app.py

import streamlit as st
import pandas as pd
import numpy as np
import re
import matplotlib.pyplot as plt
import seaborn as sns
import nltk
from nltk.corpus import stopwords
from nltk.stem.snowball import SnowballStemmer
import pickle
from transformers import pipeline as hf_pipeline
from sklearn.utils.multiclass import type_of_target
import io
import base64
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.pipeline import Pipeline
from sklearn.multiclass import OneVsRestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import roc_auc_score, accuracy_score, classification_report
from textblob import TextBlob
import warnings
from sklearn.metrics import precision_score, recall_score, f1_score, confusion_matrix
from sklearn.metrics import roc_curve

warnings.filterwarnings('ignore')

# Download required NLTK resources
try:
    nltk.data.find('corpora/stopwords')
except LookupError:
    nltk.download('stopwords')

# Initialize the stemmer
stemmer = SnowballStemmer('english')
stop_words_set = set(stopwords.words('english'))


# Text preprocessing functions
def remove_stopwords(text):
    return " ".join([word for word in str(text).split() if word.lower() not in stop_words_set])


def train_lightweight_model(data, text_column, label_column):
    """
    Train a lightweight model for toxicity detection

    Args:
        data: DataFrame containing the data
        text_column: Column name for text data
        label_column: Column name for label data

    Returns:
        Trained model and vectorizer
    """
    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import LogisticRegression
    from sklearn.pipeline import Pipeline

    # Preprocess text
    data['processed_text'] = data[text_column].apply(preprocess_text)

    # Create a pipeline with TF-IDF and Logistic Regression
    model = Pipeline([
        ('tfidf', TfidfVectorizer(max_features=5000, ngram_range=(1, 2))),
        ('clf', LogisticRegression(random_state=42, max_iter=1000))
    ])

    # Train the model
    model.fit(data['processed_text'], data[label_column])

    return model


def load_bert_model():
    """
    Load a pre-trained BERT model for sentiment analysis

    Returns:
        Loaded model
    """
    try:
        # Load sentiment analysis model from HuggingFace
        sentiment_analyzer = hf_pipeline("sentiment-analysis")
        st.success("BERT model loaded successfully!")
        return sentiment_analyzer
    except Exception as e:
        st.error(f"Error loading BERT model: {e}")
        return None


def clean_text(text):
    text = str(text).lower()
    text = re.sub(r"what's", "what is ", text)
    text = re.sub(r"\'s", " ", text)
    text = re.sub(r"\'ve", " have ", text)
    text = re.sub(r"can't", "can not ", text)
    text = re.sub(r"n't", " not ", text)
    text = re.sub(r"i'm", "i am ", text)
    text = re.sub(r"\'re", " are ", text)
    text = re.sub(r"\'d", " would ", text)
    text = re.sub(r"\'ll", " will ", text)
    text = re.sub(r"\'scuse", " excuse ", text)
    text = re.sub(r'\W', ' ', text)  # Remove non-word characters
    text = re.sub(r'\s+', ' ', text).strip()  # Remove extra spaces
    return text


def stemming(sentence):
    return " ".join([stemmer.stem(word) for word in str(sentence).split()])


def preprocess_text(text):
    text = remove_stopwords(text)
    text = clean_text(text)
    text = stemming(text)
    return text


# Function to get sentiment
def get_sentiment(text):
    score = TextBlob(text).sentiment.polarity
    if score > 0:
        return "Positive", score
    elif score < 0:
        return "Negative", score
    else:
        return "Neutral", score


# Function to moderate text based on toxicity
def moderate_text(text, predictions, threshold_moderate=0.5, threshold_delete=0.8):
    # If binary classification, only check the 'toxic' probability (index 1)
    if len(predictions) == 2:
        toxic_prob = predictions[1]
        if toxic_prob >= threshold_delete:
            return "*** COMMENT DELETED DUE TO HIGH TOXICITY ***", "delete"
        elif toxic_prob >= threshold_moderate:
            # List of potentially toxic words to censor
            toxic_words = ["stupid", "idiot", "dumb", "hate", "sucks", "terrible",
                           "awful", "garbage", "trash", "pathetic", "ridiculous"]

            words = text.split()
            moderated_words = []

            for word in words:
                # Clean word for comparison
                clean_word = re.sub(r'[^\w\s]', '', word.lower())

                # Check if the word is in the toxic words list
                if clean_word in toxic_words:
                    # Replace with a more neutral placeholder
                    moderated_words.append("[inappropriate]")
                else:
                    moderated_words.append(word)

            return " ".join(moderated_words), "moderate"
        else:
            return text, "keep"
    else:
        # Multi-label: check all classes
        if any(pred >= threshold_delete for pred in predictions):
            return "*** COMMENT DELETED DUE TO HIGH TOXICITY ***", "delete"
        elif any(pred >= threshold_moderate for pred in predictions):
            # List of potentially toxic words to censor
            toxic_words = ["stupid", "idiot", "dumb", "hate", "sucks", "terrible",
                           "awful", "garbage", "trash", "pathetic", "ridiculous"]

            words = text.split()
            moderated_words = []

            for word in words:
                # Clean word for comparison
                clean_word = re.sub(r'[^\w\s]', '', word.lower())

                # Check if the word is in the toxic words list
                if clean_word in toxic_words:
                    # Replace with a more neutral placeholder
                    moderated_words.append("[inappropriate]")
                else:
                    moderated_words.append(word)

            return " ".join(moderated_words), "moderate"
        else:
            return text, "keep"


# Function to train and save the model
def train_model(X_train, y_train, model_type='logistic_regression'):
    st.write("Training model...")

