import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score, mean_squared_error
from sklearn.model_selection import train_test_split
import scipy.stats as stats
import plotly.express as px
import plotly.graph_objects as go
from pathlib import Path
import os
import re
from plotly.subplots import make_subplots

# Set up the style for all plots
plt.style.use('default')
sns.set_theme(style="whitegrid", palette="husl")

def simple_word_tokenize(text):
    """Simple word tokenization function"""
    # Convert to string and lowercase
    text = str(text).lower()
    # Remove special characters and extra whitespace
    text = re.sub(r'[^\w\s]', ' ', text)
    # Split on whitespace and remove empty strings
    words = [word for word in text.split() if word]
    return words

def simple_sentence_split(text):
    """Simple sentence splitting function"""
    # Convert to string
    text = str(text)
    # Split on common sentence endings
    sentences = re.split(r'[.!?]+', text)
    # Remove empty strings and strip whitespace
    sentences = [s.strip() for s in sentences if s.strip()]
    return sentences

def extract_text_features(text):
    """Extract basic features from text"""
    try:
        # Handle NaN or None values
        if pd.isna(text) or text is None:
            return None  # Return None instead of default values
        
        words = simple_word_tokenize(text)
        sentences = simple_sentence_split(text)
        
        features = {
            'word_count': len(words),
            'sentence_count': len(sentences),
            'avg_word_length': np.mean([len(word) for word in words]) if words else None,
            'avg_sentence_length': len(words) / len(sentences) if sentences else None
        }
        return features
    except Exception as e:
        return None  # Return None if any error occurs

def load_data():
    """Load and prepare the data"""
    # Try multiple possible paths for Hugging Face Spaces compatibility
    import os
    
    # Debug: Print current working directory
    st.write(f"Current working directory: {os.getcwd()}")
    
    # Try different possible data directory paths
    possible_paths = [
        Path("Data"),  # Relative to current working directory
        Path.cwd() / "Data",  # Explicitly relative to current working directory
        Path("/home/user/app/Data"),  # Hugging Face Spaces typical path
        Path("/home/user/Data"),  # Alternative Hugging Face Spaces path
    ]
    
    data_dir = None
    for path in possible_paths:
        st.write(f"Checking path: {path}")
        if path.exists():
            data_dir = path
            st.write(f"Found data directory at: {data_dir}")
            break
    
    if data_dir is None:
        st.error("Could not find Data directory in any of the expected locations")
        return None, None, None, None
    
    # Load the datasets
    try:
        df_reviews = pd.read_csv(data_dir / "reviews.csv")
        df_submissions = pd.read_csv(data_dir / "Submissions.csv")
        df_dec = pd.read_csv(data_dir / "decision.csv")
        df_keyword = pd.read_csv(data_dir / "submission_keyword.csv")
        
        # Clean the data by dropping rows with NaN values in critical columns
        df_reviews = df_reviews.dropna(subset=['review', 'rating_int', 'confidence_int'])
        
        # Extract features
        features = df_reviews['review'].apply(extract_text_features)
        df_features = pd.DataFrame(features.tolist())
        df_reviews = pd.concat([df_reviews, df_features], axis=1)
        
        # Drop any remaining rows with NaN values
        df_reviews = df_reviews.dropna()
        
        # Verify no NaN values remain
        if df_reviews.isna().any().any():
            st.warning("Some NaN values were found and those rows were dropped")
            df_reviews = df_reviews.dropna()
        
        return df_reviews, df_submissions, df_dec, df_keyword
    except FileNotFoundError as e:
        st.error(f"Data files not found. Please make sure the data files are in the correct location: {data_dir}")
        st.error(f"Error details: {str(e)}")
        return None, None, None, None
    except Exception as e:
        st.error(f"Error processing data: {str(e)}")
        return None, None, None, None

def create_feature_plot(df, x_col, y_col, title):
    """Create an interactive scatter plot using plotly"""
    # Ensure no NaN values
    df_plot = df.dropna(subset=[x_col, y_col])
    
    fig = px.scatter(df_plot, x=x_col, y=y_col, 
                    title=title,
                    labels={x_col: x_col.replace('_', ' ').title(),
                           y_col: y_col.replace('_', ' ').title()},
                    template="plotly_dark")
    fig.update_layout(
        title_x=0.5,
        title_font_size=20,
        showlegend=True,
        plot_bgcolor='rgb(30, 30, 30)',
        paper_bgcolor='rgb(30, 30, 30)',
        font=dict(color='white')
    )
    return fig

def create_correlation_heatmap(df, columns):
    """Create a correlation heatmap using plotly"""
    # Ensure no NaN values
    df_corr = df[columns].dropna()
    corr = df_corr.corr()
    
    fig = go.Figure(data=go.Heatmap(
        z=corr,
        x=corr.columns,
        y=corr.columns,
        colorscale='RdBu',
        zmin=-1, zmax=1,
        text=[[f'{val:.2f}' for val in row] for row in corr.values],
        texttemplate='%{text}',
        textfont={"size": 12}
    ))
    fig.update_layout(
        title='Feature Correlation Heatmap',
        title_x=0.5,
        title_font_size=20,
        plot_bgcolor='rgb(30, 30, 30)',
        paper_bgcolor='rgb(30, 30, 30)',
        font=dict(color='white')
    )
    return fig

def create_regression_plot(df, x_col, y_col, title):
    """Create a scatter plot with regression line"""
    # Ensure no NaN values
    df_plot = df.dropna(subset=[x_col, y_col])
    
    fig = px.scatter(df_plot, x=x_col, y=y_col, 
                    title=title,
                    labels={x_col: x_col.replace('_', ' ').title(),
                           y_col: y_col.replace('_', ' ').title()},
                    template="plotly_dark")
    
