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import psycopg2
import sqlalchemy
from decouple import config 
#AWS_DATABASE_URL is 'postgres://username:password@database-instance-name.randomchars.us-east-2.rds.amazonaws.com:5432/databaseName
host = config('AWS_DATABASE_URL')
db = sqlalchemy.create_engine(host)

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import pickle
filename = "saved_thing"
infile = open(filename, "rb")
thing = pickle.load(infile)
infile.close()

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import pickle
filename = "saved_thing"
outfile = open(filename,'wb')
pickle.dump(thing, outfile)
outfile.close()

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def execute_async_event_loop(min_song, max_song, song_urls):
    """
    This function does something analogous to compiling the get_data_asynchronously function,
    Then it executes loop.
    1. Call the get_data_function
    2. Get the event_loop
    3. Run the tasks (Much easier to understand in python 3.7, "ensure_future" was changed to "create_task")
    4. Edge_list and top_interactions will be passed to the next functions
    """
    future = asyncio.create_task(

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from decouple import config
import json
import requests
import sys
import asyncio
from concurrent.futures import ThreadPoolExecutor
import nest_asyncio

nest_asyncio.apply()

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  async def get_data_asynchronous(min_song, max_song, song_urls):
    """
    1. Establish an executor and number of workers
    2. Establish the session
    3. Establish the event loop
    4. Create the task by list comprehensions
    5. Gather tasks.
    """
    with ThreadPoolExecutor(max_workers=40) as executor:
        with requests.Session() as session:

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def request_song_info(session, song_num, song_urls):
    base_url = "https://api.genius.com/songs/" + str(song_num)
    headers = {"Authorization": "Bearer " + CLIENT_ACCESS_TOKEN}
    response = requests.get(base_url, headers=headers)
    try:
        if response.json()["meta"]["status"] == 200:
            song_urls.append(
                (
                    song_num,
                    response.json()["response"]["song"]["title"],

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<!--make the html element to contain the chart.-->
<canvas id="line-chart" width="3" height="1"></canvas>

{% block javascript %}
	<script>
  // load the objects like you would with a normal jinja template i.e. {{ object }}, but add | tojson
  // pass it into the parse function
	labels = JSON.parse({{ labels | tojson }})
	data = JSON.parse({{ data | tojson }})
	new Chart(document.getElementById("line-chart"),{

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#The important imports for this tutorial
from flask import Flask, render_template, json

#Skiping a whole lot of the file to get to the useful parts.


"""Focusing on just this route that display a linechart for the sentiment of a topic over time."""
@app.route('/line', methods =["GET"])
def line_chart():
  """

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model = create_model(gp_result.x[0],gp_result.x[1],gp_result.x[2],gp_result.x[3],gp_result.x[4],gp_result.x[5])
model.fit(X_train,y_train, epochs=3)
model.evaluate(X_test,y_test)