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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import nltk | |
| import tensorflow as tf | |
| from nltk.corpus import stopwords | |
| import re | |
| from nltk.tokenize import word_tokenize | |
| from nltk.stem import WordNetLemmatizer | |
| from nltk.stem import LancasterStemmer | |
| from keras.models import load_model | |
| import string | |
| # Download required NLTK data | |
| nltk.download('stopwords') | |
| nltk.download('punkt') | |
| nltk.download('wordnet') | |
| nltk.download('data') | |
| # Load the pre-trained model | |
| model = tf.keras.models.load_model('restaurant_review') | |
| # Mendefinisikan Stopwords | |
| stopwords_eng = stopwords.words("english") | |
| # Membuat Stopwords Baru | |
| new_stw = ['i', | |
| 'me', | |
| 'my', | |
| 'myself', | |
| 'we', | |
| 'our', | |
| 'ours', | |
| 'ourselves', | |
| 'you', | |
| "you're", | |
| "you've", | |
| "you'll", | |
| "you'd", | |
| 'your', | |
| 'yours', | |
| 'yourself', | |
| 'yourselves', | |
| 'he', | |
| 'him', | |
| 'his', | |
| 'himself', | |
| 'she', | |
| "she's", | |
| 'her', | |
| 'hers', | |
| 'herself', | |
| 'it', | |
| "it's", | |
| 'its', | |
| 'itself', | |
| 'they', | |
| 'them', | |
| 'their', | |
| 'theirs', | |
| 'themselves', | |
| 'what', | |
| 'which', | |
| 'who', | |
| 'whom', | |
| 'this', | |
| 'that', | |
| "that'll", | |
| 'these', | |
| 'those', | |
| 'am', | |
| 'is', | |
| 'are', | |
| 'was', | |
| 'were', | |
| 'be', | |
| 'been', | |
| 'being', | |
| 'have', | |
| 'has', | |
| 'had', | |
| 'having', | |
| 'do', | |
| 'does', | |
| 'did', | |
| 'doing', | |
| 'a', | |
| 'an', | |
| 'the', | |
| 'and', | |
| 'but', | |
| 'if', | |
| 'or', | |
| 'because', | |
| 'as', | |
| 'until', | |
| 'while', | |
| 'of', | |
| 'at', | |
| 'by', | |
| 'for', | |
| 'with', | |
| 'about', | |
| 'against', | |
| 'between', | |
| 'into', | |
| 'through', | |
| 'during', | |
| 'before', | |
| 'after', | |
| 'above', | |
| 'below', | |
| 'to', | |
| 'from', | |
| 'up', | |
| 'down', | |
| 'in', | |
| 'out', | |
| 'on', | |
| 'off', | |
| 'over', | |
| 'under', | |
| 'again', | |
| 'further', | |
| 'then', | |
| 'once', | |
| 'here', | |
| 'there', | |
| 'when', | |
| 'where', | |
| 'why', | |
| 'how', | |
| 'all', | |
| 'any', | |
| 'both', | |
| 'each', | |
| 'few', | |
| 'more', | |
| 'most', | |
| 'other', | |
| 'some', | |
| 'such', | |
| 'no', | |
| 'nor', | |
| 'not', | |
| 'only', | |
| 'own', | |
| 'same', | |
| 'so', | |
| 'than', | |
| 'too', | |
| 'very', | |
| 's', | |
| 't', | |
| 'can', | |
| 'will', | |
| 'just', | |
| 'don', | |
| "don't", | |
| 'should', | |
| "should've", | |
| 'now', | |
| 'd', | |
| 'll', | |
| 'm', | |
| 'o', | |
| 're', | |
| 've', | |
| 'y', | |
| 'ain', | |
| 'aren', | |
| "aren't", | |
| 'couldn', | |
| "couldn't", | |
| 'didn', | |
| "didn't", | |
| 'doesn', | |
| "doesn't", | |
| 'hadn', | |
| "hadn't", | |
| 'hasn', | |
| "hasn't", | |
| 'haven', | |
| "haven't", | |
| 'isn', | |
| "isn't", | |
| 'ma', | |
| 'mightn', | |
| "mightn't", | |
| 'mustn', | |
| "mustn't", | |
| 'needn', | |
| "needn't", | |
| 'shan', | |
| "shan't", | |
| 'shouldn', | |
| "shouldn't", | |
| 'wasn', | |
| "wasn't", | |
| 'weren', | |
| "weren't", | |
| 'won', | |
| "won't", | |
| 'wouldn', | |
| "wouldn't" | |
| ] | |
| # Compile Stopwords | |
| stw_en = stopwords_eng + new_stw | |
| stw_en = list(set(stw_en)) | |
| # Define Lancaster Stemmer | |
| lanc = LancasterStemmer() | |
| # Define the text processing function | |
| def text_proses(review): | |
| # Mengubah Review ke Lowercase | |
| review = review.lower() | |
| # Menghilangkan Hashtag | |
| review = re.sub("#[A-Za-z0-9_]+", " ", review) | |
| # Menghilangkan \n | |
| review = re.sub(r"\\n", " ",review) | |
| # Menghilangkan Whitespace | |
| review = review.strip() | |
| # Menghilangkan Tanda Baca | |
| review = review.translate(str.maketrans('', '', string.punctuation)) | |
| # Menghilangkan Link | |
| review = re.sub(r"http\S+", " ", review) | |
| review = re.sub(r"www.\S+", " ", review) | |
| # Menghilangkan yang Bukan Huruf seperti Emoji, Simbol Matematika (seperti μ), dst | |
| review = re.sub("[^A-Za-z\s']", " ", review) | |
| # Menghilangkan duplicate characters | |
| review = re.sub("(.)\\1{2,}", "\\1", review) | |
| # Merapikan Spasi | |
| review = ' '.join(review.split()) | |
| # Melakukan Tokenisasi | |
| tokens = word_tokenize(review) | |
| # Menghilangkan Stopwords | |
| review = ' '.join([word for word in tokens if word not in stw_en]) | |
| # Melakukan Stemming | |
| review = lanc.stem(review) | |
| # Join the processed words into a single string | |
| return review | |
| # Define the lemmatization function | |
| def lemmatize_text(text): | |
| sentence = [] | |
| for word in text.split(): | |
| lemmatizer = WordNetLemmatizer() | |
| sentence.append(lemmatizer.lemmatize(word, 'v')) | |
| return ' '.join(sentence) | |
| # Define the Streamlit app | |
| def run(): | |
| st.title("Restaurant Review Inference") | |
| # Define the input form | |
| review = st.text_input("Enter a restaurant review:") | |
| liked = st.selectbox("Did you like the restaurant?", ['Yes', 'No']) | |
| # Process the input data | |
| data = { | |
| 'Review': review, | |
| 'Liked': 1 if liked == 'Yes' else 0 | |
| } | |
| data = pd.DataFrame([data]) | |
| data['text_processed'] = data['Review'].apply(text_proses) | |
| data['text_processed'] = data['text_processed'].apply(lemmatize_text) | |
| # Perform the inference | |
| if st.button("Predict"): | |
| y_pred = model.predict(data['text_processed']) | |
| y_pred = np.where(y_pred >= 0.5, 1, 0) | |
| # Show the prediction result | |
| if y_pred[0] == 1: | |
| st.write("The review is positive!") | |
| else: | |
| st.write("The review is negative.") | |
| # Run the Streamlit app | |
| if __name__ == '__main__': | |
| app() |