    # Add regression line
    model = LinearRegression()
    X = df_plot[x_col].values.reshape(-1, 1)
    y = df_plot[y_col].values
    model.fit(X, y)
    y_pred = model.predict(X)
    
    fig.add_trace(go.Scatter(
        x=df_plot[x_col],
        y=y_pred,
        mode='lines',
        name='Regression Line',
        line=dict(color='red', width=2)
    ))
    
    fig.update_layout(
        title_x=0.5,
        title_font_size=20,
        showlegend=True,
        plot_bgcolor='rgb(30, 30, 30)',
        paper_bgcolor='rgb(30, 30, 30)',
        font=dict(color='white')
    )
    return fig, model

def create_correlation_examples():
    """Create example plots showing different correlation types"""
    # Generate example data
    np.random.seed(42)
    n_points = 100
    
    # Perfect positive correlation
    x1 = np.linspace(0, 10, n_points)
    y1 = x1 + np.random.normal(0, 0.1, n_points)
    
    # Perfect negative correlation
    x2 = np.linspace(0, 10, n_points)
    y2 = -x2 + np.random.normal(0, 0.1, n_points)
    
    # Low correlation
    x3 = np.random.normal(5, 2, n_points)
    y3 = np.random.normal(5, 2, n_points)
    
    # Create subplots
    fig = make_subplots(rows=1, cols=3,
                       subplot_titles=('Perfect Positive Correlation (r ≈ 1)',
                                     'Perfect Negative Correlation (r ≈ -1)',
                                     'Low Correlation (r ≈ 0)'))
    
    # Add traces
    fig.add_trace(go.Scatter(x=x1, y=y1, mode='markers', name='r ≈ 1'),
                 row=1, col=1)
    fig.add_trace(go.Scatter(x=x2, y=y2, mode='markers', name='r ≈ -1'),
                 row=1, col=2)
    fig.add_trace(go.Scatter(x=x3, y=y3, mode='markers', name='r ≈ 0'),
                 row=1, col=3)
    
    # Update layout
    fig.update_layout(
        height=400,
        showlegend=False,
        template="plotly_dark",
        plot_bgcolor='rgb(30, 30, 30)',
        paper_bgcolor='rgb(30, 30, 30)',
        font=dict(color='white', size=14),
        title=dict(
            text='Examples of Different Correlation Types',
            x=0.5,
            y=0.95,
            font=dict(size=20)
        )
    )
    
    # Update axes
    for i in range(1, 4):
        fig.update_xaxes(title_text='X', row=1, col=i)
        fig.update_yaxes(title_text='Y', row=1, col=i)
    
    return fig

def show():
    st.title("Week 5: Introduction to Machine Learning and Linear Regression")
    
    # Introduction Section
    st.header("Course Overview")
    st.write("""
    In this week, we'll explore machine learning through a fascinating real-world challenge: The Academic Publishing Crisis.
    
    Imagine you're the program chair for a prestigious AI conference. You've just received 5,000 paper submissions, and you need to:
    - Decide which papers to accept (only 20% can be accepted)
    - Ensure fair and consistent reviews
    - Understand what makes reviewers confident in their assessments
    
    The Problem: Human reviewers are inconsistent. Some are harsh, others lenient. Some write detailed reviews, others just a few sentences. 
    How can we use data to understand and improve this process?
    
    **Your Mission: Build a machine learning system to analyze review patterns and predict paper acceptance!**
    """)
    
    # Learning Path
    st.subheader("Key Concepts You'll Learn")
    st.write("""
    1. **Linear Regression (线性回归):**
       - Definition: A statistical method that models the relationship between a dependent variable and one or more independent variables
       - Real-world example: Predicting house prices based on size and location
    
    2. **Correlation Analysis (相关性分析):**
       - Definition: Statistical measure that shows how strongly two variables are related
       - Range: -1 (perfect negative correlation) to +1 (perfect positive correlation)
    """)
    
    # Add correlation examples
    st.write("Here are examples of different correlation types:")
    corr_examples = create_correlation_examples()
    st.plotly_chart(corr_examples, use_container_width=True)
    
    # Show example code for correlation analysis
    with st.expander("Example Code: Correlation Analysis"):
        st.code("""
# Example: Calculating and visualizing correlations
import numpy as np
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots

