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app.py
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app.py
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from flask import Flask, request, render_template
from flask_cors import cross_origin
import sklearn
import pickle
import pandas as pd
import datetime
import os
from prediction_service import prediction
from sklearn.preprocessing import LabelEncoder
webapp_dir = "webapp"
static_dir = os.path.join(webapp_dir, "static")
templates_dir = os.path.join(webapp_dir, "templates")
app = Flask(__name__, static_folder=static_dir, template_folder=templates_dir)
# Loading pickle files
model = pickle.load(open("webapp/lgb_model_v2.pkl", "rb"))
channelGrouping_pkl = pickle.load(open('webapp/Column Pickle/channelGrouping.pkl', 'rb'))
device_browser_pkl = pickle.load(open('webapp/Column Pickle/device_browser.pkl', 'rb'))
device_deviceCategory_pkl = pickle.load(open("webapp/Column Pickle/device_deviceCategory.pkl", 'rb'))
device_isMobile_pkl = pickle.load(open('webapp/Column Pickle/device_isMobile.pkl', 'rb'))
device_operatingSystem_pkl = pickle.load(open('webapp/Column Pickle/device_operatingSystem.pkl', 'rb'))
geoNetwork_continent_pkl = pickle.load(open('webapp/Column Pickle/geoNetwork_continent.pkl', 'rb'))
geoNetwork_country_pkl = pickle.load(open('webapp/Column Pickle/geoNetwork_country.pkl', 'rb'))
geoNetwork_city_pkl = pickle.load(open('webapp/Column Pickle/geoNetwork_city.pkl', 'rb'))
geoNetwork_subContinent_pkl = pickle.load(open('webapp/Column Pickle/geoNetwork_subContinent.pkl', 'rb'))
geoNetwork_metro_pkl = pickle.load(open('webapp/Column Pickle/geoNetwork_metro.pkl', 'rb'))
geoNetwork_networkDomain_pkl = pickle.load(open('webapp/Column Pickle/geoNetwork_networkDomain.pkl', 'rb'))
geoNetwork_region_pkl = pickle.load(open('webapp/Column Pickle/geoNetwork_region.pkl', 'rb'))
trafficSource_adContent_pkl = pickle.load(open('webapp/Column Pickle/trafficSource_adContent.pkl', 'rb'))
trafficSource_adwordsClickInfo_page_pkl = pickle.load(open('webapp/Column Pickle/trafficSource_adwordsClickInfo_page.pkl', 'rb'))
trafficSource_adwordsClickInfo_gclId_pkl = pickle.load(open('webapp/Column Pickle/trafficSource_adwordsClickInfo_gclId.pkl', 'rb'))
trafficSource_adwordsClickInfo_slot_pkl = pickle.load(open('webapp/Column Pickle/trafficSource_adwordsClickInfo_slot.pkl', 'rb'))
trafficSource_campaign_pkl = pickle.load(open('webapp/Column Pickle/trafficSource_campaign.pkl', 'rb'))
trafficSource_isTrueDirect_pkl = pickle.load(open('webapp/Column Pickle/trafficSource_isTrueDirect.pkl', 'rb'))
trafficSource_keyword_pkl = pickle.load(open('webapp/Column Pickle/trafficSource_keyword.pkl', 'rb'))
trafficSource_medium_pkl = pickle.load(open('webapp/Column Pickle/trafficSource_medium.pkl', 'rb'))
trafficSource_referralPath_pkl = pickle.load(open('webapp/Column Pickle/trafficSource_referralPath.pkl', 'rb'))
trafficSource_source_pkl = pickle.load(open('webapp/Column Pickle/trafficSource_source.pkl', 'rb'))
@app.route("/", methods=['GET'])
@cross_origin()
def home():
return render_template("home.html")
@app.route("/batch", methods=['GET', 'POST'])
def index():
if request.method == 'POST':
try:
path = request.files['file']
path = path.filename
response = prediction.form_response(path)
result = "The Top Ten Predictions are "
return render_template("index1.html", response=[response], result=result)
except Exception as e:
error = {"error": e}
return render_template("404.html", error=error)
else:
return render_template('index1.html')
@app.route("/predict", methods=["POST"])
@cross_origin()
def predict():
# Extracting the weekday, month, year, visitHour
date = request.form["Date"]
month = pd.to_datetime(date, format="%Y-%m-%dT%H:%M").month
year = pd.to_datetime(date, format="%Y-%m-%dT%H:%M").year
#weekday = 1#pd.to_datetime(date, format="%Y-%m-%dT%H:%M").weekday
a = datetime.datetime.strptime(date,"%Y-%m-%dT%H:%M")
weekday= a.weekday()
visitHour = int(pd.to_datetime(date, format='%Y-%m-%dT%H:%M').hour)
# Extracting visitNumber
visitNumber = float(request.form["visitNumber"])
# Extracting totals.hits
totals_hits = float(request.form["hits"])
# Extracting totals.pageviews
totals_pageviews = int(request.form["pageviews"])
# Extracting totals.newVisits
totals_newVisits = int(request.form["newVisits"])
# Extracting channelGrouping
channelGrouping = str(request.form['channelGrouping'])
# Extracting browser
device_browser = str(request.form['browser'])
# Extracting operatingSystem
device_operatingSystem = str(request.form['operatingSystem'])
# Extracting isMobile
device_isMobile = str(request.form['isMobile'])
# Extracting deviceCategory
device_deviceCategory = str(request.form['deviceCategory'])
# Extracting continent
geoNetwork_continent = str(request.form['continent'])
# Extracting subContinent
geoNetwork_subContinent = str(request.form['subContinent'])
