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# Install: !pip install next_word_prediction
# curText = doc_set[1135957]
from next_word_prediction import GPT2
gpt2 = GPT2()

# Predict the next word after 'vegetarian'
gpt2.predict_next(curText.replace('[MASK]', ''), 5)

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# Replace with the correct words
from functools import reduce
reduce(lambda x, y: x.replace(*y), [curText, *list(correctDict.items())])

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# Install: !pip install pyspellchecker
from spellchecker import SpellChecker
spell = SpellChecker()

# Text from 122001 of the wikiText data; modified to inculde typos: 
# 'commentary' -> 'commentyra', 'gimmick' -> 'gimimick'; 
# curText = doc_set[122001]
# Use the spellchecker to identify and correct the typos
correctDict = {}
for val in re.split(r'[^\w]', curText): 

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import re
# curText = doc_set[122001]
curText = '''
six months ago we thought it would be a fun idea to release our album on election day but this is not the election to be cute . 
we felt as though rather than making a commentary we were only riding the wave of the election . this seemed less and less like what we 
intended to do and more of a gimmick . 
'''
## Replace the words 'day', 'we', 'is'
print(' '.join(re.sub(r'|'.join(map(re.escape, ['day', 'we', 'is'])), ' ', curText).split()))

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### Install: !pip install textstate
import textstat
# curText = doc_set[27310]
# (1) Flesch readability score
print(textstat.flesch_reading_ease(curText))
68.94  ## indicating Standard
# (2) Reading time, assuming 15 ms/character
print(textstat.reading_time(curText, ms_per_char=15))
3.8   ## 3.8s to read 
# (3) Grade level: Intended for text written for children up to grade four

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import streamlit as st
st.title("Online Retail")
## !!! date options
date = st.sidebar.selectbox(
            "Select a Invoice Date Range",
            [
              "'2010-12-01' and '2011-01-01'", 
              "'2010-12-01' and '2010-12-15'"              
            ])
## !!! product options           

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## Query with placeholders
timeSeries_query = f"""
SELECT 
	InvoiceDate, 
	Description,
	ROUND(SUM(Quantity * UnitPrice), 2) AS Total_Sale_Amt  
FROM 
	OnlineRetail
WHERE InvoiceDate BETWEEN {date}           ## !!! Placeholder for date
	AND Description IN ('{product}')   ## !!! Placeholder for product 

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## Read in external SQL file 
sqlFile = open(r'...\OnlineRetailPull.sql', 'r')
myQuery = sqlFile.read()
OnlineRetailData = pd.read_sql_query(myQuery, engine)     

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## Query to pull data
timeSeries_query = """
SELECT 
	InvoiceDate, 
	Description,
	ROUND(SUM(Quantity * UnitPrice), 2) AS Total_Sale_Amt  
FROM 
	OnlineRetail
WHERE InvoiceDate BETWEEN '2010-12-01' AND '2011-01-01'
	AND Description IN ('CHOCOLATE HOT WATER BOTTLE', 'GREY HEART HOT WATER BOTTLE')

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import sqlalchemy as sa
import urllib

params = urllib.parse.quote_plus("DRIVER={SQL Server Native Client 11.0};"
                                 "SERVER=localhost\SQLEXPRESS;"
                                 "DATABASE=master;"
                                 "Trusted_Connection=yes")
                                 
## Connect using the specified parameters
engine = sa.create_engine("mssql+pyodbc:///?odbc_connect={}".format(params))