# Generate example data
np.random.seed(42)
n_points = 100

# Perfect positive correlation
x1 = np.linspace(0, 10, n_points)
y1 = x1 + np.random.normal(0, 0.1, n_points)

# Perfect negative correlation
x2 = np.linspace(0, 10, n_points)
y2 = -x2 + np.random.normal(0, 0.1, n_points)

# Low correlation
x3 = np.random.normal(5, 2, n_points)
y3 = np.random.normal(5, 2, n_points)

# Calculate correlations
corr1 = np.corrcoef(x1, y1)[0,1]  # Should be close to 1
corr2 = np.corrcoef(x2, y2)[0,1]  # Should be close to -1
corr3 = np.corrcoef(x3, y3)[0,1]  # Should be close to 0

print(f"Correlation 1: {corr1:.3f}")
print(f"Correlation 2: {corr2:.3f}")
print(f"Correlation 3: {corr3:.3f}")
        """)
    
    st.write("""
    3. **Reading Linear Regression Output (解读线性回归结果):**
       - R-squared (R²): Proportion of variance explained by the model (0-1)
       - p-value: It represents the probability of observing results at least as extreme as the ones seen in the study, assuming the null hypothesis is true. Essentially, it's the likelihood of getting the observed outcome (or a more extreme one) if there's actually no real effect or relationship being studied
       - Coefficients (系数): How much the dependent variable changes with a one-unit change in the independent variable
       - Standard errors: Uncertainty in coefficient estimates
       - Confidence intervals: Range where true coefficient likely lies
    """)

    # Load the data
    df_reviews, df_submissions, df_dec, df_keyword = load_data()
    
    if df_reviews is not None:
        try:
            # Module 1: Data Exploration
            st.header("Module 1: Data Exploration")
            st.write("Let's explore our dataset to understand the review patterns:")
            
            # Show example code for data loading and cleaning
            with st.expander("Example Code: Data Loading and Cleaning"):
                st.code("""
# Load and clean the data
import pandas as pd
import numpy as np

def load_and_clean_data():
    # Load datasets
    df_reviews = pd.read_csv('reviews.csv')
    df_submissions = pd.read_csv('Submissions.csv')
    df_dec = pd.read_csv('decision.csv')
    df_keyword = pd.read_csv('submission_keyword.csv')
    
    # Clean reviews data
    df_reviews = df_reviews.dropna(subset=['review', 'rating_int', 'confidence_int'])
    
    # Extract text features
    def extract_text_features(text):
        if pd.isna(text) or text is None:
            return {
                'word_count': 0,
                'sentence_count': 0,
                'avg_word_length': 0,
                'avg_sentence_length': 0
            }
        
        # Convert to string and clean
        text = str(text).lower()
        text = re.sub(r'[^\\w\\s]', ' ', text)
        
        # Split into words and sentences
        words = [word for word in text.split() if word]
        sentences = [s.strip() for s in re.split(r'[.!?]+', text) if s.strip()]
        
        return {
            'word_count': len(words),
            'sentence_count': len(sentences),
            'avg_word_length': np.mean([len(word) for word in words]) if words else 0,
            'avg_sentence_length': len(words) / len(sentences) if sentences else 0
        }
    
    # Apply feature extraction
    features = df_reviews['review'].apply(extract_text_features)
    df_features = pd.DataFrame(features.tolist())
    df_reviews = pd.concat([df_reviews, df_features], axis=1)
    
    # Fill any remaining NaN values
    df_reviews = df_reviews.fillna(0)
    
    return df_reviews, df_submissions, df_dec, df_keyword
                """)
            
            # Verify data quality
            st.subheader("Data Quality Check")
            missing_data = df_reviews.isna().sum()
            if missing_data.any():
                st.warning("Missing values found in the dataset:")
                st.write(missing_data[missing_data > 0])
            
            # Show basic statistics
            col1, col2 = st.columns(2)
            with col1:
                st.metric("Total Reviews", len(df_reviews))
                st.metric("Average Rating", f"{df_reviews['rating_int'].mean():.2f}")
            with col2:
                st.metric("Average Word Count", f"{df_reviews['word_count'].mean():.0f}")
                st.metric("Average Confidence", f"{df_reviews['confidence_int'].mean():.2f}")
            
            # Interactive feature selection
            st.subheader("Interactive Feature Analysis")
            feature_cols = ['word_count', 'sentence_count', 'avg_word_length', 
                          'avg_sentence_length', 'rating_int', 'confidence_int']
            
            col1, col2 = st.columns(2)
            with col1:
                x_feature = st.selectbox("Select X-axis feature:", feature_cols)
            with col2:
                y_feature = st.selectbox("Select Y-axis feature:", feature_cols)
            
            # Create interactive plot
            fig = create_feature_plot(df_reviews, x_feature, y_feature,
                                   f'{x_feature.replace("_", " ").title()} vs {y_feature.replace("_", " ").title()}')
            st.plotly_chart(fig, use_container_width=True)
            
