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CAN YOU READ THIS
June-29-2025 Java
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CAN YOU READ THIS
June-29-2025 Java
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CAN YOU READ THIS
June-29-2025 Java
0
CAN YOU READ THIS
June-29-2025 Java
0
CAN YOU READ THIS
June-29-2025 Java
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29/6/2025 3:38
June-29-2025 Web
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29/6/2025 3:38
June-29-2025 Web
0
29/6/2025 3:38
June-29-2025 Web
0
29/6/2025 3:38
June-29-2025 Web
0
29/6/2025 3:38
June-29-2025 Web

Top answers

20
fast api
December-01-2020
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
1
invert
April-08-2021
#!/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
2
How to create FastApi
April-04-2022
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]}
20
fastapi
December-01-2020
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
-1
invert
April-08-2021
#!/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
9
run fastapi server
November-19-2020
uvicorn main:app --reload
-1
invert
April-08-2021
#!/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
20
install fastapi
December-01-2020
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
-1
installing fastapi
April-09-2022
pip install fastapi
-1
masking
April-08-2021
#!/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