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
#!/bin/sh # Make an inverse binary image mask (switch the 1s and 0’s) # Create the inverse mask (Mark Jenkinson's approach. Very clever: # Multiply by negative 1, then add 1). Make sure you pass in a binary mask) fslmaths mask -mul -1 -add 1 -bin mask_inverse # OR use -binv flag fslmaths mask -binv mask_inverse
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]}
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
#!/bin/sh # Make an inverse binary image mask (switch the 1s and 0’s) # Create the inverse mask (Mark Jenkinson's approach. Very clever: # Multiply by negative 1, then add 1). Make sure you pass in a binary mask) fslmaths mask -mul -1 -add 1 -bin mask_inverse # OR use -binv flag fslmaths mask -binv mask_inverse
#!/bin/sh # Make an inverse binary image mask (switch the 1s and 0’s) # Create the inverse mask (Mark Jenkinson's approach. Very clever: # Multiply by negative 1, then add 1). Make sure you pass in a binary mask) fslmaths mask -mul -1 -add 1 -bin mask_inverse # OR use -binv flag fslmaths mask -binv mask_inverse
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
#!/bin/sh # Make an inverse binary image mask (switch the 1s and 0’s) # Create the inverse mask (Mark Jenkinson's approach. Very clever: # Multiply by negative 1, then add 1). Make sure you pass in a binary mask) fslmaths mask -mul -1 -add 1 -bin mask_inverse # OR use -binv flag fslmaths mask -binv mask_inverse
Questions
Answers
Answer accepted
Users
Copyright © 2021 Codeinu
Forgot your account's password or having trouble logging into your Account? Don't worry, we'll help you to get back your account. Enter your email address and we'll send you a recovery link to reset your password. If you are experiencing problems resetting your password contact us