    # Ensure `y_train` has 6 columns
    label_columns = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']

    # Create missing columns if they don't exist
    for col in label_columns:
        if col not in y_train.columns:
            y_train[col] = 0

    # Ensure columns are in the right order
    y_train = y_train[label_columns]

    if model_type == 'logistic_regression':
        pipeline = Pipeline([
            ('tfidf', TfidfVectorizer(stop_words='english', max_features=50000)),
            ('clf', OneVsRestClassifier(LogisticRegression(max_iter=1000), n_jobs=-1))
        ])
    else:  # Naive Bayes
        pipeline = Pipeline([
            ('tfidf', TfidfVectorizer(stop_words='english', max_features=50000)),
            ('clf', OneVsRestClassifier(MultinomialNB(), n_jobs=-1))
        ])

    pipeline.fit(X_train, y_train)

    return pipeline




def evaluate_model(pipeline, X_test, y_test):
    """
    Evaluates the given trained pipeline on test data.
    Returns:
        accuracy: Accuracy score
        roc_auc: ROC AUC score
        predictions: Predicted labels
        pred_probs: Predicted probabilities
        fpr: False Positive Rate array (for ROC curve)
        tpr: True Positive Rate array (for ROC curve)
    """

    # Get predictions and prediction probabilities
    predictions = pipeline.predict(X_test)
    pred_probs = pipeline.predict_proba(X_test)

    if isinstance(pred_probs, list) and len(pred_probs) == 1:
        pred_probs = pred_probs[0]  # Handle list with 1 array

    # Ensure predictions format matches y_test
    y_type = type_of_target(y_test)
    pred_type = type_of_target(predictions)

    if y_type != pred_type:
        if y_type == "multilabel-indicator" and pred_type == "binary":
            # Expand binary predictions to multilabel shape
            predictions = np.array([[pred] * y_test.shape[1] for pred in predictions])
        elif y_type == "binary" and pred_type == "multilabel-indicator":
            # Collapse multilabel predictions to binary
            predictions = predictions[:, 0]

    # Calculate accuracy
    accuracy = accuracy_score(y_test, predictions)

    # Calculate ROC AUC
    try:
        if len(y_test.shape) > 1 and y_test.shape[1] > 1:
            # Multi-label case
            roc_auc_sum = 0
            valid_labels = 0
            for i in range(y_test.shape[1]):
                try:
                    roc_auc_sum += roc_auc_score(y_test.iloc[:, i], pred_probs[:, i])
                    valid_labels += 1
                except Exception:
                    continue
            roc_auc = roc_auc_sum / valid_labels if valid_labels > 0 else 0.0
        else:
            # Binary case
            roc_auc = roc_auc_score(y_test, pred_probs[:, 1] if pred_probs.ndim > 1 and pred_probs.shape[1] > 1 else pred_probs)
    except Exception as e:
        print(f"Warning: Could not compute ROC AUC - {e}")
        roc_auc = 0.0

    # Calculate FPR, TPR for ROC curve (only for binary classification)
    try:
        if len(y_test.shape) == 1 or (len(y_test.shape) > 1 and y_test.shape[1] == 1):
            fpr, tpr, _ = roc_curve(
                y_test,
                pred_probs[:, 1] if pred_probs.ndim > 1 and pred_probs.shape[1] > 1 else pred_probs
            )
        else:
            fpr, tpr = None, None  # ROC Curve not available for multilabel
    except Exception as e:
        print(f"Warning: Could not compute ROC Curve - {e}")
        fpr, tpr = None, None

    return accuracy, roc_auc, predictions, pred_probs, fpr, tpr



# Function to create a download link for the trained model
def get_model_download_link(model, filename):
    model_bytes = pickle.dumps(model)
    b64 = base64.b64encode(model_bytes).decode()
    href = f'<a href="data:file/pickle;base64,{b64}" download="{filename}">Download Trained Model</a>'
    return href


# Function to plot toxicity distribution
def plot_toxicity_distribution(df, toxicity_columns):
    fig, ax = plt.subplots(figsize=(10, 6))

    x = df[toxicity_columns].sum()
    sns.barplot(x=x.index, y=x.values, alpha=0.8, palette='viridis', ax=ax)

    plt.title('Toxicity Distribution')
    plt.ylabel('Count')
    plt.xlabel('Toxicity Category')
    plt.xticks(rotation=45)

    return fig


# Function to provide sample data format
def show_sample_data_format():
    st.subheader("Sample Data Format")

    # Create sample dataframe
    sample_data = {
        'comment_text': [
            "This is a normal comment.",
            "This is a toxic comment you idiot!",
            "You're all worthless and should die.",
            "I respectfully disagree with your point."
        ],
        'toxic': [0, 1, 1, 0],
        'severe_toxic': [0, 0, 1, 0],
        'obscene': [0, 1, 0, 0],
        'threat': [0, 0, 1, 0],
        'insult': [0, 1, 1, 0],
        'identity_hate': [0, 0, 0, 0]
    }

    sample_df = pd.DataFrame(sample_data)
    st.dataframe(sample_df)

    # Create download link for sample data
    csv = sample_df.to_csv(index=False)
    b64 = base64.b64encode(csv.encode()).decode()
    href = f'<a href="data:file/csv;base64,{b64}" download="sample_toxic_data.csv">Download Sample CSV</a>'
    st.markdown(href, unsafe_allow_html=True)

    st.info("""
    Your CSV file should contain:
    1. A column with comment text
    2. One or more columns with binary values (0 or 1) for each toxicity category
    """)