            # Show correlation between selected features
            corr = df_reviews[[x_feature, y_feature]].corr().iloc[0,1]
            st.write(f"Correlation between {x_feature} and {y_feature}: {corr:.3f}")
            
            # Distribution plots
            st.subheader("Distribution of Ratings and Confidence")
            col1, col2 = st.columns(2)
            with col1:
                fig = px.histogram(df_reviews.dropna(subset=['rating_int']), 
                                 x='rating_int', 
                                 title='Distribution of Ratings',
                                 template="plotly_dark")
                st.plotly_chart(fig, use_container_width=True)
            with col2:
                fig = px.histogram(df_reviews.dropna(subset=['confidence_int']), 
                                 x='confidence_int',
                                 title='Distribution of Confidence',
                                 template="plotly_dark")
                st.plotly_chart(fig, use_container_width=True)
            
            # Show example code for distribution analysis
            with st.expander("Example Code: Distribution Analysis"):
                st.code("""
# Analyze distributions of numerical features
import plotly.express as px

def analyze_distributions(df):
    # Create histograms for key features
    fig1 = px.histogram(df, x='rating_int', 
                       title='Distribution of Ratings',
                       template="plotly_dark")
    
    fig2 = px.histogram(df, x='confidence_int',
                       title='Distribution of Confidence',
                       template="plotly_dark")
    
    # Calculate summary statistics
    stats = df[['rating_int', 'confidence_int']].describe()
    
    return fig1, fig2, stats

# Usage
fig1, fig2, stats = analyze_distributions(df_reviews)
print(stats)
                """)
            
            # Text feature distributions
            st.subheader("Text Feature Distributions")
            col1, col2 = st.columns(2)
            with col1:
                fig = px.histogram(df_reviews.dropna(subset=['avg_word_length']), 
                                 x='avg_word_length',
                                 title='Average Word Length Distribution',
                                 template="plotly_dark")
                st.plotly_chart(fig, use_container_width=True)
            with col2:
                fig = px.histogram(df_reviews.dropna(subset=['avg_sentence_length']), 
                                 x='avg_sentence_length',
                                 title='Average Sentence Length Distribution',
                                 template="plotly_dark")
                st.plotly_chart(fig, use_container_width=True)
            
            # Correlation analysis
            st.subheader("Feature Correlations")
            corr_fig = create_correlation_heatmap(df_reviews, feature_cols)
            st.plotly_chart(corr_fig, use_container_width=True)
            
            # Show example code for correlation analysis
            with st.expander("Example Code: Correlation Analysis"):
                st.code("""
# Analyze correlations between features
import plotly.graph_objects as go

def analyze_correlations(df, columns):
    # Calculate correlation matrix
    corr = df[columns].corr()
    
    # Create heatmap
    fig = go.Figure(data=go.Heatmap(
        z=corr,
        x=corr.columns,
        y=corr.columns,
        colorscale='RdBu',
        zmin=-1, zmax=1,
        text=[[f'{val:.2f}' for val in row] for row in corr.values],
        texttemplate='%{text}',
        textfont={"size": 12}
    ))
    
    fig.update_layout(
        title='Feature Correlation Heatmap',
        template="plotly_dark"
    )
    
    return fig, corr

# Usage
fig, corr_matrix = analyze_correlations(df_reviews, feature_cols)
print(corr_matrix)
                """)
            
            # Module 2: Simple Linear Regression
            st.header("Module 2: Simple Linear Regression")
            st.write("""
            Let's explore the relationship between review length and rating using simple linear regression.
            """)
            
            # Interactive feature selection for regression
            st.subheader("Interactive Regression Analysis")
            col1, col2 = st.columns(2)
            with col1:
                x_reg = st.selectbox("Select feature for X-axis:", feature_cols)
            with col2:
                y_reg = st.selectbox("Select target variable:", feature_cols)
            
            # Create regression plot
            fig, model = create_regression_plot(df_reviews, x_reg, y_reg,
                                             f'{x_reg.replace("_", " ").title()} vs {y_reg.replace("_", " ").title()}')
            st.plotly_chart(fig, use_container_width=True)
            
            # Show regression metrics
            st.subheader("Regression Metrics")
            col1, col2 = st.columns(2)
            with col1:
                r2_score = model.score(df_reviews[[x_reg]].dropna(), 
                                     df_reviews[y_reg].dropna())
                st.metric("R-squared", f"{r2_score:.3f}")
            with col2:
                st.metric("Slope", f"{model.coef_[0]:.3f}")
            
            # Show example code for simple linear regression
            with st.expander("Example Code: Simple Linear Regression"):
                st.code('''
# Perform simple linear regression
from sklearn.linear_model import LinearRegression
import plotly.graph_objects as go

def simple_linear_regression(df, x_col, y_col, title=None):
    """
    Perform simple linear regression on any DataFrame.
    
