File size: 1,500 Bytes
b7b112a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | # pip install flask
from flask import Flask,render_template,request
import tensorflow as tf
import pickle
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import MinMaxScaler
# If you need to inverse transform, you can use scaler.inverse_transform(scaled_data)
# loading the label encoder
#le=pickle.load(open('label_encoder.pkl','rb'))
# loading my mlr model
model=pickle.load(open('modelrff.pkl','rb'))
#loading Scaler
scalar=pickle.load(open('scaler.pkl','rb'))
# Flask is used for creating your application
# render template is use for rendering the html page
app= Flask(__name__) # your application
@app.route('/') # default route
def home():
return render_template('home.html') # rendering if your home page.
@app.route('/pred',methods=['POST']) # prediction route
def predict1():
'''
For rendering results on HTML
'''
rd = request.form["Signal_Strength"]
ad= request.form["Latency"]
ms = request.form["Required_Bandwidth"]
s = request.form["type"]
p = request.form["Allocated_Bandwidth"]
t = np.array([[float(rd),float(ad),float(ms),float(s),float(p)] ])
x=scalar.transform(t)
output =model.predict(x)
return render_template("home.html", result = "The predicted Resource_Allocation is "+str(np.round(output[0])))
# running your application
if __name__ == "__main__":
app.run()
#http://localhost:5000/ or localhost:5000 |