RuntimeFile.py

import pickle
from sklearn.feature_extraction.text import CountVectorizer
#model filename

filename = 'reviews_classifier.sav'

loaded_classifier = pickle.load(open(filename, 'rb'))

cv = CountVectorizer(max_features = 2000)
with open('corpus.data', 'rb') as filehandle:
    # read the data as binary data stream
    corpus = pickle.load(filehandle)
cv.fit_transform(corpus)


print("Welcome to the Restaurant Review analyser")
print("The output will be either positive or negative.")
print("A count vectorizer was used")
print("---------------------------------------------------")
user_input=input("Enter the review of the restaurant:  ")

test = [user_input]
test_vec = cv.transform(test)
val=loaded_classifier.predict(test_vec)[0]

print("---------------------------------------------------")

if(val==0):
    print("The review entered was negative.")
    print("The user did not like the restaurant.")
    
if(val==1):
    print("The review entered was positive.")
    print("The user liked the restaurant.")