app.py

"""

Demand Prediction System - Streamlit Dashboard



Interactive dashboard for visualizing sales trends and making demand predictions.

"""

import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import joblib
import json
from datetime import datetime, timedelta, date as dt_date
import os
import warnings
warnings.filterwarnings('ignore')

# Page configuration
st.set_page_config(
    page_title="Demand Prediction Dashboard",
    page_icon="📊",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Custom CSS for better styling
st.markdown("""

    <style>

    .main-header {

        font-size: 2.5rem;

        font-weight: bold;

        color: #1f77b4;

        text-align: center;

        margin-bottom: 2rem;

    }

    .metric-card {

        background-color: #f0f2f6;

        padding: 1rem;

        border-radius: 0.5rem;

        margin: 0.5rem 0;

    }

    .stButton>button {

        width: 100%;

        background-color: #1f77b4;

        color: white;

    }

    </style>

""", unsafe_allow_html=True)

# Configuration
DATA_PATH = 'data/sales.csv'
MODEL_DIR = 'models'
MODEL_PATH = f'{MODEL_DIR}/best_model.joblib'
TS_MODEL_PATH = f'{MODEL_DIR}/best_timeseries_model.joblib'
PREPROCESSING_PATH = f'{MODEL_DIR}/preprocessing.joblib'
ALL_MODELS_METADATA_PATH = f'{MODEL_DIR}/all_models_metadata.json'


@st.cache_data
def load_data():
    """Load sales data with caching."""
    if os.path.exists(DATA_PATH):
        df = pd.read_csv(DATA_PATH)
        df['date'] = pd.to_datetime(df['date'])
        return df
    return None


@st.cache_resource
def load_models():
    """Load trained models with caching."""
    models = {
        'ml_model': None,
        'ts_model': None,
        'preprocessing': None,
        'model_name': None,
        'is_timeseries': False,
        'metadata': None
    }
    
    # Load metadata
    if os.path.exists(ALL_MODELS_METADATA_PATH):
        with open(ALL_MODELS_METADATA_PATH, 'r') as f:
            models['metadata'] = json.load(f)
            models['model_name'] = models['metadata'].get('best_model', 'Unknown')
            models['is_timeseries'] = models['model_name'] in ['ARIMA', 'Prophet']
    
    # Load ML model
    if os.path.exists(MODEL_PATH):
        try:
            models['ml_model'] = joblib.load(MODEL_PATH)
        except:
            pass
    
    # Load time-series model
    if os.path.exists(TS_MODEL_PATH):
        try:
            models['ts_model'] = joblib.load(TS_MODEL_PATH)
        except:
            pass
    
    # Load preprocessing
    if os.path.exists(PREPROCESSING_PATH):
        try:
            models['preprocessing'] = joblib.load(PREPROCESSING_PATH)
        except:
            pass
    
    return models


def prepare_features_ml(product_id, date, price, discount, category, preprocessing_data):
    """Prepare features for ML model prediction."""
    if preprocessing_data is None:
        return None
    
    # Convert date to pandas Timestamp (handles date, datetime, and string)
    # Handle datetime.date objects explicitly
    if isinstance(date, dt_date):
        date = pd.Timestamp(date)
    elif not isinstance(date, pd.Timestamp):
        date = pd.to_datetime(date)
    
    # Extract date features
    day = date.day
    month = date.month
    day_of_week = date.weekday()
    weekend = 1 if day_of_week >= 5 else 0
    year = date.year
    quarter = date.quarter
    
    # Encode categorical variables
    category_encoder = preprocessing_data['encoders']['category']
    product_encoder = preprocessing_data['encoders']['product_id']
    
    try:
        category_encoded = category_encoder.transform([category])[0]
    except ValueError:
        category_encoded = 0
    
    try:
        product_id_encoded = product_encoder.transform([product_id])[0]
    except ValueError:
        product_id_encoded = product_encoder.transform([product_encoder.classes_[0]])[0]
    
    # Create feature dictionary
    feature_dict = {
        'price': price,
        'discount': discount,
        'day': day,
        'month': month,
        'day_of_week': day_of_week,
        'weekend': weekend,
        'year': year,
        'quarter': quarter,
        'category_encoded': category_encoded,
        'product_id_encoded': product_id_encoded
    }
    