# Function to validate dataset
def validate_dataset(df, comment_column, toxicity_columns):
    issues = []

    # Check if comment column exists
    if comment_column not in df.columns:
        issues.append(f"Comment column '{comment_column}' not found in the dataset")

    # Check if toxicity columns exist
    missing_columns = [col for col in toxicity_columns if col not in df.columns]
    if missing_columns:
        issues.append(f"Missing toxicity columns: {', '.join(missing_columns)}")

    # Check if values in toxicity columns are valid (0 or 1)
    for col in toxicity_columns:
        if col in df.columns:
            # Check for non-numeric values
            if not pd.api.types.is_numeric_dtype(df[col]):
                issues.append(f"Column '{col}' contains non-numeric values")
            else:
                # Check for values other than 0 and 1
                invalid_values = df[col].dropna().apply(lambda x: x not in [0, 1, 0.0, 1.0])
                if invalid_values.any():
                    issues.append(f"Column '{col}' contains values other than 0 and 1")

    # Check for empty data
    if df.empty:
        issues.append("Dataset is empty")
    elif df[comment_column].isna().all():
        issues.append("Comment column contains no data")

    return issues


# Function to extract predictions from model output
def extract_predictions(predictions_proba, toxicity_categories):
    """
    Helper function to extract probabilities from model output,
    handling different output formats.
    """
    # Debug information
    if st.session_state.debug_mode:
        st.write(f"Predictions type: {type(predictions_proba)}")
        st.write(
            f"Predictions shape/length: {np.shape(predictions_proba) if hasattr(predictions_proba, 'shape') else len(predictions_proba)}")

    # Case 1: List of arrays with one element per toxicity category
    if isinstance(predictions_proba, list) and len(predictions_proba) == len(toxicity_categories):
        return [pred_array[:, 1][0] if pred_array.shape[1] > 1 else pred_array[0] for pred_array in predictions_proba]

    # Case 2: List with a single array (common for OneVsRestClassifier)
    elif isinstance(predictions_proba, list) and len(predictions_proba) == 1:
        pred_array = predictions_proba[0]
        # If it's a 2D array with number of columns equal to number of categories
        if len(pred_array.shape) == 2 and pred_array.shape[1] == len(toxicity_categories):
            return pred_array[0]  # Return first row, which contains all probabilities
        # If it's a 2D array with 2 columns per category (common binary classifier output)
        elif len(pred_array.shape) == 2 and pred_array.shape[1] == 2:
            return np.array([pred_array[0, 1]])

    # Case 3: Direct numpy array
    elif isinstance(predictions_proba, np.ndarray):
        # If it's already the right shape
        if len(predictions_proba.shape) == 2 and predictions_proba.shape[1] == len(toxicity_categories):
            return predictions_proba[0]
        # If it's a 2D array with two columns (binary classification)
        elif len(predictions_proba.shape) == 2 and predictions_proba.shape[1] == 2:
            # For binary classification, return the probability of positive class
            return np.array([predictions_proba[0, 1]])

    # If prediction format isn't recognized, return a repeated array of single probability
    # This handles the case where we only have one prediction but need to repeat it
    if isinstance(predictions_proba, list) and len(predictions_proba) == 1:
        single_prob = predictions_proba[0]
        if hasattr(single_prob, 'shape') and len(single_prob.shape) == 2 and single_prob.shape[1] == 2:
            # Take positive class probability and repeat for all categories
            return np.full(len(toxicity_categories), single_prob[0, 1])

    # Last resort fallback
    st.warning(f"Unexpected prediction format. Creating default predictions.")
    return np.zeros(len(toxicity_categories))


def display_classification_result(result):
    st.subheader("Classification Result")

    # Show original and moderated text side by side
    col1, col2 = st.columns(2)
    with col1:
        st.markdown("**Original Text**")
        st.code(result["original_text"], language="text")
    with col2:
        st.markdown("**Moderated Text**")
        st.code(result["moderated_text"], language="text")

    # Show action with color
    action = result["action"]
    if action == "keep":
        st.success("✅ This comment is allowed (Non-toxic).")
    elif action == "moderate":
        st.warning("⚠️ This comment is moderated (Potentially toxic).")
    elif action == "delete":
        st.error("🚫 This comment is deleted (Highly toxic).")

    # Show toxicity scores
    st.markdown("**Toxicity Scores:**")
    score_cols = st.columns(len(result["toxicity_scores"]))
    for i, (label, score) in enumerate(result["toxicity_scores"].items()):
        score_cols[i].metric(label.capitalize(), f"{score:.2%}")

    # Show sentiment if available
    if "sentiment" in result:
        st.markdown("**Sentiment Analysis:**")
        st.info(f"{result['sentiment']['label']} (score: {result['sentiment']['score']:.2%})")


def moderate_comment(comment, model, sentiment_model=None):
    """
    Moderate a single comment using the trained model and optionally BERT sentiment analysis.
    
