# -*- coding: utf-8 -*- """ Created on Sat Jun 13 02:20:31 2020 @author: Krish Naik """ # -*- coding: utf-8 -*- """ Created on Fri May 15 12:50:04 2020 @author: krish.naik """ import numpy as np import pickle import pandas as pd #from flasgger import Swagger import streamlit as st from PIL import Image #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(variance,skewness,curtosis,entropy): """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 """ prediction=classifier.predict([[variance,skewness,curtosis,entropy]]) print(prediction) return prediction def main(): st.title("Bank Authenticator") html_temp = """

Streamlit Bank Authenticator ML App

""" st.markdown(html_temp,unsafe_allow_html=True) variance = st.text_input("Variance","Type Here") skewness = st.text_input("skewness","Type Here") curtosis = st.text_input("curtosis","Type Here") entropy = st.text_input("entropy","Type Here") result="" if st.button("Predict"): result=predict_note_authentication(variance,skewness,curtosis,entropy) st.success('The output is {}'.format(result)) if st.button("About"): st.text("Lets LEarn") st.text("Built with Streamlit") if __name__=='__main__': main()