-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathapp.py
70 lines (56 loc) · 2.11 KB
/
app.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
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
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__)
@app.route("/", methods=["GET"])
def homePage():
return render_template("index.html")
@app.route("/train", methods=["GET"])
def training():
os.system("python main.py")
return "Training complete"
@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)