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import streamlit as st |
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import numpy as np |
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import transformers |
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import re |
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import string |
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import preprocessor as pre |
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import torch |
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from transformers import BertTokenizer, BertForSequenceClassification |
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with open("style.css") as f: |
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st.markdown('<style>{}</style>'.format(f.read()), unsafe_allow_html=True) |
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model_path = "ninahf1503/SA-BERTchatgptapp" |
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tokenizer = BertTokenizer.from_pretrained(model_path) |
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model = BertForSequenceClassification.from_pretrained(model_path, ignore_mismatched_sizes=True ) |
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seq_max_length = 55 |
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def tokenizing_text(sentence): |
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sentence = preprocess_text(sentence) |
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encoded = tokenizer.encode_plus( |
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sentence, |
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add_special_tokens=True, |
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max_length=seq_max_length, |
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truncation=True, |
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padding='max_length', |
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return_tensors='pt' |
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) |
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input_ids = encoded['input_ids'] |
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attention_mask = encoded['attention_mask'] |
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return input_ids, attention_mask |
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def preprocess_text(sentence): |
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re_cleansing = "@\S+|https?:\S+|http?:\S|#[A-Za-z0-9]+|^RT[\s]+|(^|\W)\d+" |
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for punctuation in string.punctuation: |
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sentence = sentence.encode().decode('unicode_escape') |
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sentence = re.sub(r'\n', ' ', sentence) |
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sentence = pre.clean(sentence) |
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sentence = re.sub(r'[^\w\s]', ' ', sentence) |
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sentence = re.sub(r'[0-9]', ' ', sentence) |
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sentence = re.sub(re_cleansing, ' ', sentence).strip() |
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sentence = sentence.replace(punctuation, '') |
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sentence = sentence.lower() |
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return sentence |
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def predict_sentiment(input_text): |
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input_ids, attention_mask = tokenizing_text(input_text) |
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with torch.no_grad(): |
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outputs = model(input_ids, attention_mask) |
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logits = outputs.logits |
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predict_class = torch.argmax(logits, dim=1).item() |
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label_sentiment = {0: "Bad", 1: "Good", 2: "Neutral"} |
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predict_label = label_sentiment[predict_class] |
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return predict_label |
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def main(): |
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st.title("Sentimen Analysis", anchor=False) |
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tweet_text = st.text_area(" ", placeholder="Enter the sentence you want to analyze", label_visibility="collapsed") |
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if st.button("SUBMIT"): |
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if tweet_text.strip() == "": |
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st.title("Text Input Still Empty", anchor=False) |
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st.info("Please fill in the sentence you want to analyze") |
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else: |
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sentiment = predict_sentiment(tweet_text) |
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if sentiment == "Good": |
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st.title("Sentiment Analysis Results", anchor=False) |
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st.markdown('<div style="background-color: #5d9c59; padding: 16px; border-radius: 5px; font-weight: bold; color:white;">This sentence contains a positive sentiment</div>', unsafe_allow_html=True) |
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elif sentiment == "Bad": |
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st.title("Sentiment Analysis Results", anchor=False) |
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st.markdown('<div style="background-color: #df2e38; padding: 16px; border-radius: 5px; font-weight: bold; color:white;">This sentence contains a negative sentiment</div>', unsafe_allow_html=True) |
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else: |
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st.title("Sentiment Analysis Results", anchor=False) |
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st.markdown('<div style="background-color: #ffa500; padding: 16px; border-radius: 5px; font-weight: bold; color:white;">This sentence is neutral</div>', unsafe_allow_html=True) |
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if __name__ == "__main__": |
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main() |
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