Machine-Learning-Model-to-Production-Website-using-GKE/app.py at main · Shreyjain203/Machine-Learning-Model-to-Production-Website-using-GKE · GitHub

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
from flask import Flask,request,render_template
import numpy as np
import pandas as pd

from sklearn.preprocessing import StandardScaler

application=Flask(__name__)

app=application

## Route for a home page

@app.route('/')
def index():
    return render_template('index.html')

@app.route('/predictdata',methods=['GET','POST'])
def predict_datapoint():
    if request.method=='GET':
        return render_template('home.html')
    else:
        data=request.form.get('text')
        # pred_df=data.get_data_as_data_frame()

        # pred_df['filename'] = ""
        # pred_df['preprocessed_text'] = ""
        # pred_df['preprocessed_email'] = ""
        # pred_df['preprocessed_subject'] = ""

        # d = {0: 'alt.atheism', 1: 'comp.graphics', 2: 'comp.os.ms-windows.misc', 3: 'comp.sys.ibm.pc.hardware', 4: 'comp.sys.mac.hardware', 5: 'comp.windows.x', 6: 'misc.forsale', 7: 'rec.autos', 8: 'rec.motorcycles', 9: 'rec.sport.baseball', 10: 'rec.sport.hockey', 11: 'sci.crypt', 12: 'sci.electronics', 13: 'sci.med', 14: 'sci.space', 15: 'soc.religion.christian', 16: 'talk.politics.guns', 17: 'talk.politics.mideast', 18: 'talk.politics.misc', 19: 'talk.religion.misc'}

        # predict_pipeline=PredictPipeline()
        # results=predict_pipeline.predict(pred_df)
        results="Shrey tested this"
        return render_template('home.html',results=d[results])

if __name__=="__main__":
    app.run(host="0.0.0.0", port=80)