    Args:
        comment: The comment text to moderate
        model: The trained model to use for toxicity detection
        sentiment_model: Optional BERT model for sentiment analysis
        
    Returns:
        Dictionary containing moderation results
    """
    # Preprocess the comment
    processed_text = preprocess_text(comment)
    
    # Get model predictions
    predictions = model.predict_proba([processed_text])[0]
    
    # Get sentiment if BERT model is available
    sentiment = None
    if sentiment_model:
        sentiment = sentiment_model(comment)[0]
    
    # Moderate the text
    moderated_text, action = moderate_text(comment, predictions)
    
    # Prepare result
    result = {
        "original_text": comment,
        "moderated_text": moderated_text,
        "action": action,
        "toxicity_scores": {}
    }
    
    # Handle both binary and multi-class predictions
    if len(predictions) == 2:  # Binary classification
        result["toxicity_scores"] = {
            "toxic": float(predictions[1]),  # Probability of positive class
            "non_toxic": float(predictions[0])  # Probability of negative class
        }
    else:  # Multi-class classification
        # Define the toxicity categories
        categories = ["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]
        
        # Add scores for each category that exists in the predictions
        for i, category in enumerate(categories):
            if i < len(predictions):
                result["toxicity_scores"][category] = float(predictions[i])
    
    if sentiment:
        result["sentiment"] = {
            "label": sentiment["label"],
            "score": float(sentiment["score"])
        }
    
    return result


# --- Bias Detection Module ---
def detect_subgroup(text):
    gender_keywords = ["he", "she", "him", "her", "man", "woman", "boy", "girl", "male", "female"]
    ethnicity_keywords = [
        "asian", "black", "white", "hispanic", "latino", "indian", "african", "european", "arab", "jewish", "muslim", "christian"
    ]
    text_lower = text.lower()
    subgroups = set()
    if any(word in text_lower for word in gender_keywords):
        subgroups.add("gender")
    if any(word in text_lower for word in ethnicity_keywords):
        subgroups.add("ethnicity")
    return list(subgroups)

def bias_report(X, y_true, y_pred, text_column_name):
    # X: DataFrame with text column
    # y_pred: predicted labels (same shape as y_true)
    # y_true: true labels (same shape as y_pred)
    # text_column_name: name of the text column
    results = []
    for idx, row in X.iterrows():
        subgroups = detect_subgroup(row[text_column_name])
        if subgroups:
            for subgroup in subgroups:
                results.append({
                    "subgroup": subgroup,
                    "is_toxic": int(y_pred[idx].sum() > 0) if len(y_pred.shape) > 1 else int(y_pred[idx] > 0)
                })
    if not results:
        return "No sensitive subgroups detected in the evaluation set."
    df = pd.DataFrame(results)
    report = ""
    for subgroup in df["subgroup"].unique():
        total = (df["subgroup"] == subgroup).sum()
        toxic = df[(df["subgroup"] == subgroup) & (df["is_toxic"] == 1)].shape[0]
        rate = toxic / total if total > 0 else 0
        report += f"- **{subgroup.capitalize()}**: {toxic}/{total} ({rate:.1%}) flagged as toxic\n"
    return report


# Streamlit app
def main():
    st.set_page_config(
        page_title="Toxic Comment Classifier",
        page_icon="🧊",
        layout="wide",
        initial_sidebar_state="expanded",
    )

    col1, col2 = st.columns([1, 4])  # Adjust the ratio as needed

    with col1:
        st.image("logo.jpeg", width=100)  # Smaller width fits better

    with col2:
        st.title("Toxic Comment Classifier")


    # Initialize session state variables if they don't exist
    if 'data' not in st.session_state:
        st.session_state.data = None
    if 'model' not in st.session_state:
        st.session_state.model = None
    if 'vectorizer' not in st.session_state:
        st.session_state.vectorizer = None
    if 'predictions' not in st.session_state:
        st.session_state.predictions = None
    if 'lightweight_model' not in st.session_state:
        st.session_state.lightweight_model = None
    if 'bert_model' not in st.session_state:
        st.session_state.bert_model = None

    # Create sidebar navigation
    st.sidebar.title("Navigation")
    page = st.sidebar.radio(
        "Select a page",
        ["Home", "Data Preprocessing", "Model Training", "Model Evaluation", "Prediction", "Visualization"]
    )

    # Home page
    if page == "Home":

        st.header("Home")
        st.write("""
        Welcome to the Toxic Comment Classifier application. This tool helps you to:
        1. Upload and preprocess data
        2. Train a machine learning model to detect toxic comments
        3. Evaluate model performance
        4. Make predictions on new data
        5. Visualize results

        Please use the sidebar navigation to get started.
        """)

        # Add option to load BERT sentiment model
        if st.sidebar.checkbox("Use BERT for Sentiment Analysis"):
            st.subheader("BERT-Based Sentiment Analysis")
            st.write("This option uses a pre-trained BERT model for advanced sentiment analysis.")

            if st.button("Load BERT Model"):
                with st.spinner("Loading BERT model..."):
                    st.session_state.bert_model = load_bert_model()
                    st.write("DEBUG: bert_model in session_state after loading:", st.session_state.bert_model)

        # Sample data section
        st.subheader("Sample Data Format")
        show_sample_data_format()

        # Single comment moderation
        st.subheader("Try Comment Moderation")
        comment = st.text_area("Enter a comment to moderate:")

        col1, col2 = st.columns(2)
        with col1:
            use_default_model = st.checkbox("Use built-in model for demo", value=True)

        with col2:
            use_bert = st.checkbox("Use BERT model for sentiment (if loaded)", value=False)

        if st.button("Moderate Comment"):
            if comment:
                with st.spinner("Analyzing comment..."):
                    st.write("DEBUG: bert_model in session_state before use:", st.session_state.bert_model)
                    sentiment_model = st.session_state.bert_model if use_bert and st.session_state.bert_model is not None else None

                    if use_default_model or st.session_state.model or st.session_state.lightweight_model:
                        model_to_use = None
                        if st.session_state.model:
                            model_to_use = st.session_state.model
                        elif st.session_state.lightweight_model:
                            model_to_use = st.session_state.lightweight_model

                        result = moderate_comment(comment, model_to_use, sentiment_model)

                        display_classification_result(result)
                    else:
                        st.error("No model available. Please train a model first or enable the demo model.")
            else:
                st.warning("Please enter a comment to moderate.")

