app.py

# import streamlit as st
# import pandas as pd
# import plotly.graph_objects as go
# import joblib
# import nltk
# nltk.download('vader_lexicon')
# from nltk.sentiment.vader import SentimentIntensityAnalyzer
# from lime.lime_text import LimeTextExplainer

# # Add hero image
# st.image('images/hero.jpg', use_column_width=True)

# # Load time series data
# df_resampled = pd.read_csv('data/reviews_processed.csv')
# df_resampled.set_index('date', inplace=True)

# # Add app title
# st.title('Time Series Analysis Customer Reviews for Sandbar')

# # User input for the time frame selection and sentiment analysis
# st.subheader('Select a Time Frame')
# time_frame = st.slider('Time Frame (3-Month Moving Average)',
#                        min_value=-1,
#                        max_value=(len(df_resampled)),
#                        step=3)

# # Resample the data according to user-selected time frame
# resampled_data = df_resampled['stars'].rolling(window=time_frame).mean()

# # Plot the time series data
# fig = go.Figure()

# fig.add_trace(go.Scatter(
#     x=df_resampled.index,
#     y=df_resampled['stars'],
#     mode='lines',
#     name='Monthly Average'
# ))

# # Add 3-Month Moving Average
# fig.add_trace(go.Scatter(
#     x=df_resampled.index,
#     y=resampled_data,
#     mode='lines',
#     name=f'{time_frame}-Month Moving Average'
# ))

# # Add title and labels
# fig.update_layout(
#     title=f'Average Star Rating Over Time with {time_frame}-Monthly Moving Average',
#     xaxis_title='Time',
#     yaxis_title='Average Star Rating',
# )

# # Display plot
# st.plotly_chart(fig, use_container_width=True)

# # Sentiment Analysis

# # Load Naive Bayes model and TF-IDF Vectorizer
# naiveBayesModel = joblib.load('models/naive_bayes_model.pkl')
# vectorizerTFIDF = joblib.load('models/vectorizer.pkl')

# # Instantiate VADER
# vader = SentimentIntensityAnalyzer()

# # Instantiate the LIME text explainer
# lime_explainer = LimeTextExplainer(class_names=['Positive', 'Neutral', 'Negative'])

# # Function to get the predictions from Naive Bayes and VADER
# def get_model_prediction(text):

#     # VADER prediction
#     vader_scores = vader.polarity_scores(text)
#     vader_sentiment = max(vader_scores, key=vader_scores.get)

#     # Naive Bayes
#     naiveBayesVectorizer = vectorizerTFIDF.transform([text])
#     naiveBayesPrediction = naiveBayesModel.predict(naiveBayesVectorizer)[0]

#     return {
#         'VADER': vader_sentiment,
#         'Naive Bayes': naiveBayesPrediction
#     }, vader_scores

# # Function to predict probabilities and explain using LIME
# def predict_proba(texts):

#     return naiveBayesModel.predict_proba(vectorizerTFIDF.transform(texts))

# # Sentiment Analysis
# st.header('Sentiment Analysis')

# # User text input
# user_input = st.text_area('Enter text for sentiment analysis')

# # Predict sentiment
# if st.button('Analyze'):

#     if user_input:

#         # Get predictions
#         predictions, vaderScores = get_model_prediction(text=user_input)

#         # Display predictions
#         st.write(f'VADER Sentiment: {predictions["VADER"]}')
#         st.write(f'Naive Bayes Sentiment: {predictions["Naive Bayes"]}')

#         # Visualize model confidence
#         fig = go.Figure()

#         # Add VADER confidence
#         fig.add_trace(go.Bar(
#             x=list(vaderScores.keys()),
#             y=list(vaderScores.value()),
#             name='VADER Scores'
#         ))

#         # Add Naive Bayes confidence
#         fig.add_trace(go.Bar(
#             x=['Naive Bayes'],
#             y=[1 if predictions['Naive Bayes'] == 'Positive' else 0],
#             name='Naive Bayes Scores'
#         ))

#         fig.update_layout(
#             title = 'Model Sentiment Comparison',
#             xaxis_title = 'Models',
#             yaxis_title = 'Confidence Levels',
#         )

#         st.plotly_chart(fig, use_container_width=True)

#         # LIME
#         st.subheader('LIME Explanation fo Naive Bayes')
#         explainer = lime_explainer.explain_instance(
#             user_input,
#             predict_proba,
#             num_features=10
#         )

