Model.py

from textblob import TextBlob
import pandas as pd
import re 
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')

def inputs():
    flag = True
    lines2 = []
    while flag:
      val = input("Enter the text: ")
      lines2.append(val)
      ans = input("Do u want to enter another tweet y/n? ")
      flag = ans == 'y'

    df = pd.DataFrame({'Tweets' : lines2})
    
    df['Tweets'] = df['Tweets'].apply(cleanText)
    
    df['Polarity'] = df['Tweets'].apply(getPolarity)
    
    df['Analysis'] = df['Polarity'].apply(getAnalysis)

    sortedDf = printHistory(df, True)
    
    return (str(sortedDf))


def input(text):
    
    lines2 = []
    lines2.append(text)
    df = pd.DataFrame({'Tweets' : lines2})
    
    df['Tweets'] = df['Tweets'].apply(cleanText)
    
    df['Polarity'] = df['Tweets'].apply(getPolarity)
    
    df['Analysis'] = df['Polarity'].apply(getAnalysis)
    sortedDf = printHistory(df, True)
    
    return (str(sortedDf))


#Clean the text
def cleanText(text):
  text = re.sub(r'@[A-za-z0-9]+' , '', text) #Remove mentions
  text = re.sub(r'RT[\s]+', '', text) #remove retweets
  text = re.sub(r'https?:\/\/\S+', '', text) #remove hyperlinks

  return text 


#Create a function to get polarity
def getPolarity(text):
  return TextBlob(text).sentiment.polarity


#Create a fn to compute the negative, neutral and positive analysis
def getAnalysis(score):
  if score < - 0.2:
    return 'Negative'
  elif score > 0.2:
    return 'Positive'
  else: 
    return 'Neutral'


def printHistory(df, asc = True, num = 5):
  sortedDF = df.sort_values(by = ['Polarity'], axis=0, ascending = asc)
  sortedDF = sortedDF.reset_index(drop=True)
  sortedDF = sortedDF[['Tweets', 'Analysis']].iloc[:num]
  return sortedDF