anajnas/text_classification_streamlit

1import pickle2import string3 4import streamlit as st 5import pandas as pd 6import numpy as np 7 8import tensorflow as tf9from keras.utils import pad_sequences10 11from nltk.stem import WordNetLemmatizer 12 13 14categories = ["Negative", "Neutral", "Positive"]15 16 17def preprocess(x):18    # Remove lowercase, punctuations, lemmatize19    20    lemmatizer = WordNetLemmatizer()21    22    text_lcase = x.lower()23    text_no_punct = text_lcase.translate(str.maketrans('', '', string.punctuation))24    25    return ' '.join(lemmatizer.lemmatize(t) for t in text_no_punct.split())26 27st.title('Financial sentiment analysis')28title = st.text_input('Movie title', 'Life of Brian')29 30st.write(title)31 32cleaned_title = preprocess(title)33 34model = tf.keras.models.load_model('model.keras')35 36tokenizer = None37with open('tokenizer.pkl', 'rb') as f:38    tokenizer = pickle.load(f)39 40tokenized_input = tokenizer.texts_to_sequences([cleaned_title])41tokenized_input = pad_sequences(tokenized_input, maxlen=52, padding='post')42# print(tokenized_input)43 44res = model.predict(tokenized_input)45 46cat = categories[np.argmax(res)]47st.write(cat)