# Extracting country
geoNetwork_country = str(request.form['myCountry'])
# Extracting region
geoNetwork_region = str(request.form['myregion'])
# Extracting metro
geoNetwork_metro = str(request.form['mymetro'])
# Extracting city
geoNetwork_city = str(request.form['mycity'])
# Extracting network Domain
geoNetwork_networkDomain = str(request.form['mynetworkDomain'])
trafficSource_campaign = str(request.form['campaign'])
trafficSource_source = str(request.form['trafficsource'])
trafficSource_medium = str(request.form['medium'])
trafficSource_keyword = str(request.form['traffickeyword'])
trafficSource_isTrueDirect = str(request.form['isTrueDirect'])
trafficSource_referralPath = str(request.form['trafficreferral'])
trafficSource_adwordsClickInfo_page = str(request.form['adwordsClickInfopage'])
trafficSource_adwordsClickInfo_slot = str(request.form['adwordsClickInfoslot'])
trafficSource_adwordsClickInfo_gclId = str(request.form['trafficgclid'])
trafficSource_adContent = str(request.form['adContent'])
data_for_df = [{'channelGrouping': channelGrouping, 'visitNumber': visitNumber, 'device_browser': device_browser,
'device_operatingSystem': device_operatingSystem,
'device_isMobile': device_isMobile, 'device_deviceCategory': device_deviceCategory,
'geoNetwork_continent': geoNetwork_continent,
'geoNetwork_subContinent': geoNetwork_subContinent, 'geoNetwork_country': geoNetwork_country,
'geoNetwork_region': geoNetwork_region, 'geoNetwork_metro': geoNetwork_metro,
'geoNetwork_city': geoNetwork_city, 'geoNetwork_networkDomain': geoNetwork_networkDomain,
'totals_hits': totals_hits,
'totals_pageviews': totals_pageviews, 'totals_newVisits': totals_newVisits,
'trafficSource_campaign': trafficSource_campaign, 'trafficSource_source': trafficSource_source,
'trafficSource_medium': trafficSource_medium,
'trafficSource_keyword': trafficSource_keyword,
'trafficSource_isTrueDirect': trafficSource_isTrueDirect,
'trafficSource_referralPath': trafficSource_referralPath,
'trafficSource_adwordsClickInfo_page': trafficSource_adwordsClickInfo_page,
'trafficSource_adwordsClickInfo_slot': trafficSource_adwordsClickInfo_slot,
'trafficSource_adwordsClickInfo_gclId': trafficSource_adwordsClickInfo_gclId,
'trafficSource_adContent': trafficSource_adContent, 'weekday': weekday,
'month': month, 'year': year, 'visitHour': visitHour}]
df = pd.DataFrame(data_for_df)
df['channelGrouping'] = channelGrouping_pkl.transform(df['channelGrouping'])
df['device_browser']= device_browser_pkl.transform(df['device_browser'])
df['device_deviceCategory'] = device_deviceCategory_pkl.transform(df['device_deviceCategory'])
df['device_isMobile']= device_isMobile_pkl.transform(df['device_isMobile'])
df['device_operatingSystem'] = device_operatingSystem_pkl.transform(df['device_operatingSystem'])
df['geoNetwork_city'] = geoNetwork_city_pkl.transform(df['geoNetwork_city'])
df['geoNetwork_continent'] = geoNetwork_continent_pkl.transform(df['geoNetwork_continent'])
df['geoNetwork_country'] = geoNetwork_country_pkl.transform(df['geoNetwork_country'])
df['geoNetwork_metro'] = geoNetwork_metro_pkl.transform(df['geoNetwork_metro'])
df['geoNetwork_networkDomain'] = geoNetwork_networkDomain_pkl.transform(df['geoNetwork_networkDomain'])
df['geoNetwork_region'] = geoNetwork_region_pkl.transform(df['geoNetwork_region'])
df['geoNetwork_subContinent'] = geoNetwork_subContinent_pkl.transform(df['geoNetwork_subContinent'])
df['trafficSource_adContent']= trafficSource_adContent_pkl.transform(df['trafficSource_adContent'])
df['trafficSource_adwordsClickInfo_gclId'] = trafficSource_adwordsClickInfo_gclId_pkl.transform(df['trafficSource_adwordsClickInfo_gclId'])
df['trafficSource_adwordsClickInfo_page'] = trafficSource_adwordsClickInfo_page_pkl.transform(df['trafficSource_adwordsClickInfo_page'])
df['trafficSource_adwordsClickInfo_slot'] = trafficSource_adwordsClickInfo_slot_pkl.transform(df['trafficSource_adwordsClickInfo_slot'])
df['trafficSource_source'] = trafficSource_source_pkl.transform(df['trafficSource_source'])
df['trafficSource_campaign'] = trafficSource_campaign_pkl.transform(df['trafficSource_campaign'])
df['trafficSource_isTrueDirect'] = trafficSource_isTrueDirect_pkl.transform(df['trafficSource_isTrueDirect'])
df['trafficSource_keyword'] = trafficSource_keyword_pkl.transform(df['trafficSource_keyword'])
df['trafficSource_medium'] = trafficSource_medium_pkl.transform(df['trafficSource_medium'])
df['trafficSource_referralPath'] = trafficSource_referralPath_pkl.transform(df['trafficSource_referralPath'])
# Making predictions
prediction= model.predict(df)
output = round(prediction[0], 4)
if output < 0:
return render_template('home.html', prediction_text="Transaction Revenue is less than 0")
else:
return render_template('home.html', prediction_text="Transaction Revenue is {} $".format(output))
if __name__ == "__main__":
app.run(debug=True)