    # Create feature array in the same order as training
    feature_names = preprocessing_data['feature_names']
    features = np.array([[feature_dict[name] for name in feature_names]])
    
    # Scale features
    scaler = preprocessing_data['scaler']
    features_scaled = scaler.transform(features)
    
    return features_scaled


def predict_ml(product_id, date, price, discount, category, model, preprocessing_data):
    """Make prediction using ML model."""
    features = prepare_features_ml(product_id, date, price, discount, category, preprocessing_data)
    if features is None:
        return None
    prediction = model.predict(features)[0]
    return max(0, prediction)


def predict_timeseries(date, model, model_name):
    """Make prediction using time-series model."""
    # Convert date to pandas Timestamp (handles date, datetime, and string)
    if isinstance(date, dt_date):
        date = pd.Timestamp(date)
    elif not isinstance(date, pd.Timestamp):
        date = pd.to_datetime(date)
    
    if model_name == 'ARIMA':
        try:
            forecast = model.forecast(steps=1)
            prediction = forecast[0] if hasattr(forecast, '__iter__') else forecast
            return max(0, prediction)
        except:
            return None
    
    elif model_name == 'Prophet':
        try:
            future = pd.DataFrame({'ds': [date]})
            forecast = model.predict(future)
            prediction = forecast['yhat'].iloc[0]
            return max(0, prediction)
        except:
            return None
    
    return None


def main():
    """Main dashboard function."""
    
    # Header
    st.markdown('<h1 class="main-header">📊 Demand Prediction Dashboard</h1>', unsafe_allow_html=True)
    
    # Load data
    df = load_data()
    if df is None:
        st.error("❌ Sales data not found. Please run generate_dataset.py first.")
        return
    
    # Load models
    models = load_models()
    
    # Sidebar
    with st.sidebar:
        st.header("⚙️ Navigation")
        page = st.radio(
            "Select Page",
            ["📈 Sales Trends", "🔮 Demand Prediction", "📊 Model Comparison"],
            index=0
        )
        
        st.markdown("---")
        st.header("ℹ️ Information")
        if models['metadata']:
            best_model = models['metadata'].get('best_model', 'Unknown')
            st.info(f"**Best Model:** {best_model}")
            if best_model in models['metadata'].get('all_models', {}):
                metrics = models['metadata']['all_models'][best_model]
                st.metric("R2 Score", f"{metrics.get('r2', 0):.4f}")
    
    # Main content based on selected page
    if page == "📈 Sales Trends":
        show_sales_trends(df)
    elif page == "🔮 Demand Prediction":
        show_prediction_interface(df, models)
    elif page == "📊 Model Comparison":
        show_model_comparison(models)


def show_sales_trends(df):
    """Display sales trends visualizations."""
    st.header("📈 Sales Trends Analysis")
    
    # Filters
    col1, col2, col3 = st.columns(3)
    
    with col1:
        categories = ['All'] + sorted(df['category'].unique().tolist())
        selected_category = st.selectbox("Select Category", categories)
    
    with col2:
        products = ['All'] + sorted(df['product_id'].unique().tolist())
        selected_product = st.selectbox("Select Product", products)
    
    with col3:
        date_range = st.date_input(
            "Select Date Range",
            value=(df['date'].min(), df['date'].max()),
            min_value=df['date'].min(),
            max_value=df['date'].max()
        )
    
    # Filter data
    filtered_df = df.copy()
    
    if selected_category != 'All':
        filtered_df = filtered_df[filtered_df['category'] == selected_category]
    
    if selected_product != 'All':
        filtered_df = filtered_df[filtered_df['product_id'] == int(selected_product)]
    
    if isinstance(date_range, tuple) and len(date_range) == 2:
        filtered_df = filtered_df[
            (filtered_df['date'] >= pd.to_datetime(date_range[0])) &
            (filtered_df['date'] <= pd.to_datetime(date_range[1]))
        ]
    
    if len(filtered_df) == 0:
        st.warning("No data available for selected filters.")
        return
    
