First thing first, Activate your python environment
Let us assume that we have a “classifier.pkl” file
Create a main.py file
#main.py
import uvicorn
from fastapi import FastAPI
from BankNotes import BankNote
import numpy as np
import pickle
import pandas as pd
app = FastAPI()
pickle_in = open(“classifier.pkl”,”rb”)
classifier=pickle.load(pickle_in)
@app.get(‘/’)
def index():
return {‘message’: ‘Hello, World’}
@app.get(‘/{name}’)
def get_name(name: str):
return {‘Welcome’: f'{name}’}
@app.post(‘/predict’)
def predict_banknote(data:BankNote):
data = data.dict()
variance=data[‘variance’]
skewness=data[‘skewness’]
curtosis=data[‘curtosis’]
entropy=data[‘entropy’]
# print(classifier.predict([[variance,skewness,curtosis,entropy]]))
prediction = classifier.predict([[variance,skewness,curtosis,entropy]])
if(prediction[0]>0.5):
prediction=”Fake note”
else:
prediction=”Its a Bank note”
return {
‘prediction’: prediction
}
# Will run on http://127.0.0.1:8000
if __name__ == ‘__main__’:
uvicorn.run(app, host=’127.0.0.1′, port=8000)
Create a banknote.py file:
from pydantic import BaseModel
# Class which describes Bank Notes measurements
class BankNote(BaseModel):
variance: float
skewness: float
curtosis: float
entropy: float
Now, In the terminal run the application
>>uvicorn main:app –reload
# This will run on http://127.0.0.1:8000
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