toolkit
pip install pydantic # Data validation and type annotations
# OR
pip install fastapi uvicorn starlette pydantic

fastapi

from fastapi import FastAPI

app = FastAPI()


@app.get("/")
async def root():
    return {"message": "Hello World"}

How to create FastApi

from fastapi import FastAPI
import uvicorn
from sklearn.datasets import load_iris
from sklearn.naive_bayes import GaussianNB
from pydantic import BaseModel
 
# Creating FastAPI instance
app = FastAPI()
 
# Creating class to define the request body
# and the type hints of each attribute
class request_body(BaseModel):
    sepal_length : float
    sepal_width : float
    petal_length : float
    petal_width : float
 
# Loading Iris Dataset
iris = load_iris()
 
# Getting our Features and Targets
X = iris.data
Y = iris.target
 
# Creating and Fitting our Model
clf = GaussianNB()
clf.fit(X,Y)
 
# Creating an Endpoint to receive the data
# to make prediction on.
@app.post('/predict')
def predict(data : request_body):
    # Making the data in a form suitable for prediction
    test_data = [[
            data.sepal_length,
            data.sepal_width,
            data.petal_length,
            data.petal_width
    ]]
     
    # Predicting the Class
    class_idx = clf.predict(test_data)[0]
     
    # Return the Result
    return { 'class' : iris.target_names[class_idx]}

fastapi

Best framework

how to use fastapi

from typing import Union

from fastapi import FastAPI

app = FastAPI()


@app.get("/")
async def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
async def read_item(item_id: int, q: Union[str, None] = None):
    return {"item_id": item_id, "q": q}