    Parameters:
    -----------
    df : pandas.DataFrame
        Input DataFrame containing the features
    x_col : str
        Name of the column to use as independent variable
    y_col : str
        Name of the column to use as dependent variable
    title : str, optional
        Title for the plot. If None, will use column names
    
    Returns:
    --------
    tuple
        (model, r2_score, fig) where:
        - model is the fitted LinearRegression object
        - r2_score is the R-squared value
        - fig is the plotly figure object
    """
    # Handle missing values by dropping them
    df_clean = df.dropna(subset=[x_col, y_col])
    
    if len(df_clean) == 0:
        raise ValueError("No valid data points after removing missing values")
    
    # Prepare data
    X = df_clean[[x_col]]
    y = df_clean[y_col]
    
    # Fit model
    model = LinearRegression()
    model.fit(X, y)
    
    # Calculate R-squared
    r2_score = model.score(X, y)
    
    # Create visualization
    fig = go.Figure()
    
    # Add scatter plot
    fig.add_trace(go.Scatter(
        x=X[x_col],
        y=y,
        mode='markers',
        name='Data Points',
        marker=dict(size=8, opacity=0.6)
    ))
    
    # Add regression line
    x_range = np.linspace(X[x_col].min(), X[x_col].max(), 100)
    y_pred = model.predict(x_range.reshape(-1, 1))
    
    fig.add_trace(go.Scatter(
        x=x_range,
        y=y_pred,
        mode='lines',
        name='Regression Line',
        line=dict(color='red', width=2)
    ))
    
    # Update layout
    title = title or f'{x_col} vs {y_col}'
    fig.update_layout(
        title=title,
        xaxis_title=x_col,
        yaxis_title=y_col,
        template="plotly_dark",
        showlegend=True
    )
    
    return model, r2_score, fig

# Usage
fig, model = simple_linear_regression(df_reviews, 'word_count', 'rating_int')
print(f"R-squared: {model.score(X, y):.3f}")
print(f"Slope: {model.coef_[0]:.3f}")
''')
            
            # Module 3: Multiple Linear Regression
            st.header("Module 3: Multiple Linear Regression")
            st.write("""
            Now let's build a more complex model using multiple features to predict ratings.
            """)
            
            try:
                # Prepare data for modeling
                feature_cols = ['word_count', 'sentence_count', 
                              'avg_word_length', 'avg_sentence_length',
                              'confidence_int']
                
                # Interactive feature selection for multiple regression
                st.subheader("Select Features for Multiple Regression")
                selected_features = st.multiselect(
                    "Choose features to include in the model:",
                    feature_cols,
                    default=feature_cols
                )
                
                if selected_features:
                    # Ensure no NaN values in features
                    df_model = df_reviews.dropna(subset=selected_features + ['rating_int'])
                    
                    X = df_model[selected_features]
                    y = df_model['rating_int']
                    
                    # Split data
                    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
                    
                    # Fit regression model
                    model = LinearRegression()
                    model.fit(X_train, y_train)
                    
                    # Create 3D visualization if exactly 2 features are selected
                    if len(selected_features) == 2:
                        st.subheader("3D Visualization of Selected Features")
                        fig = px.scatter_3d(df_model.sample(min(1000, len(df_model))), 
                                        x=selected_features[0], 
                                        y=selected_features[1], 
                                        z='rating_int',
                                        title='Review Features in 3D Space',
                                        template="plotly_dark")
                        fig.update_layout(
                            title_x=0.5,
                            title_font_size=20,
                            scene = dict(
                                xaxis_title=selected_features[0].replace('_', ' ').title(),
                                yaxis_title=selected_features[1].replace('_', ' ').title(),
                                zaxis_title='Rating'
                            )
                        )
                        st.plotly_chart(fig, use_container_width=True)
                    
                    # Show model metrics
                    st.subheader("Model Performance")
                    col1, col2, col3 = st.columns(3)
                    with col1:
                        st.metric("Training R²", f"{model.score(X_train, y_train):.3f}")
                    with col2:
                        st.metric("Testing R²", f"{model.score(X_test, y_test):.3f}")
                    with col3:
                        st.metric("RMSE", f"{np.sqrt(mean_squared_error(y_test, model.predict(X_test))):.3f}")
                    
                    # Show coefficients
                    st.subheader("Model Coefficients")
                    coef_df = pd.DataFrame({
                        'Feature': X.columns,
                        'Coefficient': model.coef_
                    })
                    st.dataframe(coef_df)
                    
                    # Show example code for multiple linear regression
                    with st.expander("Example Code: Multiple Linear Regression"):
                        st.code('''
# Perform multiple linear regression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

def multiple_linear_regression(df, feature_cols, target_col, test_size=0.2, random_state=42):
    """
    Perform multiple linear regression on any DataFrame.
    
