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.ensemble import RandomForestRegressor
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

# Set page configuration with custom theme
st.set_page_config(
    page_title="Data Analytics Hub",
    page_icon="📊",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Custom CSS for better styling
st.markdown("""
    <style>
    .main {
        padding-top: 2rem;
    }
    .stButton>button {
        width: 100%;
        border-radius: 5px;
        height: 3em;
        background-color: #ff4b4b;
        color: white;
        border: none;
    }
    .stButton>button:hover {
        background-color: #ff6b6b;
        color: white;
    }
    div[data-testid="stSidebarNav"] {
        background-image: linear-gradient(#f0f2f6, #e0e2e6);
        padding: 2rem 0;
        border-radius: 10px;
    }
    .css-1d391kg {
        padding: 2rem 1rem;
    }
    .stAlert {
        padding: 1rem;
        border-radius: 5px;
    }
    div[data-testid="stMetricValue"] {
        background-color: #f0f2f6;
        padding: 1rem;
        border-radius: 5px;
    }
    </style>
""", unsafe_allow_html=True)

# Initialize session state
if 'data' not in st.session_state:
    # Create sample data
    np.random.seed(42)
    dates = pd.date_range('2023-01-01', periods=100, freq='D')
    st.session_state.data = pd.DataFrame({
        'date': dates,
        'sales': np.random.normal(1000, 200, 100),
        'visitors': np.random.normal(500, 100, 100),
        'conversion_rate': np.random.uniform(0.01, 0.05, 100),
        'customer_satisfaction': np.random.normal(4.2, 0.5, 100),
        'region': np.random.choice(['North', 'South', 'East', 'West'], 100)
    })

# Sidebar with enhanced styling
with st.sidebar:
    st.image("https://via.placeholder.com/150?text=Analytics+Hub", width=150)
    st.title("Analytics Hub")
    selected_page = st.radio(
        "📑 Navigation",
        ["🏠 Dashboard", "🔍 Data Explorer", "📊 Visualization", "🤖 ML Predictions"],
        key="navigation"
    )

# Dashboard page
if selected_page == "🏠 Dashboard":
    st.title("📊 Data Analytics Dashboard")
    
    # Quick stats in a grid
    col1, col2, col3, col4 = st.columns(4)
    
    with col1:
        st.metric(
            "Total Records",
            f"{len(st.session_state.data):,}",
            "Current dataset size"
        )
    
    with col2:
        st.metric(
            "Avg Sales",
            f"${st.session_state.data['sales'].mean():,.2f}",
            f"{st.session_state.data['sales'].pct_change().mean()*100:.1f}%"
        )
    
    with col3:
        st.metric(
            "Avg Visitors",
            f"{st.session_state.data['visitors'].mean():,.0f}",
            f"{st.session_state.data['visitors'].pct_change().mean()*100:.1f}%"
        )
    
    with col4:
        st.metric(
            "Satisfaction",
            f"{st.session_state.data['customer_satisfaction'].mean():.2f}",
            "Average rating"
        )
    
    # Data upload section with better styling
    st.markdown("### 📁 Upload Your Dataset")
    upload_col1, upload_col2 = st.columns([2, 3])
    
    with upload_col1:
        uploaded_file = st.file_uploader(
            "Choose a CSV file",
            type="csv",
            help="Upload your CSV file to begin analysis"
        )
        if uploaded_file is not None:
            try:
                st.session_state.data = pd.read_csv(uploaded_file)
                st.success("✅ Data uploaded successfully!")
            except Exception as e:
                st.error(f"❌ Error uploading file: {e}")
    
    with upload_col2:
        st.markdown("#### Dataset Preview")
        st.dataframe(
            st.session_state.data.head(3),
            use_container_width=True
        )
# Data Explorer page
elif selected_page == "🔍 Data Explorer":
    st.title("🔍 Data Explorer")
    
    # Enhanced data summary
    col1, col2 = st.columns([1, 2])
    
    with col1:
        st.markdown("### 📊 Dataset Overview")
        st.info(f"""
        - **Rows:** {st.session_state.data.shape[0]:,}
        - **Columns:** {st.session_state.data.shape[1]}
        - **Memory Usage:** {st.session_state.data.memory_usage().sum() / 1024**2:.2f} MB
        """)
    
    with col2:
        st.markdown("### 📈 Quick Stats")
        st.dataframe(
            st.session_state.data.describe(),
            use_container_width=True
        )
    
