deepak-karkala · December 28, 2020 13:37
diff --git a/flask_webapp_airbnb_predict.py b/flask_webapp_airbnb_predict.py
 # FLASK Webapp for serving price Predictions for Airbnb listings 
 import os, sys
 sys.path.append(".")
 import webapp_predict_price

 import pickle
 from flask import Flask
 import flask
 import sklearn
 import joblib
 import pandas as pd

 # Load pre-trained machine learning model.
 BASE_PATH = "webapp_predict_price/"
 model = joblib.load(BASE_PATH + "best_model.pkl")


 def create_app(test_config=None):
    # create and configure the app
    app = Flask(__name__, instance_relative_config=True)
    app.config.from_mapping(
        SECRET_KEY='dev',
        #DATABASE=os.path.join(app.instance_path, 'flaskr.sqlite'),
    )

    if test_config is None:
        # load the instance config, if it exists, when not testing
        app.config.from_pyfile('config.py', silent=True)
    else:
        # load the test config if passed in
        app.config.from_mapping(test_config)

    # ensure the instance folder exists
    try:
        os.makedirs(app.instance_path)
    except OSError:
        pass

    # Landing page
    @app.route('/', methods=['GET', 'POST'])
    def hello():
        # return 'Hello, World!'
        
        # Return landing page
        if flask.request.method == 'GET':
            return(flask.render_template('base.html'))

        # Return prediction output
        if flask.request.method == 'POST':

            # Create input to Model from form data
            df_input = pd.DataFrame([[country, city, neighbourhood, propertytype, roomtype, bedtype,
                                    cancellationpolicy, hostresponsetime, accommodates, num_bedrooms, num_beds,
                                    min_nights, availability_30, availability_60, availability_90, availability_365,
                                    num_reviews, reviews_per_month, review_scores_rating, review_scores_accuracy, 
                                    review_scores_cleanliness, review_scores_checkin, review_scores_communication,
                                    review_scores_location, review_scores_value, host_response_rate,
                                    ]], dtype=float)

            # Inference: Get prediction from Model
            prediction_price = model.predict(df_input)[0]
            prediction_price = round(prediction_price)

            return(flask.render_template('base.html', result=prediction_price))

    return app


 # if this is the main thread of execution first load the model and
 # then start the server
 if __name__ == "__main__":
    print(("* Loading Scikit-learn model and Flask starting server..."
        "please wait until server has fully started"))
    app = create_app()
    app.run(host='0.0.0.0', port=5000)
	# FLASK Webapp for serving price Predictions for Airbnb listings
	import os, sys
	sys.path.append(".")
	import webapp_predict_price

	import pickle
	from flask import Flask
	import flask
	import sklearn
	import joblib
	import pandas as pd

	# Load pre-trained machine learning model.
	BASE_PATH = "webapp_predict_price/"
	model = joblib.load(BASE_PATH + "best_model.pkl")


	def create_app(test_config=None):
	# create and configure the app
	app = Flask(__name__, instance_relative_config=True)
	app.config.from_mapping(
	SECRET_KEY='dev',
	#DATABASE=os.path.join(app.instance_path, 'flaskr.sqlite'),
	)

	if test_config is None:
	# load the instance config, if it exists, when not testing
	app.config.from_pyfile('config.py', silent=True)
	else:
	# load the test config if passed in
	app.config.from_mapping(test_config)

	# ensure the instance folder exists
	try:
	os.makedirs(app.instance_path)
	except OSError:
	pass

	# Landing page
	@app.route('/', methods=['GET', 'POST'])
	def hello():
	# return 'Hello, World!'

	# Return landing page
	if flask.request.method == 'GET':
	return(flask.render_template('base.html'))

	# Return prediction output
	if flask.request.method == 'POST':

	# Create input to Model from form data
	df_input = pd.DataFrame([[country, city, neighbourhood, propertytype, roomtype, bedtype,
	cancellationpolicy, hostresponsetime, accommodates, num_bedrooms, num_beds,
	min_nights, availability_30, availability_60, availability_90, availability_365,
	num_reviews, reviews_per_month, review_scores_rating, review_scores_accuracy,
	review_scores_cleanliness, review_scores_checkin, review_scores_communication,
	review_scores_location, review_scores_value, host_response_rate,
	]], dtype=float)

	# Inference: Get prediction from Model
	prediction_price = model.predict(df_input)[0]
	prediction_price = round(prediction_price)

	return(flask.render_template('base.html', result=prediction_price))

	return app


	# if this is the main thread of execution first load the model and
	# then start the server
	if __name__ == "__main__":
	print(("* Loading Scikit-learn model and Flask starting server..."
	"please wait until server has fully started"))
	app = create_app()
	app.run(host='0.0.0.0', port=5000)