நமது algorithm கணிக்கும் மதிப்பினை ஒரு API-ஆக expose செய்வதற்கு Flask பயன்படுகிறது. இதற்கான நிரல் பின்வருமாறு.
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| import os | |
| import json | |
| import pandas as pd | |
| import numpy | |
| from flask import Flask, render_template, request, jsonify | |
| from pandas.io.json import json_normalize | |
| from sklearn.externals import joblib | |
| app = Flask(__name__) | |
| port = int(os.getenv('PORT', 5500)) | |
| @app.route('/') | |
| def home(): | |
| return render_template('index.html') | |
| @app.route('/api/salepricemodel', methods=['POST']) | |
| def salepricemodel(): | |
| if request.method == 'POST': | |
| try: | |
| post_data = request.get_json() | |
| json_data = json.dumps(post_data) | |
| s = pd.read_json(json_data) | |
| p = joblib.load("./salepricemodel.pkl") | |
| r = p.predict(s) | |
| return str(r) | |
| except Exception as e: | |
| return (e) | |
| if __name__ == '__main__': | |
| app.run(host='0.0.0.0', port=port, debug=True) |
நிரலுக்கான வெளியீடு:
* Serving Flask app “flask_api” (lazy loading)
* Environment: production
WARNING: Do not use the development server in a production environment.
Use a production WSGI server instead.
* Debug mode: on
* Restarting with stat
* Debugger is active!
* Debugger PIN: 690-746-333
* Running on 0.0.0.0:5500/ (Press CTRL+C to quit)
இதனை postman எனும் கருவி மூலம் நாம் சோதித்துக் கொள்ளலாம்.
