helper.py

import numpy as np
import pandas as pd
import re
import string
import pickle

from nltk.stem import PorterStemmer
ps = PorterStemmer()

with open('static/model/model.pickle', 'rb') as f:
    model = pickle.load(f)

with open('static/model/corpora/stopwords/english', 'r') as file:
    sw = file.read().splitlines()

vocab = pd.read_csv('static/model/vocabulary.txt', header=None)
tokens = vocab[0].tolist()

def remove_punctuations(text):
    for punctuation in string.punctuation:
        text = text.replace(punctuation, '')
    return text

def preprocessing(text):
    data = pd.DataFrame([text], columns=['tweet'])
    data["tweet"] = data["tweet"].apply(lambda x: " ".join(x.lower() for x in x.split()))
    data["tweet"] = data["tweet"].apply(lambda x: " ".join(re.sub(r'^https?:\/\/.*[r\r\n]*', '', x, flags=re.MULTILINE) for x in x.split()))
    data["tweet"] = data["tweet"].apply(remove_punctuations)
    data["tweet"] = data['tweet'].str.replace('\d+', '', regex=True)
    data["tweet"] = data["tweet"].apply(lambda x: " ".join(x for x in x.split() if x not in sw))
    data["tweet"] = data["tweet"].apply(lambda x: " ".join(ps.stem(x) for x in x.split()))
    return data['tweet']

def vectorizer(ds):
    vectorized_lst = []

    for sentence in ds:
        sentence_lst = np.zeros(len(tokens))

        for i in range(len(tokens)):
            if tokens[i] in sentence.split():
                sentence_lst[i] = 1
    
        vectorized_lst.append(sentence_lst) 

    vectorized_lst_new = np.asanyarray(vectorized_lst, dtype=np.float32)

    return vectorized_lst_new

def get_prediction(vectorized_text):
    prediction = model.predict(vectorized_text)
    if prediction == 1:
        return 'negative'
    else:
        return 'positive'