    # Data Preprocessing page
    elif page == "Data Preprocessing":
        st.header("Data Preprocessing")

        # File upload
        st.subheader("Upload Dataset")
        uploaded_file = st.file_uploader("Choose a CSV file", type="csv")

        if uploaded_file is not None:
            try:
                # Load data
                data = pd.read_csv(uploaded_file)

                # Display raw data
                st.subheader("Raw Data")
                st.write(data.head())

                # Validate the dataset
                validation_result = validate_dataset(data, 'comment_text', ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate'])

                if not validation_result:  # Empty list means no issues
                    st.success("Dataset is valid!")

                    # Store data in session state
                    st.session_state.data = data

                    # Data cleaning
                    st.subheader("Data Cleaning")
                    st.write("Select columns to include in the analysis:")

                    # Get all columns
                    all_columns = data.columns.tolist()
                    default_selected = ["comment_text", "toxic", "severe_toxic", "obscene", "threat", "insult",
                                        "identity_hate"]
                    default_selected = [col for col in default_selected if col in all_columns]

                    selected_columns = st.multiselect(
                        "Select columns",
                        options=all_columns,
                        default=default_selected
                    )

                    if selected_columns:
                        # Filter data by selected columns
                        filtered_data = data[selected_columns]

                        # Display cleaned data
                        st.subheader("Filtered Data")
                        st.write(filtered_data.head())

                        # Display data statistics
                        st.subheader("Data Statistics")
                        st.write(filtered_data.describe())

                        # Check for missing values
                        st.subheader("Missing Values")
                        missing_values = filtered_data.isnull().sum()
                        st.write(missing_values)

                        # Handle missing values if any
                        if missing_values.sum() > 0:
                            st.warning("There are missing values in the dataset.")

                            if st.button("Handle Missing Values"):
                                # Fill missing text with empty string
                                text_columns = [col for col in selected_columns if filtered_data[col].dtype == 'object']
                                for col in text_columns:
                                    filtered_data[col] = filtered_data[col].fillna("")

                                # Fill missing numerical values with 0
                                numerical_columns = [col for col in selected_columns if
                                                     filtered_data[col].dtype != 'object']
                                for col in numerical_columns:
                                    filtered_data[col] = filtered_data[col].fillna(0)

                                st.success("Missing values handled!")
                                st.write(filtered_data.isnull().sum())

                        # Text preprocessing
                        st.subheader("Text Preprocessing")

                        # Select text column
                        text_columns = [col for col in selected_columns if filtered_data[col].dtype == 'object']

                        if text_columns:
                            text_column = st.selectbox("Select text column for preprocessing", text_columns)

                            # Show sample of original text
                            st.write("Sample original text:")
                            sample_texts = filtered_data[text_column].head(3).tolist()
                            for i, text in enumerate(sample_texts):
                                st.text(f"Text {i + 1}: {text[:200]}...")

                            # Preprocess text
                            if st.button("Preprocess Text"):
                                with st.spinner("Preprocessing text..."):
                                    filtered_data['processed_text'] = filtered_data[text_column].apply(preprocess_text)

                                    # Show sample of preprocessed text
                                    st.write("Sample preprocessed text:")
                                    sample_preprocessed = filtered_data['processed_text'].head(3).tolist()
                                    for i, text in enumerate(sample_preprocessed):
                                        st.text(f"Processed Text {i + 1}: {text[:200]}...")

                                    # Store preprocessed data
                                    st.session_state.data = filtered_data
                                    st.success("Text preprocessing completed!")
                        else:
                            st.warning("No text columns found in the selected columns.")
                    else:
                        st.warning("Please select at least one column.")
                else:
                    st.error(f"Dataset validation failed: {validation_result['reason']}")
                    st.warning("Please upload a valid dataset.")

            except Exception as e:
                st.error(f"Error loading data: {e}")
                st.warning("Please upload a valid CSV file.")
        else:
            st.info("Please upload a CSV file to begin preprocessing.")

    # Model Training page
    elif page == "Model Training":
        st.header("Model Training")

        # Check if data is available
        if st.session_state.data is not None:
            # Display data info
            st.subheader("Dataset Information")
            st.write(f"Number of samples: {len(st.session_state.data)}")

            if 'processed_text' in st.session_state.data.columns:
                st.write("Text preprocessing: Done")
            else:
                st.warning("Text preprocessing is not done. Please preprocess the data first.")

            # Model training options
            st.subheader("Training Options")

            # Select target column
            numerical_columns = [col for col in st.session_state.data.columns if
                                 st.session_state.data[col].dtype != 'object']

            if numerical_columns:
                target_column = st.selectbox("Select target column", numerical_columns)

                # Set training parameters
                st.write("Training Parameters:")
                col1, col2 = st.columns(2)

                with col1:
                    test_size = st.slider("Test Size", 0.1, 0.5, 0.2, 0.05)

                with col2:
                    random_state = st.number_input("Random State", 0, 100, 42, 1)

                # Model selection
                model_type = st.radio(
                    "Select model type",
                    ["Standard Model", "Lightweight Model"]
                )

                # Train model button
                if st.button("Train Model"):
                    with st.spinner("Training model..."):
                        # Check if processed text is available
                        if 'processed_text' in st.session_state.data.columns:
                            try:
                                if model_type == "Standard Model":
                                    # Train standard model
                                    X_train = st.session_state.data['processed_text']
                                    y_train = st.session_state.data[[target_column]]
                                    model = train_model(X_train, y_train, 'logistic_regression')

                                    # Store model in session state
                                    st.session_state.model = model
                                    st.session_state.vectorizer = None  # Vectorizer is part of the pipeline

                                    st.success("Model training completed!")
                                else:
                                    # Train lightweight model
                                    lightweight_model = train_lightweight_model(
                                        st.session_state.data,
                                        'processed_text',
                                        target_column
                                    )

                                    # Store lightweight model in session state
                                    st.session_state.lightweight_model = lightweight_model

                                    st.success("Lightweight model training completed!")

