Initial Commit

Procfile

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+web: gunicorn --worker-class eventlet -w 1 app:app --timeout 120

app.py

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+# Import Modules
+
+from flask import Flask, render_template
+from flask_socketio import SocketIO, emit
+import numpy as np
+import requests
+from datetime import datetime
+from fbprophet import Prophet
+import pandas as pd
+from helper_v4 import forecastr,determine_timeframe,get_summary_stats,validate_model
+import logging
+
+
+
+# Socket IO Flask App Setup
+
+app = Flask(__name__)
+app.config['SECRET_KEY'] = 'secret!'
+socketio = SocketIO(app, logger=False, engineio_logger=False)
+
+
+# Suppress logs except for error: https://stackoverflow.com/questions/43487264/disabling-logger-in-flask-socket-io
+#logging.getLogger('socketio').setLevel(logging.ERROR)
+#logging.getLogger('engineio').setLevel(logging.ERROR)
+#logging.getLogger('geventwebsocket.handler').setLevel(logging.ERROR)
+
+
+
+@app.after_request
+def add_header(r):
+    """
+    Add headers to both force latest IE rendering engine or Chrome Frame,
+    and also to cache the rendered page for 10 minutes.
+    """
+    r.headers["Cache-Control"] = "no-cache, no-store, must-revalidate"
+    r.headers["Pragma"] = "no-cache"
+    r.headers["Expires"] = "0"
+    r.headers['Cache-Control'] = 'public, max-age=0'
+    return r
+
+# Flask App
+
+@app.route('/app/')
+def index():
+    return render_template('build-forecast-v3.html') # Application
+
+@app.route('/')
+def about():
+    return render_template('forecastr.html') # Product Page
+
+
+@socketio.on('connection_msg')
+def connected(message):
+
+    data = message
+    print(data)
+
+
+@socketio.on('forecast_settings')
+def forecast_settings(message):
+
+    # Initial forecast settings - the first time the user sends forecast settings through the app - will use this value in forecastr method
+    build_settings = 'initial'
+
+    # store message['data'] into a df called data
+    data = message['data']
+
+    # Keep Original Data in Exisiting Structure
+    original_dataset = data[1]['data'][1]['data']
+
+    #print("******************** ORIGINAL DATASET *****************************")
+    #print(original_dataset)
+    #print("******************** ORIGINAL DATASET *****************************")
+
+    # Extract info from forecast_settings message
+    time_series_data = pd.DataFrame(data[1]['data'][1]['data'])
+    forecast_settings = data[0]
+    freq = data[2]
+    column_headers = data[1]['data'][0]
+
+    # Format the date and metric unit
+    time_unit = column_headers[0]
+    time_series_data[time_unit] = time_series_data[time_unit].apply(lambda x: pd.to_datetime(str(x)))
+    metric = column_headers[1]
+
+    # y (aka as "the original data for the metric being forecasted") will be used in the chartjs line graph 
+    y = time_series_data[metric].tolist()
+
+    # Use Facebook Prophet through forecastr method
+    forecast = forecastr(time_series_data,forecast_settings,column_headers,freq,build_settings)
+
+    # Need to convert forecast back into a list / array for y, y_hat and date so it can be properly graphed with chartjs
+    y_hat = forecast[0]
+    dates = forecast[1]
+    model = forecast[2]
+    csv_export = forecast[3]
+
+    # Send data back to the client
+    data_back_to_client = [dates,y_hat,y,forecast_settings,column_headers,freq,original_dataset,csv_export]
+    #print(data_back_to_client)
+
+
+    emit('render_forecast_chart', {'data': data_back_to_client})
+
+
+
+    # Validate Model
+    #mape_score = validate_model(model,dates)
+
+    #emit('model_validation', {'data':mape_score})
+
+
+@socketio.on('update_chart_settings')
+def update_chart(message):
+
+    # This is an update to the initial forecast settings. The user has changed their settings on Step 3, so we set build_settings to update.
+    build_settings = 'update'
+
+    data = message['data']
+
+    ### Setup variables for use in the forecastr method    
+    time_series_data = data[4]
+    original_dataset = time_series_data
+    time_series_data = pd.DataFrame(time_series_data)
+
+    #print("********* TIME SERIES DF ****************")
+    #print(time_series_data.head())
+    #print("********* TIME SERIES DF ****************")
+
+    forecast_settings = data[1]
+    column_headers = data[2]
+    freq = data[3]
+
+    # Dimension and Metric
+    time_unit = column_headers[0]
+    metric = column_headers[1]
+
+    # Make sure time_unit is converted to datetime in order to join in helper_v3
+    time_series_data[time_unit] = time_series_data[time_unit].apply(lambda x: pd.to_datetime(str(x)))
+
+
+    #print([time_unit,metric])
+
+    # Original Data
+    y = time_series_data[metric].tolist()
+
+    # Use Facebook Prophet through forecastr method
+    forecast = forecastr(time_series_data,forecast_settings,column_headers,freq,build_settings)
+
+    # Need to convert forecast back into a list / array for y, y_hat and date so it can be properly graphed with chartjs
+    y_hat = forecast[0]
+    dates = forecast[1]
+    model = forecast[2]
+    csv_export = forecast[3]
+
+    # Send data back to the client - took out original dataset
+    data_back_to_client = [dates,y_hat,y,forecast_settings,column_headers,freq,original_dataset,csv_export]
+    emit('render_forecast_chart', {'data': data_back_to_client})
+
+    # Validate Model
+    #mape_score = validate_model(model,dates)
+
+    #emit('model_validation', {'data':mape_score})
+
+    #print(forecast)
+
+
+
+
+    #print("UPDATING **********************")
+    #print(time_series_data)
+
+
+@socketio.on('reset')    
+def reset(message):
+
+    data = message['data']
+    #print(data)
+
+
+@socketio.on('send_csv')
+def main(message):
+
+    # Store message['data'] in data
+    data = message['data']
+
+    # Convert data to a pandas DataFrame
+    data = pd.DataFrame(data)
+
+    # Get list of column headers
+    column_headers = list(data)
+
+    time_unit = column_headers[0]
+    metric_unit = column_headers[1]
+
+    #print([time_unit,metric_unit])
+
+    # Determine whether the timeframe is daily, weekly, monthly, or yearly
+    timeframe = determine_timeframe(data, time_unit)
+
+    # Get summary statistics about original dataset
+    summary_stats = get_summary_stats(data,column_headers)
+
+    # Send original data to a list
+    dimension = data[time_unit].tolist()
+    metric = data[metric_unit].tolist()
+
+    original_data = [dimension,metric]
+
+    # Send data back to the client in the form of a label detected or text extracted.
+    emit('render_uploaded_csv_data', {'data': [column_headers,message, timeframe, summary_stats,original_data]})
+
+    # Probably need some sort of date checker function to properly format the date.
+    data[time_unit] = data[time_unit].apply(lambda x: pd.to_datetime(str(x)))
+
+
+
+if __name__ == '__main__':
+    socketio.run(app, log_output=False)