    # Column analysis with better visualization
    st.markdown("### 🔬 Column Analysis")
    
    col1, col2, col3 = st.columns([1, 1, 2])
    
    with col1:
        column = st.selectbox(
            "Select column:",
            st.session_state.data.columns,
            help="Choose a column to analyze"
        )
    
    with col2:
        if pd.api.types.is_numeric_dtype(st.session_state.data[column]):
            analysis_type = st.selectbox(
                "Analysis type:",
                ["Distribution", "Time Series"] if "date" in column.lower() else ["Distribution"],
                help="Choose type of analysis"
            )
        else:
            analysis_type = "Value Counts"
    
    with col3:
        if pd.api.types.is_numeric_dtype(st.session_state.data[column]):
            stats_col1, stats_col2 = st.columns(2)
            with stats_col1:
                st.metric("Mean", f"{st.session_state.data[column].mean():.2f}")
                st.metric("Std Dev", f"{st.session_state.data[column].std():.2f}")
            with stats_col2:
                st.metric("Median", f"{st.session_state.data[column].median():.2f}")
                st.metric("IQR", f"{st.session_state.data[column].quantile(0.75) - st.session_state.data[column].quantile(0.25):.2f}")
    
    # Enhanced visualization
    fig, ax = plt.subplots(figsize=(12, 6))
    if pd.api.types.is_numeric_dtype(st.session_state.data[column]):
        sns.set_style("whitegrid")
        sns.histplot(data=st.session_state.data, x=column, kde=True, ax=ax)
        ax.set_title(f"Distribution of {column}", pad=20)
    else:
        value_counts = st.session_state.data[column].value_counts()
        sns.barplot(x=value_counts.index, y=value_counts.values, ax=ax)
        ax.set_title(f"Value Counts for {column}", pad=20)
        plt.xticks(rotation=45)
    
    st.pyplot(fig)
# Visualization page
elif selected_page == "📊 Visualization":
    st.title("📊 Advanced Visualizations")
    
    # Enhanced chart selection
    chart_type = st.selectbox(
        "Select visualization type:",
        ["📊 Bar Chart", "📈 Line Chart", "🔵 Scatter Plot", "🌡️ Heatmap"],
        help="Choose the type of visualization you want to create"
    )
    
    if chart_type in ["📊 Bar Chart", "📈 Line Chart"]:
        col1, col2, col3 = st.columns([1, 1, 1])
        
        with col1:
            x_column = st.selectbox("X-axis:", st.session_state.data.columns)
        
        with col2:
            y_column = st.selectbox(
                "Y-axis:",
                [col for col in st.session_state.data.columns 
                 if pd.api.types.is_numeric_dtype(st.session_state.data[col])]
            )
        
        with col3:
            color_theme = st.selectbox(
                "Color theme:",
                ["viridis", "magma", "plasma", "inferno"]
            )
        
        # Create enhanced visualization
        fig, ax = plt.subplots(figsize=(12, 6))
        sns.set_style("whitegrid")
        sns.set_palette(color_theme)
        
        if not pd.api.types.is_numeric_dtype(st.session_state.data[x_column]):
            agg_data = st.session_state.data.groupby(x_column)[y_column].mean().reset_index()
            
            if "Bar" in chart_type:
                sns.barplot(x=x_column, y=y_column, data=agg_data, ax=ax)
            else:
                sns.lineplot(x=x_column, y=y_column, data=agg_data, ax=ax, marker='o')
        else:
            if "Bar" in chart_type:
                sns.barplot(x=x_column, y=y_column, data=st.session_state.data, ax=ax)
            else:
                sns.lineplot(x=x_column, y=y_column, data=st.session_state.data, ax=ax)
        
        plt.xticks(rotation=45)
        ax.set_title(f"{y_column} by {x_column}", pad=20)
        st.pyplot(fig)
    
    elif "Scatter" in chart_type:
        col1, col2, col3 = st.columns([1, 1, 1])
        
        with col1:
            x_column = st.selectbox(
                "X-axis:",
                [col for col in st.session_state.data.columns 
                 if pd.api.types.is_numeric_dtype(st.session_state.data[col])]
            )
        
        with col2:
            y_column = st.selectbox(
                "Y-axis:",
                [col for col in st.session_state.data.columns 
                 if pd.api.types.is_numeric_dtype(st.session_state.data[col]) and col != x_column]
            )
        
        with col3:
            hue_column = st.selectbox(
                "Color by:",
                ["None"] + list(st.session_state.data.columns)
            )
        
        fig, ax = plt.subplots(figsize=(12, 6))
        sns.set_style("whitegrid")
        
        if hue_column != "None":
            sns.scatterplot(x=x_column, y=y_column, data=st.session_state.data, hue=hue_column, ax=ax)
        else:
            sns.scatterplot(x=x_column, y=y_column, data=st.session_state.data, ax=ax)
        
        ax.set_title(f"{y_column} vs {x_column}", pad=20)
        st.pyplot(fig)
    
