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app.py
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app.py
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from flask import Flask, render_template, request
import os
import numpy as np
import pandas as pd
from mlProject.pipeline.prediction import PredictionPipeline
app = Flask(__name__) # initializing a flask app
@app.route('/',methods=['GET']) # route to display the home page
def homePage():
return render_template("index.html")
@app.route('/train',methods=['GET']) # route to train the pipeline
def training():
os.system("python main.py")
return "Training Successful!"
@app.route('/predict',methods=['POST','GET']) # route to show the predictions in a web UI
def index():
if request.method == 'POST':
try:
# reading the inputs given by the user
fixed_acidity =float(request.form['fixed_acidity'])
volatile_acidity =float(request.form['volatile_acidity'])
citric_acid =float(request.form['citric_acid'])
residual_sugar =float(request.form['residual_sugar'])
chlorides =float(request.form['chlorides'])
free_sulfur_dioxide =float(request.form['free_sulfur_dioxide'])
total_sulfur_dioxide =float(request.form['total_sulfur_dioxide'])
density =float(request.form['density'])
pH =float(request.form['pH'])
sulphates =float(request.form['sulphates'])
alcohol =float(request.form['alcohol'])
data = [fixed_acidity,volatile_acidity,citric_acid,residual_sugar,chlorides,free_sulfur_dioxide,total_sulfur_dioxide,density,pH,sulphates,alcohol]
data = np.array(data).reshape(1, 11)
obj = PredictionPipeline()
predict = obj.predict(data)
return render_template('results.html', prediction = str(predict))
except Exception as e:
print('The Exception message is: ',e)
return 'something is wrong'
else:
return render_template('index.html')
if __name__ == "__main__":
# app.run(host="0.0.0.0", port = 8080, debug=True)
app.run(host="0.0.0.0", port = 8080)