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# -*- 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 = """
<div style="background-color:tomato;padding:10px">
<h2 style="color:white;text-align:center;">Streamlit Bank Authenticator ML App </h2>
</div>
"""
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()
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