    Parameters:
    -----------
    df : pandas.DataFrame
        Input DataFrame containing the features
    feature_cols : list of str
        Names of the columns to use as independent variables
    target_col : str
        Name of the column to use as dependent variable
    test_size : float, optional
        Proportion of data to use for testing
    random_state : int, optional
        Random seed for reproducibility
    
    Returns:
    --------
    tuple
        (model, metrics, coef_df, fig) where:
        - model is the fitted LinearRegression object
        - metrics is a dictionary of performance metrics
        - coef_df is a DataFrame of feature coefficients
        - fig is the plotly figure object (if 2 features selected)
    """
    # Handle missing values by dropping them
    df_clean = df.dropna(subset=feature_cols + [target_col])
    
    if len(df_clean) == 0:
        raise ValueError("No valid data points after removing missing values")
    
    # Prepare data
    X = df_clean[feature_cols]
    y = df_clean[target_col]
    
    # Split data
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=test_size, random_state=random_state)
    
    # Fit model
    model = LinearRegression()
    model.fit(X_train, y_train)
    
    # Make predictions
    y_train_pred = model.predict(X_train)
    y_test_pred = model.predict(X_test)
    
    # Calculate metrics
    metrics = {
        'train_r2': r2_score(y_train, y_train_pred),
        'test_r2': r2_score(y_test, y_test_pred),
        'train_rmse': np.sqrt(mean_squared_error(y_train, y_train_pred)),
        'test_rmse': np.sqrt(mean_squared_error(y_test, y_test_pred))
    }
    
    # Create coefficient DataFrame
    coef_df = pd.DataFrame({
        'Feature': feature_cols,
        'Coefficient': model.coef_,
        'Absolute_Impact': np.abs(model.coef_)
    }).sort_values('Absolute_Impact', ascending=False)
    
    # Create visualization if exactly 2 features are selected
    fig = None
    if len(feature_cols) == 2:
        fig = px.scatter_3d(
            df_clean.sample(min(1000, len(df_clean))),
            x=feature_cols[0],
            y=feature_cols[1],
            z=target_col,
            title=f'Relationship between {feature_cols[0]}, {feature_cols[1]}, and {target_col}',
            template="plotly_dark"
        )
        
        # Add regression plane
        x_range = np.linspace(df_clean[feature_cols[0]].min(), df_clean[feature_cols[0]].max(), 20)
        y_range = np.linspace(df_clean[feature_cols[1]].min(), df_clean[feature_cols[1]].max(), 20)
        x_grid, y_grid = np.meshgrid(x_range, y_range)
        
        z_grid = (model.intercept_ +
                 model.coef_[0] * x_grid +
                 model.coef_[1] * y_grid)
        
        fig.add_trace(go.Surface(
            x=x_grid,
            y=y_grid,
            z=z_grid,
            opacity=0.5,
            showscale=False
        ))
    
    return model, metrics, coef_df, fig

# Usage
model, train_score, test_score, rmse, coef_df = multiple_linear_regression(
    df_reviews, 
    ['word_count', 'sentence_count', 'confidence_int'],
    'rating_int'
)
print(f"Training R²: {train_score:.3f}")
print(f"Testing R²: {test_score:.3f}")
print(f"RMSE: {rmse:.3f}")
print(coef_df)
''')
                
            except Exception as e:
                st.error(f"Error in model training: {str(e)}")
                st.write("Please check the data quality and try again.")
        
        except Exception as e:
            st.error(f"Error in data processing: {str(e)}")
            st.write("Please check the data format and try again.")

    # Practice Exercises
    st.header("Practice Exercises")
    
    # Add new section for writing prompts
    st.subheader("Writing Prompts for Analyzing Linear Regression Results")
    st.write("""
    Use these prompts to help you interpret and write about your linear regression results:
    
    1. **Model Fit and R-squared:**
       - "The model explains [R² value]% of the variance in [dependent variable], suggesting [strong/moderate/weak] predictive power."
       - "With an R-squared of [value], we can conclude that [interpretation of model fit]."
       - "The relatively [high/low] R-squared value indicates that [interpretation of model's explanatory power]."
    
    2. **Statistical Significance and p-values:**
       - "The p-value of [value] for [feature] suggests that this relationship is [statistically significant/not significant]."
       - "Given the p-value of [value], we [can/cannot] reject the null hypothesis that [interpretation]."
       - "The statistical significance (p = [value]) indicates that [interpretation of relationship]."
    
    3. **Coefficients and Their Meaning:**
       - "For each unit increase in [independent variable], [dependent variable] [increases/decreases] by [coefficient value] units."
       - "The coefficient of [value] for [feature] suggests that [interpretation of relationship]."
       - "The positive/negative coefficient indicates that [interpretation of direction of relationship]."
    
    4. **Uncertainty and Standard Errors:**
       - "The standard error of [value] for [feature] indicates [interpretation of precision]."
       - "The relatively [small/large] standard error suggests that [interpretation of estimate reliability]."
       - "The uncertainty in our coefficient estimates, as shown by the standard errors, [interpretation of confidence in results]."
    
    5. **Confidence Intervals:**
       - "We are 95% confident that the true coefficient for [feature] lies between [lower bound] and [upper bound]."
       - "The confidence interval [includes/does not include] zero, suggesting that [interpretation of significance]."
       - "The narrow/wide confidence interval indicates [interpretation of precision]."
    
    6. **Practical Significance:**
       - "While the relationship is statistically significant, the effect size of [value] suggests [interpretation of practical importance]."
       - "The coefficient of [value] indicates that [interpretation of real-world impact]."
       - "In practical terms, this means that [interpretation of practical implications]."
    