    # Visualizations
    tab1, tab2, tab3, tab4 = st.tabs(["📅 Daily Trends", "📆 Monthly Trends", "📦 Category Analysis", "💰 Price vs Demand"])
    
    with tab1:
        st.subheader("Daily Sales Trends")
        daily_sales = filtered_df.groupby('date')['sales_quantity'].sum().reset_index()
        
        fig, ax = plt.subplots(figsize=(14, 6))
        ax.plot(daily_sales['date'], daily_sales['sales_quantity'], linewidth=2, alpha=0.7)
        ax.set_title('Total Daily Sales Quantity', fontsize=16, fontweight='bold')
        ax.set_xlabel('Date', fontsize=12)
        ax.set_ylabel('Sales Quantity', fontsize=12)
        ax.grid(True, alpha=0.3)
        plt.xticks(rotation=45)
        plt.tight_layout()
        st.pyplot(fig)
        
        # Statistics
        col1, col2, col3, col4 = st.columns(4)
        with col1:
            st.metric("Total Sales", f"{daily_sales['sales_quantity'].sum():,.0f}")
        with col2:
            st.metric("Average Daily", f"{daily_sales['sales_quantity'].mean():.1f}")
        with col3:
            st.metric("Max Daily", f"{daily_sales['sales_quantity'].max():,.0f}")
        with col4:
            st.metric("Min Daily", f"{daily_sales['sales_quantity'].min():,.0f}")
    
    with tab2:
        st.subheader("Monthly Sales Trends")
        filtered_df['month_year'] = filtered_df['date'].dt.to_period('M')
        monthly_sales = filtered_df.groupby('month_year')['sales_quantity'].sum().reset_index()
        monthly_sales['month_year'] = monthly_sales['month_year'].astype(str)
        
        fig, ax = plt.subplots(figsize=(14, 6))
        ax.bar(range(len(monthly_sales)), monthly_sales['sales_quantity'], alpha=0.7, color='steelblue')
        ax.set_title('Monthly Sales Quantity', fontsize=16, fontweight='bold')
        ax.set_xlabel('Month', fontsize=12)
        ax.set_ylabel('Sales Quantity', fontsize=12)
        ax.set_xticks(range(len(monthly_sales)))
        ax.set_xticklabels(monthly_sales['month_year'], rotation=45, ha='right')
        ax.grid(True, alpha=0.3, axis='y')
        plt.tight_layout()
        st.pyplot(fig)
    
    with tab3:
        st.subheader("Sales by Category")
        category_sales = filtered_df.groupby('category')['sales_quantity'].sum().sort_values(ascending=False)
        
        fig, ax = plt.subplots(figsize=(12, 6))
        category_sales.plot(kind='barh', ax=ax, color='coral', alpha=0.7)
        ax.set_title('Total Sales by Category', fontsize=16, fontweight='bold')
        ax.set_xlabel('Total Sales Quantity', fontsize=12)
        ax.set_ylabel('Category', fontsize=12)
        ax.grid(True, alpha=0.3, axis='x')
        plt.tight_layout()
        st.pyplot(fig)
        
        # Category statistics table
        category_stats = filtered_df.groupby('category').agg({
            'sales_quantity': ['sum', 'mean', 'std', 'min', 'max']
        }).round(2)
        category_stats.columns = ['Total', 'Average', 'Std Dev', 'Min', 'Max']
        st.dataframe(category_stats, use_container_width=True)
    
    with tab4:
        st.subheader("Price vs Demand Relationship")
        
        # Scatter plot
        fig, ax = plt.subplots(figsize=(12, 6))
        scatter = ax.scatter(filtered_df['price'], filtered_df['sales_quantity'], 
                           c=filtered_df['discount'], cmap='viridis', alpha=0.6, s=50)
        ax.set_title('Price vs Sales Quantity (colored by discount)', fontsize=16, fontweight='bold')
        ax.set_xlabel('Price', fontsize=12)
        ax.set_ylabel('Sales Quantity', fontsize=12)
        ax.grid(True, alpha=0.3)
        plt.colorbar(scatter, ax=ax, label='Discount %')
        plt.tight_layout()
        st.pyplot(fig)
        
        # Correlation
        correlation = filtered_df['price'].corr(filtered_df['sales_quantity'])
        st.metric("Price-Demand Correlation", f"{correlation:.3f}")


def show_prediction_interface(df, models):
    """Display interactive prediction interface."""
    st.header("🔮 Demand Prediction")
    