                            except Exception as e:
                                st.error(f"Error training model: {e}")
                        else:
                            st.error("Processed text not found. Please preprocess the data first.")
            else:
                st.warning("No numerical columns found in the dataset. Please ensure you have target columns.")
        else:
            st.info("Please upload and preprocess data before training a model.")

    # Model Evaluation page
    elif page == "Model Evaluation":
        st.header("Model Evaluation")

        # Check if model is available
        model_available = st.session_state.model is not None or st.session_state.lightweight_model is not None

        if model_available:
            # Display model info
            st.subheader("Model Information")
            if st.session_state.model is not None:
                st.write("Standard model is trained and ready.")
            if st.session_state.lightweight_model is not None:
                st.write("Lightweight model is trained and ready.")

            # Select model to evaluate
            model_choice = None
            if st.session_state.model is not None and st.session_state.lightweight_model is not None:
                model_choice = st.radio(
                    "Select model to evaluate",
                    ["Standard Model", "Lightweight Model"]
                )

            # Model evaluation
            st.subheader("Evaluation Options")

            # Set evaluation parameters
            st.write("Evaluation Parameters:")
            col1, col2 = st.columns(2)

            with col1:
                test_size = st.slider("Test Size (Evaluation)", 0.1, 0.5, 0.2, 0.05)

            with col2:
                random_state = st.number_input("Random State (Evaluation)", 0, 100, 42, 1)

            # Target column selection
            if st.session_state.data is not None:
                numerical_columns = [col for col in st.session_state.data.columns if
                                     st.session_state.data[col].dtype != 'object']

                if numerical_columns:
                    target_column = st.selectbox("Select target column for evaluation", numerical_columns)

                    # Evaluate model button
                    if st.button("Evaluate Model"):
                        with st.spinner("Evaluating model..."):
                            try:
                                # Determine which model to evaluate
                                model_to_evaluate = None
                                if model_choice == "Lightweight Model" or (
                                        model_choice is None and st.session_state.model is None):
                                    model_to_evaluate = st.session_state.lightweight_model
                                else:
                                    model_to_evaluate = st.session_state.model

                                # 1️⃣ Evaluate model
                                X_test = st.session_state.data['processed_text']
                                y_test = st.session_state.data[[target_column]]

                                accuracy, roc_auc, predictions, pred_probs, fpr, tpr = evaluate_model(model_to_evaluate,
                                                                                                      X_test, y_test)

                                # 2️⃣ Calculate additional metrics
                                precision = precision_score(y_test, predictions, average='weighted', zero_division=0)
                                recall = recall_score(y_test, predictions, average='weighted', zero_division=0)
                                f1 = f1_score(y_test, predictions, average='weighted', zero_division=0)
                                conf_matrix = confusion_matrix(y_test, predictions)
                                classification_rep = classification_report(y_test, predictions, zero_division=0)

                                # 3️⃣ Display evaluation metrics
                                st.subheader("Evaluation Results")
                                metrics_df = pd.DataFrame({
                                    'Metric': ['Accuracy', 'Precision', 'Recall', 'F1 Score', 'ROC AUC'],
                                    'Value': [accuracy, precision, recall, f1, roc_auc]
                                })
                                st.table(metrics_df)

                                # 4️⃣ Confusion Matrix
                                st.subheader("Confusion Matrix")
                                fig, ax = plt.subplots(figsize=(8, 6))
                                sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', ax=ax, cbar=False,
                                            annot_kws={"size": 16})
                                plt.xlabel('Predicted')
                                plt.ylabel('Actual')
                                plt.title('Confusion Matrix')
                                st.pyplot(fig)

                                # 5️⃣ ROC Curve
                                st.subheader("ROC Curve")
                                if fpr is not None and tpr is not None:
                                    fig, ax = plt.subplots(figsize=(8, 6))
                                    ax.plot(fpr, tpr, label=f'ROC Curve (AUC = {roc_auc:.2f})')
                                    ax.plot([0, 1], [0, 1], 'k--')
                                    ax.set_xlabel('False Positive Rate')
                                    ax.set_ylabel('True Positive Rate')
                                    ax.set_title('ROC Curve')
                                    ax.legend(loc="lower right")
                                    st.pyplot(fig)
                                else:
                                    st.info("ROC curve is not available for multi-label classification.")

                                # 6️⃣ Classification Report
                                st.subheader("Classification Report")
                                st.text(classification_rep)

                            except Exception as e:
                                st.error(f"Error evaluating model: {e}")

                                # 6️⃣ Classification Report
                                st.subheader("Classification Report")
                                st.text(classification_rep)

                                # ✅ Always show bias detection report if possible
                                # Bias Detection Report
                                st.subheader("Bias Detection Report")
                                if 'comment_text' in st.session_state.data.columns:
                                    bias_summary = bias_report(
                                        st.session_state.data[["comment_text"]].reset_index(drop=True),
                                        y_test.reset_index(drop=True),
                                        predictions,
                                        "comment_text"  # Add the text_column_name parameter
                                    )
                                    st.markdown(bias_summary)
                                else:
                                    st.info("No comment_text column found for bias analysis.")