    7. **Model Limitations:**
       - "The model's assumptions of [assumptions] may not hold in this case because [explanation]."
       - "Potential limitations of our analysis include [list limitations]."
       - "We should be cautious in interpreting these results because [explanation of limitations]."
    
    8. **Recommendations:**
       - "Based on our analysis, we recommend [specific action] because [explanation]."
       - "The results suggest that [interpretation] and therefore [recommendation]."
       - "To improve the model, we could [suggestions for improvement]."
    """)
    
    with st.expander("Exercise 1: Simple Linear Regression"):
        st.write("""
        1. Create a function that performs simple linear regression on any DataFrame
        2. The function should:
           - Take a DataFrame and column names as input
           - Handle missing values appropriately
           - Calculate and return R-squared value
           - Create a visualization of the relationship
        3. Test your function with different features from the dataset
        """)
        
        st.code('''
# Solution: Generic Simple Linear Regression Function
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
import plotly.express as px
import plotly.graph_objects as go

def simple_linear_regression(df, x_col, y_col, title=None):
    """
    Perform simple linear regression on any DataFrame.
    
    Parameters:
    -----------
    df : pandas.DataFrame
        Input DataFrame containing the features
    x_col : str
        Name of the column to use as independent variable
    y_col : str
        Name of the column to use as dependent variable
    title : str, optional
        Title for the plot. If None, will use column names
    
    Returns:
    --------
    tuple
        (model, r2_score, fig) where:
        - model is the fitted LinearRegression object
        - r2_score is the R-squared value
        - fig is the plotly figure object
    """
    # Handle missing values by dropping them
    df_clean = df.dropna(subset=[x_col, y_col])
    
    if len(df_clean) == 0:
        raise ValueError("No valid data points after removing missing values")
    
    # Prepare data
    X = df_clean[[x_col]]
    y = df_clean[y_col]
    
    # Fit model
    model = LinearRegression()
    model.fit(X, y)
    
    # Calculate R-squared
    r2_score = model.score(X, y)
    
    # Create visualization
    fig = go.Figure()
    
    # Add scatter plot
    fig.add_trace(go.Scatter(
        x=X[x_col],
        y=y,
        mode='markers',
        name='Data Points',
        marker=dict(size=8, opacity=0.6)
    ))
    
    # Add regression line
    x_range = np.linspace(X[x_col].min(), X[x_col].max(), 100)
    y_pred = model.predict(x_range.reshape(-1, 1))
    
    fig.add_trace(go.Scatter(
        x=x_range,
        y=y_pred,
        mode='lines',
        name='Regression Line',
        line=dict(color='red', width=2)
    ))
    
    # Update layout
    title = title or f'{x_col} vs {y_col}'
    fig.update_layout(
        title=title,
        xaxis_title=x_col,
        yaxis_title=y_col,
        template="plotly_dark",
        showlegend=True
    )
    
    return model, r2_score, fig

# Example usage:
# Load your data
df = pd.read_csv('your_data.csv')

# Try different feature pairs
feature_pairs = [
    ('word_count', 'rating_int'),
    ('confidence_int', 'rating_int'),
    ('avg_word_length', 'rating_int')
]

# Analyze each pair
for x_col, y_col in feature_pairs:
    try:
        model, r2, fig = simple_linear_regression(df, x_col, y_col)
        print(f"\nAnalysis of {x_col} vs {y_col}:")
        print(f"R-squared: {r2:.3f}")
        print(f"Slope: {model.coef_[0]:.3f}")
        print(f"Intercept: {model.intercept_:.3f}")
        fig.show()
    except Exception as e:
        print(f"Error analyzing {x_col} vs {y_col}: {str(e)}")
''')
    
    with st.expander("Exercise 2: Multiple Linear Regression"):
        st.write("""
        1. Create a function that performs multiple linear regression on any DataFrame
        2. The function should:
           - Take a DataFrame and lists of feature columns as input
           - Handle missing values appropriately
           - Split data into training and test sets
           - Calculate and return performance metrics
           - Create visualizations of the results
        3. Test your function with different combinations of features
        """)
        
        st.code('''
# Solution: Generic Multiple Linear Regression Function
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score
import plotly.express as px
import plotly.graph_objects as go

def multiple_linear_regression(df, feature_cols, target_col, test_size=0.2, random_state=42):
    """
    Perform multiple linear regression on any DataFrame.
    