    # Check if models are available
    if models['ml_model'] is None and models['ts_model'] is None:
        st.error("❌ No trained models found. Please run train_model.py first.")
        return
    
    # Model selection
    model_type = st.radio(
        "Select Model Type",
        ["Auto (Best Model)", "Machine Learning", "Time-Series"],
        horizontal=True
    )
    
    st.markdown("---")
    
    if model_type == "Time-Series" or (model_type == "Auto (Best Model)" and models['is_timeseries']):
        # Time-series prediction
        st.subheader("Overall Daily Demand Prediction")
        
        col1, col2 = st.columns(2)
        with col1:
            prediction_date = st.date_input(
                "Select Date for Prediction",
                value=datetime.now().date() + timedelta(days=30),
                min_value=df['date'].max().date() + timedelta(days=1)
            )
        
        with col2:
            st.write("")  # Spacing
            st.write("")  # Spacing
        
        if st.button("🔮 Predict Demand", type="primary"):
            if models['ts_model'] is None:
                st.error("Time-series model not available.")
            else:
                with st.spinner("Making prediction..."):
                    prediction = predict_timeseries(
                        prediction_date,
                        models['ts_model'],
                        models['model_name']
                    )
                    
                    if prediction is not None:
                        st.success(f"✅ Prediction Complete!")
                        
                        col1, col2, col3 = st.columns(3)
                        with col1:
                            st.metric("Predicted Daily Demand", f"{prediction:,.0f} units")
                        with col2:
                            day_name = pd.to_datetime(prediction_date).strftime('%A')
                            st.metric("Day of Week", day_name)
                        with col3:
                            is_weekend = "Yes" if pd.to_datetime(prediction_date).weekday() >= 5 else "No"
                            st.metric("Weekend", is_weekend)
                        
                        st.info("💡 This prediction represents the total daily demand across all products.")
                    else:
                        st.error("Failed to make prediction.")
    
    else:
        # ML model prediction
        st.subheader("Product-Specific Demand Prediction")
        
        # Get unique values for dropdowns
        categories = sorted(df['category'].unique().tolist())
        products = sorted(df['product_id'].unique().tolist())
        
        col1, col2 = st.columns(2)
        
        with col1:
            selected_category = st.selectbox("Select Category", categories)
            selected_product = st.selectbox("Select Product ID", products)
            prediction_date = st.date_input(
                "Select Date for Prediction",
                value=datetime.now().date() + timedelta(days=30),
                min_value=df['date'].max().date() + timedelta(days=1)
            )
        
        with col2:
            price = st.number_input(
                "Product Price ($)",
                min_value=0.01,
                value=100.0,
                step=1.0,
                format="%.2f"
            )
            discount = st.slider(
                "Discount (%)",
                min_value=0,
                max_value=100,
                value=0,
                step=5
            )
        
        # Show product statistics
        product_data = df[df['product_id'] == selected_product]
        if len(product_data) > 0:
            with st.expander("📊 Product Statistics"):
                col1, col2, col3, col4 = st.columns(4)
                with col1:
                    st.metric("Avg Price", f"${product_data['price'].mean():.2f}")
                with col2:
                    st.metric("Avg Sales", f"{product_data['sales_quantity'].mean():.1f}")
                with col3:
                    st.metric("Total Sales", f"{product_data['sales_quantity'].sum():,.0f}")
                with col4:
                    st.metric("Category", selected_category)
        
        if st.button("🔮 Predict Demand", type="primary"):
            if models['ml_model'] is None or models['preprocessing'] is None:
                st.error("ML model or preprocessing not available.")
            else:
                with st.spinner("Making prediction..."):
                    prediction = predict_ml(
                        selected_product,
                        prediction_date,
                        price,
                        discount,
                        selected_category,
                        models['ml_model'],
                        models['preprocessing']
                    )
                    
                    if prediction is not None:
                        st.success(f"✅ Prediction Complete!")
                        