    elif "Heatmap" in chart_type:
        st.markdown("### 🌡️ Correlation Heatmap")
        
        numeric_cols = st.session_state.data.select_dtypes(include=['number']).columns.tolist()
        correlation = st.session_state.data[numeric_cols].corr()
        
        fig, ax = plt.subplots(figsize=(12, 8))
        mask = np.triu(np.ones_like(correlation))
        sns.heatmap(
            correlation,
            mask=mask,
            annot=True,
            cmap='coolwarm',
            ax=ax,
            center=0,
            square=True,
            fmt='.2f',
            linewidths=1
        )
        ax.set_title("Correlation Heatmap", pad=20)
        st.pyplot(fig)
# ML Predictions page
elif selected_page == "🤖 ML Predictions":
    st.title("🤖 Machine Learning Predictions")
    
    # Model configuration
    st.markdown("### ⚙️ Model Configuration")
    
    config_col1, config_col2 = st.columns(2)
    
    with config_col1:
        numeric_cols = st.session_state.data.select_dtypes(include=['number']).columns.tolist()
        target_column = st.selectbox(
            "Target variable:",
            numeric_cols,
            help="Select the variable you want to predict"
        )
    
    with config_col2:
        model_type = st.selectbox(
            "Model type:",
            ["📊 Linear Regression", "🌲 Random Forest"],
            help="Choose the type of model to train"
        )
    
    # Feature selection with better UI
    st.markdown("### 🎯 Feature Selection")
    feature_cols = [col for col in numeric_cols if col != target_column]
    selected_features = st.multiselect(
        "Select features for the model:",
        feature_cols,
        default=feature_cols,
        help="Choose the variables to use as predictors"
    )
    
    # Model training section
    train_col1, train_col2 = st.columns([2, 1])
    
    with train_col1:
        if st.button("🚀 Train Model", use_container_width=True):
            if len(selected_features) > 0:
                with st.spinner("Training model..."):
                    # Prepare data
                    X = st.session_state.data[selected_features]
                    y = st.session_state.data[target_column]
                    
                    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
                    
                    scaler = StandardScaler()
                    X_train_scaled = scaler.fit_transform(X_train)
                    X_test_scaled = scaler.transform(X_test)
                    
                    if "Linear" in model_type:
                        model = LinearRegression()
                    else:
                        model = RandomForestRegressor(n_estimators=100, random_state=42)
                    
                    model.fit(X_train_scaled, y_train)
                    
                    # Store model and scaler in session state
                    st.session_state.model = model
                    st.session_state.scaler = scaler
                    st.session_state.features = selected_features
                    
                    # Model evaluation
                    train_score = model.score(X_train_scaled, y_train)
                    test_score = model.score(X_test_scaled, y_test)
                    
                    st.success("✨ Model trained successfully!")
                    
                    # Display metrics
                    metric_col1, metric_col2 = st.columns(2)
                    with metric_col1:
                        st.metric("Training R² Score", f"{train_score:.4f}")
                    with metric_col2:
                        st.metric("Testing R² Score", f"{test_score:.4f}")
                    
                    # Feature importance for Random Forest
                    if "Random" in model_type:
                        st.markdown("### 📊 Feature Importance")
                        importance = pd.DataFrame({
                            'Feature': selected_features,
                            'Importance': model.feature_importances_
                        }).sort_values('Importance', ascending=False)
                        
                        fig, ax = plt.subplots(figsize=(10, 6))
                        sns.barplot(x='Importance', y='Feature', data=importance, ax=ax)
                        ax.set_title("Feature Importance")
                        st.pyplot(fig)
            else:
                st.error("⚠️ Please select at least one feature")
    
    # Prediction section
    st.markdown("### 🎯 Make Predictions")
    if 'model' in st.session_state:
        pred_col1, pred_col2 = st.columns([2, 1])
        
        with pred_col1:
            st.markdown("#### Input Features")
            input_data = {}
            
            # Create input fields for each feature
            for feature in st.session_state.features:
                min_val = float(st.session_state.data[feature].min())
                max_val = float(st.session_state.data[feature].max())
                mean_val = float(st.session_state.data[feature].mean())
                
                input_data[feature] = st.slider(
                    f"{feature}:",
                    min_value=min_val,
                    max_value=max_val,
                    value=mean_val,
                    help=f"Range: {min_val:.2f} to {max_val:.2f}"
                )
        
        with pred_col2:
            if st.button("🎯 Predict", use_container_width=True):
                input_df = pd.DataFrame([input_data])
                input_scaled = st.session_state.scaler.transform(input_df)
                prediction = st.session_state.model.predict(input_scaled)[0]
                
                st.success(f"Predicted {target_column}: {prediction:.2f}")
    else:
        st.info("ℹ️ Train a model first to make predictions")