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@app.callback(Output('text-input', 'value'),
              Input('reset-button', 'n_clicks'))
def clear_form(n_clicks):
    """Empty input textarea"""
    return ""


@app.callback(Output('explainer-obj', 'children'),
              Input('submit-button', 'n_clicks'),
              Input('reset-button', 'n_clicks'),

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# ======== App layout ======== 
app.layout = html.Div([
    html.H3('''
        LIME Explainer Dashboard for Fine-grained Sentiment
    ''', style={'text-align': 'center'}),
    html.Label('''
        1: Strongly Negative 2: Weakly Negative 3: Neutral 4: Weakly Positive 5: Strongly Positive
    ''', style={'text-align': 'center'}),
    html.Br(),
    html.Label('Enter your text:'),

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import os
from flask import Flask, request, render_template
from lime_explainer import explainer, tokenizer, METHODS

app = Flask(__name__)
SECRET_KEY = os.urandom(24)

@app.route('/')
@app.route('/result', methods=['POST'])
def index():

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# ========== Callbacks ================
@app.callback(Output('topic-data', 'data'), [Input('date-dropdown', 'value')])
def get_topic_data(value):
    with pymongo.MongoClient(**MONGO_ARGS) as connection:
        read_collection = connection[READ_DB][READ_COL]
        data = read_collection.find({'_id': value})
        # Collect data
        data = list(data)[0]
        # Perform some expensive calculation
        data_transformed = expensive_calc(data)

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# ========== Callbacks ================
@app.callback(Output('line-chart-1', 'figure'),
              [Input('dropdown-1', 'value')])
def update_graph(selected_dropdown_value):
    dff = filter_df(selected_dropdown_value)
    return {
        'data': [{
            'x': dff.Date,
            'y': dff.QuoteCount,
            'line': {

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# ========== Callbacks ================
@app.callback(Output('line-chart-1', 'figure'),
              [Input('dropdown-1', 'value')])
def update_graph(selected_dropdown_value):
    dff = df[df['Date'] == selected_dropdown_value]
    return {
        'data': [{
            'x': dff.Date,
            'y': dff.QuoteCount,
            'line': {

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import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd

app = dash.Dash('app', server=server)
df = pd.read_csv('input_data.csv')

# ========== App Layout ================
app.layout = html.Div([

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from joblib import Parallel, delayed

def chunker(iterable, total_length, chunksize):
    return (iterable[pos: pos + chunksize] for pos in range(0, total_length, chunksize))

def flatten(list_of_lists):
    "Flatten a list of lists to a combined list"
    return [item for sublist in list_of_lists for item in sublist]

def process_chunk(texts):

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def lemmatize_pipe(doc):
    lemma_list = [str(tok.lemma_).lower() for tok in doc
                  if tok.is_alpha and tok.text.lower() not in stopwords] 
    return lemma_list

def preprocess_pipe(texts):
    preproc_pipe = []
    for doc in nlp.pipe(texts, batch_size=20):
        preproc_pipe.append(lemmatize_pipe(doc))
    return preproc_pipe

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def lemmatize(text):
    """Perform lemmatization and stopword removal in the clean text
       Returns a list of lemmas
    """
    doc = nlp(text)
    lemma_list = [str(tok.lemma_).lower() for tok in doc
                  if tok.is_alpha and tok.text.lower() not in stopwords]
    return lemma_list