-
Notifications
You must be signed in to change notification settings - Fork 0
/
application.py
52 lines (39 loc) · 1.52 KB
/
application.py
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
import os
import sys
import pandas as pd
from flask import Flask,request,render_template
from sklearn.preprocessing import StandardScaler
from src.exception import CustomException
from src.pipeline.predict_pipeline import CustomData,Predictpipeline
application = Flask(__name__)
app = application
@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=CustomData(
gender=request.form.get('gender'),
race_ethnicity=request.form.get('ethnicity'),
parental_level_of_education=request.form.get('parental_level_of_education'),
lunch=request.form.get('lunch'),
test_preparation_course=request.form.get('test_preparation_course'),
reading_score=float(request.form.get('writing_score')),
writing_score=float(request.form.get('reading_score'))
)
logging.info("Prediction Started")
pred_df = data.get_data_as_data_frame()
print(pred_df)
print("Before Prediction")
logging.info("Mid Way Prediction Started")
predict_pipeline = Predictpipeline()
print("Mid Prediction")
logging.info("Prediction Completed")
results=predict_pipeline.predict(pred_df)
print("after Prediction")
return render_template('home.html',results=results[0])
if __name__=="__main__":
app.run(host="0.0.0.0")