                        col1, col2, col3, col4 = st.columns(4)
                        with col1:
                            st.metric("Predicted Demand", f"{prediction:,.0f} units")
                        with col2:
                            st.metric("Price", f"${price:.2f}")
                        with col3:
                            st.metric("Discount", f"{discount}%")
                        with col4:
                            day_name = pd.to_datetime(prediction_date).strftime('%A')
                            st.metric("Day", day_name)
                        
                        # Additional insights
                        st.markdown("### 📈 Prediction Insights")
                        date_obj = pd.to_datetime(prediction_date)
                        is_weekend = date_obj.weekday() >= 5
                        month = date_obj.month
                        
                        insights = []
                        if is_weekend:
                            insights.append("📅 Weekend - typically higher demand")
                        if month in [11, 12]:
                            insights.append("🎄 Holiday season - peak sales period")
                        if discount > 0:
                            insights.append(f"💰 {discount}% discount - may increase demand")
                        
                        if insights:
                            for insight in insights:
                                st.info(insight)
                    else:
                        st.error("Failed to make prediction.")


def show_model_comparison(models):
    """Display model comparison."""
    st.header("📊 Model Comparison")
    
    if models['metadata'] is None:
        st.warning("Model metadata not available. Please run train_model.py first.")
        return
    
    metadata = models['metadata']
    all_models = metadata.get('all_models', {})
    best_model = metadata.get('best_model', 'Unknown')
    
    if not all_models:
        st.warning("No model comparison data available.")
        return
    
    # Model metrics table
    st.subheader("Model Performance Metrics")
    
    comparison_data = []
    for model_name, metrics in all_models.items():
        comparison_data.append({
            'Model': model_name,
            'Type': 'Time-Series' if model_name in ['ARIMA', 'Prophet'] else 'Machine Learning',
            'MAE': metrics.get('mae', 0),
            'RMSE': metrics.get('rmse', 0),
            'R2 Score': metrics.get('r2', 0)
        })
    
    comparison_df = pd.DataFrame(comparison_data)
    
    # Highlight best model
    def highlight_best(row):
        if row['Model'] == best_model:
            return ['background-color: #90EE90'] * len(row)
        return [''] * len(row)
    
    st.dataframe(
        comparison_df.style.apply(highlight_best, axis=1),
        use_container_width=True
    )
    
    # Visualizations
    st.subheader("Performance Comparison Charts")
    
    col1, col2 = st.columns(2)
    
    with col1:
        fig, ax = plt.subplots(figsize=(10, 6))
        model_names = comparison_df['Model'].tolist()
        mae_scores = comparison_df['MAE'].tolist()
        
        colors = ['coral' if name in ['ARIMA', 'Prophet'] else 'skyblue' for name in model_names]
        ax.bar(model_names, mae_scores, color=colors, alpha=0.7)
        ax.set_title('MAE Comparison (Lower is Better)', fontsize=14, fontweight='bold')
        ax.set_ylabel('MAE', fontsize=12)
        ax.tick_params(axis='x', rotation=45)
        ax.grid(True, alpha=0.3, axis='y')
        plt.tight_layout()
        st.pyplot(fig)
    
    with col2:
        fig, ax = plt.subplots(figsize=(10, 6))
        r2_scores = comparison_df['R2 Score'].tolist()
        
        colors = ['coral' if name in ['ARIMA', 'Prophet'] else 'skyblue' for name in model_names]
        ax.bar(model_names, r2_scores, color=colors, alpha=0.7)
        ax.set_title('R2 Score Comparison (Higher is Better)', fontsize=14, fontweight='bold')
        ax.set_ylabel('R2 Score', fontsize=12)
        ax.tick_params(axis='x', rotation=45)
        ax.grid(True, alpha=0.3, axis='y')
        plt.tight_layout()
        st.pyplot(fig)
    
    # Best model info
    st.markdown("---")
    st.success(f"🏆 **Best Model: {best_model}**")
    if best_model in all_models:
        best_metrics = all_models[best_model]
        col1, col2, col3 = st.columns(3)
        with col1:
            st.metric("MAE", f"{best_metrics.get('mae', 0):.2f}")
        with col2:
            st.metric("RMSE", f"{best_metrics.get('rmse', 0):.2f}")
        with col3:
            st.metric("R2 Score", f"{best_metrics.get('r2', 0):.4f}")


if __name__ == "__main__":
    main()