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 pydanticfastapi
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 pydanticHow 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]}Copyright © 2021 Codeinu
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