sentiment.py

## This code runs successfully thanks to geeksforgeeks

from transformers import BertTokenizer, TFBertForSequenceClassification
import os
# import shutil
# import tarfile
import tensorflow as tf
from transformers import BertTokenizer, TFBertForSequenceClassification
import pandas as pd
# from bs4 import BeautifulSoup
import re
# import matplotlib.pyplot as plt
# import plotly.express as px
# import plotly.offline as pyo
# import plotly.graph_objects as go
# from wordcloud import WordCloud, STOPWORDS
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
import streamlit as st


path = 'path-to-save'

bert_tokenizer = BertTokenizer.from_pretrained(path +'/Tokenizer')
 
# Load model
bert_model = TFBertForSequenceClassification.from_pretrained(path +'/Model')


def Get_sentiment(Review, Tokenizer=bert_tokenizer, Model=bert_model):
    # Convert Review to a list if it's not already a list
    if not isinstance(Review, list):
        Review = [Review]
 
    Input_ids, Token_type_ids, Attention_mask = Tokenizer.batch_encode_plus(Review,
                                                                             padding=True,
                                                                             truncation=True,
                                                                             max_length=128,
                                                                             return_tensors='tf').values()
    prediction = Model.predict([Input_ids, Token_type_ids, Attention_mask])
 
    
    # Use argmax along the appropriate axis to get the predicted labels
    pred_labels = tf.argmax(prediction.logits, axis=1)
    
     
    label = {
    1: 'positive',
    0: 'Negative'
    }

    # Convert the TensorFlow tensor to a NumPy array and then to a list to get the predicted sentiment labels
    pred_labels = [label[i] for i in pred_labels.numpy().tolist()]
    return pred_labels
    

# Review ='''Bahubali is a blockbuster Indian movie that was released in 2015. 
# It is the first part of a two-part epic saga that tells the story of a legendary hero who fights for his kingdom and his love. 
# The movie has received rave reviews from critics and audiences alike for its stunning visuals, 
# spectacular action scenes, and captivating storyline.'''
Review= st.text_area('Input your text')

if Review:
    sentiment_labels = Get_sentiment(Review)
    print(sentiment_labels)
    st.write(sentiment_labels,Review)