                            except Exception as e:
                                st.error(f"Error evaluating model: {e}")
                else:
                    st.warning("No numerical columns found in the dataset. Please ensure you have target columns.")
            else:
                st.warning("Dataset not available. Please upload and preprocess data first.")

            # Model download
            st.subheader("Model Download")

            # Create download button
            model_to_download = None
            if model_choice == "Lightweight Model" or (model_choice is None and st.session_state.model is None):
                model_to_download = st.session_state.lightweight_model
            else:
                model_to_download = st.session_state.model

            if model_to_download is not None:
                # Determine the appropriate filename based on model type
                filename = "lightweight_model.pkl" if model_choice == "Lightweight Model" or (model_choice is None and st.session_state.model is None) else "standard_model.pkl"
                download_link = get_model_download_link(model_to_download, filename)
                st.markdown(download_link, unsafe_allow_html=True)
        else:
            st.info("Please train a model before evaluation.")

    # Prediction page
    elif page == "Prediction":
        st.header("Prediction")

        # Check if model is available
        model_available = st.session_state.model is not None or st.session_state.lightweight_model is not None

        if model_available:
            # Display model info
            st.subheader("Model Information")
            if st.session_state.model is not None:
                st.write("Standard model is trained and ready.")
            if st.session_state.lightweight_model is not None:
                st.write("Lightweight model is trained and ready.")

            # Select model to use
            model_choice = None
            if st.session_state.model is not None and st.session_state.lightweight_model is not None:
                model_choice = st.radio(
                    "Select model for prediction",
                    ["Standard Model", "Lightweight Model"]
                )

            # Determine which model to use
            model_to_use = None
            if model_choice == "Lightweight Model" or (model_choice is None and st.session_state.model is None):
                model_to_use = st.session_state.lightweight_model
            else:
                model_to_use = st.session_state.model

            # Prediction options
            st.subheader("Make Predictions")

            prediction_type = st.radio(
                "Select prediction type",
                ["Single Comment", "Multiple Comments"]
            )

            # Option to use BERT model
            use_bert = False
            if st.session_state.bert_model is not None:
                use_bert = st.checkbox("Include sentiment analysis with BERT")

            # Single comment prediction
            if prediction_type == "Single Comment":
                comment = st.text_area("Enter a comment to classify:")

                if st.button("Classify Comment"):
                    if comment:
                        with st.spinner("Classifying comment..."):
                            st.write("DEBUG: bert_model in session_state before use:", st.session_state.bert_model)
                            sentiment_model = st.session_state.bert_model if use_bert and st.session_state.bert_model is not None else None
                            result = moderate_comment(comment, model_to_use, sentiment_model)

                            display_classification_result(result)
                    else:
                        st.warning("Please enter a comment to classify.")

            # Multiple comments prediction
            else:
                # File upload for prediction
                uploaded_file = st.file_uploader("Upload a CSV file with comments", type="csv")

                if uploaded_file is not None:
                    try:
                        # Load data
                        pred_data = pd.read_csv(uploaded_file)

                        # Display data
                        st.subheader("Uploaded Data")
                        st.write(pred_data.head())

                        # Select text column
                        text_columns = [col for col in pred_data.columns if pred_data[col].dtype == 'object']

                        if text_columns:
                            text_column = st.selectbox("Select text column for prediction", text_columns)

                            # Batch prediction button
                            if st.button("Run Batch Prediction"):
                                with st.spinner("Classifying comments..."):
                                    try:
                                        # Preprocess text
                                        pred_data['processed_text'] = pred_data[text_column].apply(preprocess_text)

                                        # Run prediction
                                        sentiment_model = st.session_state.bert_model if use_bert else None
                                        predictions = extract_predictions(pred_data, text_column, model_to_use,
                                                                          sentiment_model)

                                        # Store predictions
                                        st.session_state.predictions = predictions

                                        # Display results
                                        st.subheader("Prediction Results")
                                        st.write(predictions.head())

                                        # Summary
                                        st.subheader("Summary")
                                        toxic_count = predictions['is_toxic'].sum()
                                        total_count = len(predictions)
                                        toxic_percentage = (toxic_count / total_count) * 100

                                        st.write(f"Total comments: {total_count}")
                                        st.write(f"Toxic comments: {toxic_count} ({toxic_percentage:.2f}%)")
                                        st.write(
                                            f"Non-toxic comments: {total_count - toxic_count} ({100 - toxic_percentage:.2f}%)")

                                        # Download predictions
                                        if not predictions.empty:
                                            csv = predictions.to_csv(index=False)
                                            b64 = base64.b64encode(csv.encode()).decode()
                                            href = f'<a href="data:file/csv;base64,{b64}" download="predictions.csv">Download Predictions CSV</a>'
                                            st.markdown(href, unsafe_allow_html=True)

                                    except Exception as e:
                                        st.error(f"Error during prediction: {e}")
                        else:
                            st.warning("No text columns found in the uploaded file.")

                    except Exception as e:
                        st.error(f"Error loading data: {e}")
                        st.warning("Please upload a valid CSV file.")
                else:
                    st.info("Please upload a CSV file with comments for batch prediction.")
        else:
            st.info("Please train a model before making predictions.")

