File size: 656 Bytes
48b2dea
24882c7
48b2dea
 
24882c7
 
 
 
 
48b2dea
 
52478dc
48b2dea
24882c7
 
 
48b2dea
24882c7
48b2dea
 
52478dc
24882c7
 
 
 
48b2dea
24882c7
48b2dea
24882c7
48b2dea
 
24882c7
 
 
48b2dea
24882c7
48b2dea
24882c7
48b2dea
 
24882c7
 
 
 
 
48b2dea
 
24882c7
 
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
from flask import Flask, request, jsonify

import joblib

import pandas as pd




app = Flask(__name__)

model = joblib.load("tuned_xgboost_model.pkl")




@app.route('/')

def home():

    return "SuperKart Sales Forecast Modal Deployment API"




@app.route('/predict', methods=['POST'])

def predict():

    try:

        data = request.get_json()

        input_df = pd.DataFrame([data])

        prediction = model.predict(input_df)[0]

        return jsonify({'Predicted_Sales': round(float(prediction), 2)})

    except Exception as e:

        return jsonify({'error': str(e)})




if __name__ == '__main__':

    app.run(host='0.0.0.0', port=7860)