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server {
    server_name www.example.com;
    access_log  /var/log/nginx/example.com.log;
    error_log    /var/log/nginx/example.com.error.log debug;
    root  /home/myuser/example.com/homepage;
    sendfile on;

    # if the uri is not found, look for index.html, else pass everthing to gunicorn
    location / {
	index index.html;

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[supervisord]
nodaemon=true
logfile=supervisord.log

[supervisorctl]

[inet_http_server]
port = 127.0.0.1:9001

[rpcinterface:supervisor]

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example.com/
  example-dango/
  homepage/
  

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#!/bin/bash
WORKING_DIR=/home/myuser/example.com/example-django
cd ${WORKING_DIR}
source .server.envrc
source ENV/bin/activate
exec $@

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import environ
import os
root = environ.Path(__file__) - 2 # two folders back (/a/b/ - 2 = /)
env = environ.Env(DEBUG=(bool, False),) # set default values and casting
GOOGLE_ANALYTICS_ID=env('GOOGLE_ANALYTICS_ID')

SITE_DOMAIN = env('SITE_DOMAIN') 
SITE_ROOT = root()

DEBUG = env('DEBUG') # False if not in os.environ

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"""
To evaluate the good or bad score of a tweet, we first tokenize the tweet, and then 
stemmize each word in our tweet. We also associate each stem with positive and negative values, 
respectively, using a dictionary.

Finally, we caculate the average word weight of a tweet, and decide if it's a good or bad one 
based on that.

"""
import json

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"""
To evaluate the good or bad score of a tweet, we count the number of good and
bad words in it.

if a word is good, increase the value of good_words by one
else if a word is bad, increase the value of bad_words by one
if good_words > bad_words then it's a good tweet otherwise it's a bad tweet

"""
import json

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{
    "consumer_key": "YOUR CONSUMER KEY",
    "consumer_secret": "YOUR CUSTOMER SECRET",
    "access_token_key": "YOUR ACCESS TOKEN KEY",
    "access_token_secret": "YOUR ACCESS TOKEN SECRET"
}

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with open('sample.json') as f:
  myJson = json.load(f)

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f = open('sample.json')
myJson = json.load(f)
f.close()