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import streamlit as st
import pandas as pd
import re
import joblib
from sklearn.feature_extraction.text import CountVectorizer
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.models import load_model
from sklearn.metrics import accuracy_score
# Function to clean text
def clean_text(text):
text = re.sub(r'<.*?>', '', text) # Remove HTML tags
text = re.sub(r'[^a-zA-Z\s]', '', text) # Remove special characters and digits
text = text.lower() # Convert to lowercase
text = re.sub(r'\s+', ' ', text).strip() # Remove extra spaces
return text
# Load the pre-trained Naive Bayes model and CountVectorizer
nb_model = joblib.load('nb_model.h5')
count_vectorizer = joblib.load('vectorizer.joblib')
# Load the pre-trained RNN model and Tokenizer
rnn_model = load_model('RNN_Model.h5')
tokenizer = joblib.load('tokenizer.joblib')
# Define max length for padding
max_length = 15
# Streamlit UI
st.title(":green[Sentiment Analysis of Reviews]")
st.write("""
This app predicts the sentiment of product reviews using two machine learning models:
- Naive Bayes
- Recurrent Neural Network (RNN)
""")
# Text input
review_text = st.text_area("Enter a review text:", "")
if st.button("Predict"):
if review_text:
cleaned_text = clean_text(review_text)
# Naive Bayes Prediction
review_cv = count_vectorizer.transform([cleaned_text])
nb_prediction = nb_model.predict(review_cv)
# RNN Prediction
review_seq = tokenizer.texts_to_sequences([cleaned_text])
review_pad = pad_sequences(review_seq, maxlen=max_length)
rnn_prediction_prob = rnn_model.predict(review_pad)
rnn_prediction = rnn_prediction_prob.argmax(axis=-1)[0]
sentiment_mapping = {0: 'Negative Review', 1: 'Neutral Review', 2: 'Positive Review'}
st.write("### Predictions")
if nb_prediction[0] =="negative":
st.success(f"**Naive Bayes Prediction: Negative Review With an Accuracy of 0.95**")
elif nb_prediction[0] =="positive":
st.success(f"**Naive Bayes Prediction: Positive Review With an Accuracy of 0.95**")
else:
st.success(f"**Naive Bayes Prediction: Neutral Review With an Accuracy of 0.95**")
st.success(f"**RNN Prediction: {sentiment_mapping[rnn_prediction]} With an Accuracy of {round(rnn_prediction_prob[0][rnn_prediction],2)}**")
# Display probabilities for RNN
# st.write(f"**RNN Prediction Probabilities:**")
# st.write(f"Negative: {rnn_prediction_prob[0][0]:.2f}")
# st.write(f"Neutral: {rnn_prediction_prob[0][1]:.2f}")
# st.write(f"Positive: {rnn_prediction_prob[0][2]:.2f}")
else:
st.write("Please enter a review text to get predictions.")
# Add some style to the UI
st.markdown("""
<style>
.reportview-container {
background: #f0f2f6;
}
.sidebar .sidebar-content {
background: #f0f2f6;
}
.stButton>button {
color: #ffffff;
background-color: #4CAF50;
border-radius: 8px;
padding: 10px;
border: none;
cursor: pointer;
}
.stButton>button:hover {
background-color: #red;
}
.stTextArea>label {
font-size: 20px;
color: #4CAF50;
}
</style>
""", unsafe_allow_html=True)
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