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How to create rest api with fastapi

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Zeki Akçakaya answered on April 4, 2022 Popularity 4/10 Helpfulness 1/10

answer How to create rest api with fastapi

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How to create rest api with fastapi

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$ mkdir ~/restful_demo

Popularity 4/10 Helpfulness 1/10 Language whatever

Source: docs.vultr.com

Tags: api fastapi rest whatever

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Contributed on Jul 10 2024

Zeki Akçakaya

0 Answers Avg Quality 2/10

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fastapi json request

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from fastapi import FastAPI, Request
my_app = FastAPI()

@my_app.post("/getInformation")
async def getInformation(info : Request):
    req_info = await info.json()
    return {
        "status" : "SUCCESS",
        "data" : req_info
    }

Popularity 10/10 Helpfulness 5/10 Language python

Source: codehandbook.org

Tags: fastapi

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Contributed on May 16 2022

dhupee

0 Answers Avg Quality 2/10

how to use fastapi

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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}

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Source: fastapi.tiangolo.com

Tags: fastapi

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Contributed on Nov 18 2022

Auraqule

0 Answers Avg Quality 2/10

How to create FastApi

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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]}

Popularity 10/10 Helpfulness 2/10 Language python

Source: www.geeksforgeeks.org

Tags: fastapi

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Contributed on Apr 04 2022

Hurt Horse

0 Answers Avg Quality 2/10

How to create rest api with fastapi

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