    # Visualization page
    elif page == "Visualization":
        st.header("Visualization")

        # Check if data is available
        if st.session_state.data is not None:
            # Data visualization
            st.subheader("Data Visualization")

            # Select visualization type
            viz_type = st.selectbox(
                "Select visualization type",
                ["Toxicity Distribution", "Comment Length Distribution", "Word Cloud", "Correlation Matrix"]
            )

            # Toxicity Distribution
            if viz_type == "Toxicity Distribution":
                # Check if there are label columns
                label_columns = [col for col in st.session_state.data.columns if col in [
                    "toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"
                ]]

                if label_columns:
                    st.write("Toxicity Distribution:")

                    # Plot toxicity distribution
                    fig = plot_toxicity_distribution(st.session_state.data, label_columns)
                    st.pyplot(fig)
                else:
                    st.warning("No toxicity label columns found in the dataset.")

            # Comment Length Distribution
            elif viz_type == "Comment Length Distribution":
                # Check if there is text column
                text_columns = [col for col in st.session_state.data.columns if
                                st.session_state.data[col].dtype == 'object']

                if text_columns:
                    text_column = st.selectbox("Select text column", text_columns)

                    # Calculate comment lengths
                    st.session_state.data['comment_length'] = st.session_state.data[text_column].apply(
                        lambda x: len(str(x)))

                    # Plot comment length distribution
                    fig, ax = plt.subplots(figsize=(10, 6))
                    sns.histplot(st.session_state.data['comment_length'], bins=50, kde=True, ax=ax)
                    plt.xlabel('Comment Length')
                    plt.ylabel('Frequency')
                    plt.title('Comment Length Distribution')
                    st.pyplot(fig)

                    # Statistics
                    st.write("Comment Length Statistics:")
                    st.write(st.session_state.data['comment_length'].describe())
                else:
                    st.warning("No text columns found in the dataset.")

            # Word Cloud
            elif viz_type == "Word Cloud":
                # Check if there is processed text
                if 'processed_text' in st.session_state.data.columns:
                    try:
                        from wordcloud import WordCloud

                        # Create word cloud
                        st.write("Word Cloud Visualization:")

                        # Combine all processed text
                        all_text = ' '.join(st.session_state.data['processed_text'].tolist())

                        # Generate word cloud
                        wordcloud = WordCloud(
                            width=800,
                            height=400,
                            background_color='white',
                            max_words=200,
                            contour_width=3,
                            contour_color='steelblue'
                        ).generate(all_text)

                        # Display word cloud
                        fig, ax = plt.subplots(figsize=(10, 6))
                        ax.imshow(wordcloud, interpolation='bilinear')
                        ax.axis('off')
                        plt.tight_layout()
                        st.pyplot(fig)

                    except ImportError:
                        st.error("WordCloud package is not installed. Please install it to use this feature.")
                else:
                    st.warning("Processed text not found. Please preprocess the data first.")

            # Correlation Matrix
            elif viz_type == "Correlation Matrix":
                # Get numerical columns
                numerical_columns = [col for col in st.session_state.data.columns if
                                     st.session_state.data[col].dtype != 'object']

                if len(numerical_columns) > 1:
                    # Select columns for correlation
                    selected_columns = st.multiselect(
                        "Select columns for correlation matrix",
                        options=numerical_columns,
                        default=[col for col in numerical_columns if col in [
                            "toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"
                        ]]
                    )

                    if selected_columns and len(selected_columns) > 1:
                        # Calculate correlation
                        correlation = st.session_state.data[selected_columns].corr()

                        # Plot correlation matrix
                        fig, ax = plt.subplots(figsize=(10, 8))
                        sns.heatmap(
                            correlation,
                            annot=True,
                            cmap='coolwarm',
                            ax=ax,
                            cbar=True,
                            fmt='.2f',
                            linewidths=0.5
                        )
                        plt.title('Correlation Matrix')
                        st.pyplot(fig)
                    else:
                        st.warning("Please select at least two columns for correlation matrix.")
                else:
                    st.warning("Not enough numerical columns for correlation analysis.")

            # Add more visualization types as needed

            # Prediction visualization
            if st.session_state.predictions is not None:
                st.subheader("Prediction Visualization")

                # Distribution of predictions
                st.write("Distribution of Predictions:")

                # Plot prediction distribution
                fig, ax = plt.subplots(figsize=(10, 6))

                if 'toxicity_score' in st.session_state.predictions.columns:
                    sns.histplot(st.session_state.predictions['toxicity_score'], bins=20, kde=True, ax=ax)
                    plt.xlabel('Toxicity Score')
                    plt.ylabel('Frequency')
                    plt.title('Distribution of Toxicity Scores')
                    st.pyplot(fig)

                    # Toxicity threshold analysis
                    st.write("Toxicity Threshold Analysis:")

                    threshold = st.slider("Toxicity Threshold", 0.0, 1.0, 0.5, 0.05)

                    # Calculate metrics at different thresholds
                    st.session_state.predictions['is_toxic_at_threshold'] = st.session_state.predictions[
                                                                                'toxicity_score'] > threshold

                    toxic_at_threshold = st.session_state.predictions['is_toxic_at_threshold'].sum()
                    total_predictions = len(st.session_state.predictions)

                    st.write(f"Threshold: {threshold}")
                    st.write(
                        f"Toxic comments: {toxic_at_threshold} ({toxic_at_threshold / total_predictions * 100:.2f}%)")
                    st.write(
                        f"Non-toxic comments: {total_predictions - toxic_at_threshold} ({(total_predictions - toxic_at_threshold) / total_predictions * 100:.2f}%)")
                else:
                    st.warning("Toxicity scores not found in predictions.")

        else:
            st.info("Please upload and preprocess data for visualization.")


if __name__ == "__main__":
    main()