note_flasgger.py

from flask import Flask,request
import pandas as pd
import numpy as np
import pickle
import flasgger
from flasgger import Swagger


app=Flask(__name__) #the point we need to start the application
Swagger(app)

pickle_in = open('classifier.pkl','rb') #read-only in birany format
classifier = pickle.load(pickle_in)

@app.route('/') #route path
def welcome():
    return "Welcome All"

@app.route('/predict',methods=['Get']) #default get methods
def predit_note_authentication():
    
    """Let's Authenticate the Banks Note 
    This is using docstrings for specifications.
    ---
    parameters:  
      - name: variance
        in: query
        type: number
        required: true
      - name: skewness
        in: query
        type: number
        required: true
      - name: curtosis
        in: query
        type: number
        required: true
      - name: entropy
        in: query
        type: number
        required: true
    responses:
        200:
            description: The output values
        
    """
    
    variance=request.args.get('variance')
    skewness=request.args.get('skewness')
    curtosis=request.args.get('curtosis')
    entropy=request.args.get('entropy')
    prediction = classifier.predict([[variance,skewness,curtosis,entropy]])
    return "The predicted value is"+ str(prediction)


@app.route('/predict_file',methods=["POST"])
def predit_note_file():
    """Let's Authenticate the Banks Note 
    This is using docstrings for specifications.
    ---
    parameters:
      - name: file
        in: formData
        type: file
        required: true
      
    responses:
        200:
            description: The output values
        
    """
    
    df_test = pd.read_csv(request.files.get("file"))
    print(df_test.head())
    prediction = classifier.predict(df_test)
    
    return  str(list(prediction))


if __name__ == '__main__':
    app.run()