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# Import Libraries
# ============================================
import streamlit as st
import re
import pickle
import joblib
import nltk
import os
import numpy as np
import pandas as pd
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow import keras
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import PorterStemmer
from huggingface_hub import hf_hub_download
# ============================================
# Setup NLTK
# ============================================
nltk_data_path = os.path.join("/tmp", "nltk_data")
os.makedirs(nltk_data_path, exist_ok=True)
nltk.data.path.append(nltk_data_path)
nltk.download("stopwords", download_dir=nltk_data_path)
nltk.download("punkt", download_dir=nltk_data_path)
# ============================================
# Loading Info
# ============================================
st.markdown(
'<p style="color:gray; font-size:14px; font-style:italic;">'
'Loading models (≈200 MB) and resources... this may take a while on first run. '
'Please be patient and DO NOT refresh the page :)'
'</p>',
unsafe_allow_html=True
)
# ============================================
# Hugging Face Hub Repo
# ============================================
repo_id = "BesottenJenny/acre-sentiment-models"
# ============================================
# Cached Loading Functions
# ============================================
@st.cache_resource
def load_sentiment_model():
path = hf_hub_download(repo_id=repo_id, filename="best_model.keras")
return keras.models.load_model(path)
@st.cache_resource
def load_tokenizer_params():
tokenizer_path = hf_hub_download(repo_id=repo_id, filename="tokenizer.pkl")
params_path = hf_hub_download(repo_id=repo_id, filename="params.pkl")
with open(tokenizer_path, "rb") as f:
tokenizer = pickle.load(f)
with open(params_path, "rb") as f:
params = pickle.load(f)
return tokenizer, params
@st.cache_resource
def load_topic_models():
neg_path = hf_hub_download(repo_id=repo_id, filename="fastopic_negative_model.pkl")
pos_path = hf_hub_download(repo_id=repo_id, filename="fastopic_positive_model.pkl")
neg_model = joblib.load(neg_path)
pos_model = joblib.load(pos_path)
return neg_model, pos_model
# ============================================
# Load all resources once
# ============================================
sentiment_model = load_sentiment_model()
tokenizer, params = load_tokenizer_params()
topic_model_neg, topic_model_pos = load_topic_models()
max_len = params["max_len"]
# ============================================
# Preprocessing Function (NLTK)
# ============================================
negations = {"not", "no", "never"}
stpwrds_en = set(stopwords.words("english")) - negations
stemmer = PorterStemmer()
replacements = {
"sia": "sq",
"flown": "fly",
"flew": "fly",
"alway": "always",
"boarding": "board",
"told": "tell",
"said": "say",
"booked": "book",
"paid": "pay",
"well": "good",
"aircraft": "plane"
}
def text_preprocessing(text):
text = text.lower()
text = re.sub(r"\\n", " ", text)
text = text.strip()
text = re.sub(r'[^a-z0-9\s]', ' ', text)
tokens = word_tokenize(text)
tokens = [replacements.get(word, word) for word in tokens]
tokens = [word for word in tokens if word not in stpwrds_en]
tokens = [stemmer.stem(word) for word in tokens]
if len(tokens) == 0:
return "emptytext"
return ' '.join(tokens)
# ============================================
# Streamlit App
# ============================================
def run():
st.title("ACRE - Automated Customer Review Analysis")
st.subheader("Sentiment & Topic Prediction for SQ Customer Reviews")
st.markdown(
"""
This section will help you understand how the **ACRE** system works.
Simply fill in the form below with either a dummy or real customer review, and the system will:
1. **Preprocess** your review text (cleaning, tokenization, and stemming).
2. **Predict sentiment** (Positive or Negative) along with a confidence score.
3. **Identify the most relevant topic** associated with the review, based on the predicted sentiment.
Use this tool to simulate how Singapore Airlines can transform raw customer feedback into **structured, data-driven insights**.
"""
)
with st.form(key='SQ-sentiment-analysis'):
date = st.date_input("Review Date")
platform = st.selectbox('Review Platform', ('Mobile', 'Desktop'), index=0)
rating = st.number_input('Rating', min_value=0, max_value=5, value=3, step=1)
st.markdown('---')
text = st.text_input('Customer Review', value='--customer review--')
title = st.text_input('Review Title', value='--review title--')
vote = st.slider('Helpful Vote', min_value=0, max_value=200, value=50, step=1)
st.markdown('---')
submitted = st.form_submit_button('Predict')
if submitted:
st.markdown("---")
st.write("### Input Data")
data_inf = {
'published_date': date,
'published_platform': platform,
'rating': rating,
'type': 'Review',
'text': text,
'title': title,
'helpful_votes': vote
}
st.dataframe(pd.DataFrame([data_inf]))
# Preprocess
processed = text_preprocessing(text)
seq = tokenizer.texts_to_sequences([processed])
padded = pad_sequences(seq, maxlen=max_len, padding="post", truncating="post")
# Sentiment Prediction
pred_probs = sentiment_model.predict(padded)
pred_class = np.argmax(pred_probs, axis=1)[0]
confidence = float(np.max(pred_probs))
label_map = {0: "Negative", 1: "Positive"}
sentiment_label = label_map[pred_class]
st.write("### Sentiment Prediction")
if sentiment_label == "Negative":
st.markdown(f"<h3 style='color:red;'>Predicted Sentiment: {sentiment_label}</h3>", unsafe_allow_html=True)
else:
st.markdown(f"<h3 style='color:green;'>Predicted Sentiment: {sentiment_label}</h3>", unsafe_allow_html=True)
st.write(f"**Confidence:** {confidence:.2f}")
# Topic Prediction
st.write("### Topic Modeling")
if sentiment_label == "Negative":
topics, probs = topic_model_neg.transform([text])
st.write("**Using Negative Model**")
st.markdown(f"<p style='color:red;'>Topic ID(s): {topics}</p>", unsafe_allow_html=True)
else:
topics, probs = topic_model_pos.transform([text])
st.write("**Using Positive Model**")
st.markdown(f"<p style='color:green;'>Topic ID(s): {topics}</p>", unsafe_allow_html=True)
st.write(f"**Probabilities:** {probs.tolist()}")
# ============================================
# Run App
# ============================================
if __name__ == "__main__":
run() |