    Parameters:
    -----------
    df : pandas.DataFrame
        Input DataFrame containing the features
    feature_cols : list of str
        Names of the columns to use as independent variables
    target_col : str
        Name of the column to use as dependent variable
    test_size : float, optional
        Proportion of data to use for testing
    random_state : int, optional
        Random seed for reproducibility
    
    Returns:
    --------
    tuple
        (model, metrics, coef_df, fig) where:
        - model is the fitted LinearRegression object
        - metrics is a dictionary of performance metrics
        - coef_df is a DataFrame of feature coefficients
        - fig is the plotly figure object (if 2 features selected)
    """
    # Handle missing values by dropping them
    df_clean = df.dropna(subset=feature_cols + [target_col])
    
    if len(df_clean) == 0:
        raise ValueError("No valid data points after removing missing values")
    
    # Prepare data
    X = df_clean[feature_cols]
    y = df_clean[target_col]
    
    # Split data
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=test_size, random_state=random_state)
    
    # Fit model
    model = LinearRegression()
    model.fit(X_train, y_train)
    
    # Make predictions
    y_train_pred = model.predict(X_train)
    y_test_pred = model.predict(X_test)
    
    # Calculate metrics
    metrics = {
        'train_r2': r2_score(y_train, y_train_pred),
        'test_r2': r2_score(y_test, y_test_pred),
        'train_rmse': np.sqrt(mean_squared_error(y_train, y_train_pred)),
        'test_rmse': np.sqrt(mean_squared_error(y_test, y_test_pred))
    }
    
    # Create coefficient DataFrame
    coef_df = pd.DataFrame({
        'Feature': feature_cols,
        'Coefficient': model.coef_,
        'Absolute_Impact': np.abs(model.coef_)
    }).sort_values('Absolute_Impact', ascending=False)
    
    # Create visualization if exactly 2 features are selected
    fig = None
    if len(feature_cols) == 2:
        fig = px.scatter_3d(
            df_clean.sample(min(1000, len(df_clean))),
            x=feature_cols[0],
            y=feature_cols[1],
            z=target_col,
            title=f'Relationship between {feature_cols[0]}, {feature_cols[1]}, and {target_col}',
            template="plotly_dark"
        )
        
        # Add regression plane
        x_range = np.linspace(df_clean[feature_cols[0]].min(), df_clean[feature_cols[0]].max(), 20)
        y_range = np.linspace(df_clean[feature_cols[1]].min(), df_clean[feature_cols[1]].max(), 20)
        x_grid, y_grid = np.meshgrid(x_range, y_range)
        
        z_grid = (model.intercept_ +
                 model.coef_[0] * x_grid +
                 model.coef_[1] * y_grid)
        
        fig.add_trace(go.Surface(
            x=x_grid,
            y=y_grid,
            z=z_grid,
            opacity=0.5,
            showscale=False
        ))
    
    return model, metrics, coef_df, fig

# Example usage:
# Load your data
df = pd.read_csv('your_data.csv')

# Define feature sets to try
feature_sets = [
    ['word_count', 'confidence_int'],
    ['word_count', 'sentence_count', 'confidence_int'],
    ['word_count', 'sentence_count', 'avg_word_length', 'avg_sentence_length', 'confidence_int']
]

# Analyze each feature set
for features in feature_sets:
    try:
        print(f"\nAnalyzing features: {features}")
        model, metrics, coef_df, fig = multiple_linear_regression(
            df, features, 'rating_int')
        
        # Print metrics
        print("\nPerformance Metrics:")
        for metric, value in metrics.items():
            print(f"{metric}: {value:.3f}")
        
        # Print coefficients
        print("\nFeature Coefficients:")
        print(coef_df)
        
        # Show visualization if available
        if fig is not None:
            fig.show()
            
    except Exception as e:
        print(f"Error analyzing features {features}: {str(e)}")
''')

    # Weekly Assignment
    username = st.session_state.get("username", "Student")
    st.header(f"{username}'s Weekly Assignment")
    
    if username == "manxiii":
        st.markdown("""
        Hello **manxiii**, here is your Assignment 5: Machine Learning Analysis.
        1. Pick out some figures from the Colab Notebook and write a short summary of the results. Add them to your overleaf paper
                    - Colab [Link](https://colab.research.google.com/drive/1ScwSa8WBcOMCloXsTV5TPFoVrcPHXlW2#scrollTo=VDMRGRbSR0gc)
                    - Overleaf [Link](https://www.overleaf.com/project/68228f4ccb9d18d92c26ba13)
        2. Update your literature review section in the overleaf paper, given the homework.

        **Due Date:** End of Week 5
        """)
    elif username == "zhu":
        st.markdown("""
        Hello **zhu**, here is your Assignment 5: Machine Learning Analysis.
        1. Implement the complete machine learning workflow
        2. Create insightful visualizations of model results
        3. Draw conclusions from your analysis
        4. Submit your work in a Jupyter notebook

        **Due Date:** End of Week 5
        """)
    elif username == "WK":
        st.markdown("""
        Hello **WK**, here is your Assignment 5: Machine Learning Analysis.
        1. Complete the feature engineering pipeline
        2. Build and evaluate linear regression models
        3. Analyze patterns in the data
        4. Submit your findings

        **Due Date:** End of Week 5
        """)
    else:
        st.markdown(f"""
        Hello **{username}**, here is your Assignment 5: Machine Learning Analysis.
        1. Complete the feature engineering pipeline
        2. Build and evaluate linear regression models
        3. Analyze patterns in the data
        4. Submit your findings

        **Due Date:** End of Week 5
        """)