#         explainer_html = explainer.as_html()

#         # Display LIME explanation
#         st.components/v1.html(explainer_html)

#     else:
#         st.write('Please provide text to analyze')

import streamlit as st
import pandas as pd
import plotly.graph_objects as go
import joblib
import nltk
nltk.download('vader_lexicon')
from nltk.sentiment.vader import SentimentIntensityAnalyzer
from lime.lime_text import LimeTextExplainer

# Add a hero image
st.image('images/hero.jpg', use_column_width=True)

# Load time series data
df_resampled = pd.read_csv('data/reviews_processed.csv')
df_resampled.set_index('date', inplace=True)

# Add app title
st.title('Time Series Analysis Customer Review for Sandbar')

# User input for the time frame selection and sentiment analysis (positive/negative)
st.subheader('Select a Time Frame')
time_frame = st.slider('Time Frame (Months)',
                       min_value=1,
                       max_value=(len(df_resampled)),
                       step=1)

# Resample data according to the user-selected time frame
resampled_data = df_resampled['stars'].rolling(window=time_frame).mean()

# Plot the time series data
fig = go.Figure()

fig.add_trace(go.Scatter(
    x=df_resampled.index,
    y=df_resampled['stars'],
    mode='lines',
    name='Monthly Average'
))

# Add moving averages to the plot as lines on top
fig.add_trace(go.Scatter(
    x=df_resampled.index,
    y=resampled_data,
    mode='lines',
    name=f'{time_frame}-Month Moving Average'
))

# Add title and labels to the plot
fig.update_layout(
    title = f'Average Star Rating Over Time with {time_frame}-Monthly Moving Average',
    xaxis_title = 'Time',
    yaxis_title = 'Average Star Rating',
)

# Show the plot
st.plotly_chart(fig, use_container_width=True)

# Load the Naive Bayes model and TF-IDF vectors for sentiment analysis
naiveBayesModel = joblib.load('models/naive_bayes_model.pkl')
vectorizerTFIdf = joblib.load('models/vectorizer.pkl')  # Make sure the filename matches
print(f"IDF attribute present: {'idf_' in dir(vectorizerTFIdf)}")  # Should print True


# Instantiate VADER
vader = SentimentIntensityAnalyzer()

# Initialize LIME text explainer
lime_explainer = LimeTextExplainer(class_names=['Positive', 'Neutral', 'Negative'])

# Function to get the predictions from VADER and Naive Bayes
def get_model_prediction(text):
    
    # VADER prediction
    vader_scores = vader.polarity_scores(text)
    vader_sentiment = max(vader_scores, key=vader_scores.get)
    
    # Naive Bayes prediction
    naiveBayesVectorizer = vectorizerTFIdf.transform([text])
    naiveBayesPrediction = naiveBayesModel.predict(naiveBayesVectorizer)[0]
    
    return {
        'VADER': vader_sentiment,
        'Naive Bayes': naiveBayesPrediction
    }, vader_scores
    
# Function to predict probabilities and explain them with LIME
def predict_proba(texts):
    return naiveBayesModel.predict_proba(vectorizerTFIdf.transform(texts))

# Sentiment analysis with LIME
st.header('Sentiment Analysis')

# User text input
user_input = st.text_area('Enter text for sentiment analysis:')

# Predict sentiment
if st.button('Analyze'):
    
    if user_input:
        
        # Get predictions
        predictions, vaderScores = get_model_prediction(user_input)
        
        # Display the predictions
        st.write(f'VADER Sentiment: {predictions["VADER"]}')
        st.write(f'Naive Bayes Sentiment: {predictions["Naive Bayes"]}')
        
        # Visualize model confidence
        fig = go.Figure()
        
        # Add VADER confidence
        fig.add_trace(go.Bar(
            x=list(vaderScores.keys()),
            y=list(vaderScores.values()),
            name='VADER Scores'
        ))
        
        # Add Naive Bayes confidence
        fig.add_trace(go.Bar(
            x=['Naive Bayes'],
            y=[1 if predictions['Naive Bayes'] == 'Positive' else 0],
            name='Naive Bayes Score'
        ))
        
        fig.update_layout(
            title = 'Model Sentiment Comparison',
            xaxis_title = 'Models',
            yaxis_title = 'Confidence Levels',
        )
        
        st.plotly_chart(fig, use_container_width=True)
        
        # LIME Explainer
        st.subheader('LIME Explanation for Naive Bayes')
        exp = lime_explainer.explain_instance(
            user_input,
            predict_proba,
            num_features=10
        )
        
        exp_html = exp.as_html()       
        
        # Display LIME Explanation
        st.components.v1.html(exp_html)
        
    else:
        st.write('Please provide text for sentiment analysis')