juliet.py

import numpy as np 
import string
from nltk.corpus import stopwords
import pandas as pd 
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.tree import DecisionTreeClassifier
from sklearn.feature_extraction.text import TfidfTransformer,TfidfVectorizer
from sklearn.pipeline import Pipeline

df = pd.read_csv('test.txt',sep='\t')
a = pd.Series(df.columns)
a = a.rename({0: df.columns[0],1: df.columns[1]})

def cleaner(x):
    return [a for a in (''.join([a for a in x if a not in string.punctuation])).lower().split()]
    
Pipe = Pipeline([
    ('bow',CountVectorizer(analyzer=cleaner)),
    ('tfidf',TfidfTransformer()),
    ('classifier',DecisionTreeClassifier())
])

Pipe.fit(df['question'],df["answer"])

while(True):
          a=input("User: ")
          print("Juliet: ", Pipe.predict([a])[0])