Flask Deplyment of ML Models

 

	from flask import Flask, request import numpy as np
	import pickle
	import pandas as pd
	import flasgger
	from flasgger import Swagger
	app=Flask(__name__)
	Swagger(app)
	pickle_in = open("classifier.pkl","rb")
	classifier=pickle.load(pickle_in)
	@app.route('/')
	def welcome():
	return "Welcome All"
	@app.route('/predict',methods=["Get"])
	def predict_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]])
	print(prediction)
	return "Hello The answer is"+str(prediction)
	@app.route('/predict_file',methods=["POST"])
	def predict_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(host='0.0.0.0',port=8000)