src/streamlit_app.py

import streamlit as st
import pandas as pd
import plotly.express as px
from streamlit_option_menu import option_menu
import numpy as np
from datetime import datetime, timedelta

# Import our modules
from data.synthetic_data import SAPDataGenerator
from agents.procurement_agent import ProcurementAgent
from utils.charts import ProcurementCharts

# Page configuration
st.set_page_config(
    page_title="🚀 SAP S/4HANA Procurement AI",
    page_icon="🚀",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Custom CSS for beautiful UI
st.markdown("""
<style>
    .main-header {
        font-size: 3rem;
        font-weight: bold;
        text-align: center;
        background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
        -webkit-background-clip: text;
        -webkit-text-fill-color: transparent;
        margin-bottom: 2rem;
    }
    
    .metric-card {
        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
        padding: 1rem;
        border-radius: 10px;
        color: white;
        text-align: center;
        margin: 0.5rem 0;
    }
    
    .insight-box {
        background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%);
        padding: 1.5rem;
        border-radius: 15px;
        color: white;
        margin: 1rem 0;
    }
    
    .recommendation-box {
        background: linear-gradient(135deg, #4facfe 0%, #00f2fe 100%);
        padding: 1rem;
        border-radius: 10px;
        color: white;
        margin: 0.5rem 0;
    }
    
    .sidebar .sidebar-content {
        background: linear-gradient(180deg, #667eea 0%, #764ba2 100%);
    }
</style>
""", unsafe_allow_html=True)

# Initialize session state
if 'data_loaded' not in st.session_state:
    st.session_state.data_loaded = False
    st.session_state.po_data = None
    st.session_state.supplier_data = None
    st.session_state.spend_data = None

@st.cache_data
def load_synthetic_data():
    """Load and cache synthetic data"""
    generator = SAPDataGenerator()
    po_data = generator.generate_purchase_orders(1000)
    supplier_data = generator.generate_supplier_performance()
    spend_data = generator.generate_spend_analysis()
    return po_data, supplier_data, spend_data

def main():
    # Main header
    st.markdown('<h1 class="main-header">🚀 SAP S/4HANA Procurement AI Assistant</h1>', unsafe_allow_html=True)
    st.markdown('<p style="text-align: center; font-size: 1.2rem; color: #666;">Intelligent Procurement Analytics with AI-Powered Insights</p>', unsafe_allow_html=True)
    
    # Load data
    if not st.session_state.data_loaded:
        with st.spinner("🔄 Loading SAP S/4HANA Data..."):
            po_data, supplier_data, spend_data = load_synthetic_data()
            st.session_state.po_data = po_data
            st.session_state.supplier_data = supplier_data
            st.session_state.spend_data = spend_data
            st.session_state.data_loaded = True
    
    # Sidebar navigation
    with st.sidebar:
        st.image("https://via.placeholder.com/200x80/667eea/white?text=SAP+S/4HANA", width=200)
        
        selected = option_menu(
            menu_title="Navigation",
            options=["🏠 Dashboard", "📊 Analytics", "🤖 AI Insights", "🔍 Deep Dive", "⚙️ Settings"],
            icons=["house", "graph-up", "robot", "search", "gear"],
            menu_icon="cast",
            default_index=0,
            styles={
                "container": {"padding": "0!important", "background-color": "#fafafa"},
                "icon": {"color": "#667eea", "font-size": "18px"},
                "nav-link": {"font-size": "16px", "text-align": "left", "margin": "0px", "--hover-color": "#eee"},
                "nav-link-selected": {"background-color": "#667eea"},
            }
        )
    
    # Initialize AI agent
    agent = ProcurementAgent()
    charts = ProcurementCharts()
    
    # Main content based on selection
    if selected == "🏠 Dashboard":
        show_dashboard(st.session_state.po_data, st.session_state.supplier_data, charts)
    elif selected == "📊 Analytics":
        show_analytics(st.session_state.po_data, st.session_state.spend_data, charts)
    elif selected == "🤖 AI Insights":
        show_ai_insights(st.session_state.po_data, st.session_state.supplier_data, agent)
    elif selected == "🔍 Deep Dive":
        show_deep_dive(st.session_state.po_data, st.session_state.supplier_data)
    elif selected == "⚙️ Settings":
        show_settings()

def show_dashboard(po_data, supplier_data, charts):
    st.subheader("📈 Executive Dashboard")
    
    # KPI Metrics
    col1, col2, col3, col4 = st.columns(4)
    
    total_spend = po_data['Total_Value'].sum()
    total_pos = len(po_data)
    avg_delivery = po_data['Delivery_Performance'].mean()
    top_supplier = po_data.groupby('Supplier')['Total_Value'].sum().idxmax()
    
    with col1:
        st.markdown(f"""
        <div class="metric-card">
            <h3>💰 Total Spend</h3>
            <h2>${total_spend:,.0f}</h2>
        </div>
        """, unsafe_allow_html=True)
    
    with col2:
        st.markdown(f"""
        <div class="metric-card">
            <h3>📋 Purchase Orders</h3>
            <h2>{total_pos:,}</h2>
        </div>
        """, unsafe_allow_html=True)
    
    with col3:
        st.markdown(f"""
        <div class="metric-card">
            <h3>🎯 Avg Delivery</h3>
            <h2>{avg_delivery:.1f}%</h2>
        </div>
        """, unsafe_allow_html=True)
    
    with col4:
        st.markdown(f"""
        <div class="metric-card">
            <h3>🏆 Top Supplier</h3>
            <h2>{top_supplier}</h2>
        </div>
        """, unsafe_allow_html=True)
    
    st.markdown("---")
    
    # Charts
    col1, col2 = st.columns(2)
    
    with col1:
        fig_trend = charts.create_spend_trend_chart(po_data)
        st.plotly_chart(fig_trend, use_container_width=True)
    
    with col2:
        fig_category = charts.create_category_pie_chart(po_data)
        st.plotly_chart(fig_category, use_container_width=True)
    
    # Status and Performance
    col1, col2 = st.columns(2)
    
    with col1:
        fig_status = charts.create_status_donut_chart(po_data)
        st.plotly_chart(fig_status, use_container_width=True)
    
    with col2:
        fig_supplier = charts.create_supplier_performance_chart(po_data)
        st.plotly_chart(fig_supplier, use_container_width=True)

def show_analytics(po_data, spend_data, charts):
    st.subheader("📊 Advanced Analytics")
    
    # Filter controls
    col1, col2, col3 = st.columns(3)
    
    with col1:
        selected_suppliers = st.multiselect(
            "Select Suppliers:",
            options=po_data['Supplier'].unique(),
            default=po_data['Supplier'].unique()[:5]
        )
    
    with col2:
        selected_categories = st.multiselect(
            "Select Categories:",
            options=po_data['Category'].unique(),
            default=po_data['Category'].unique()[:5]
        )
    
    with col3:
        date_range = st.date_input(
            "Date Range:",
            value=(po_data['PO_Date'].min(), po_data['PO_Date'].max()),
            min_value=po_data['PO_Date'].min(),
            max_value=po_data['PO_Date'].max()
        )
    
    # Filter data
    filtered_data = po_data[
        (po_data['Supplier'].isin(selected_suppliers)) &
        (po_data['Category'].isin(selected_categories))
    ]
    
    st.markdown("---")
    
    # Advanced Charts
    tab1, tab2, tab3 = st.tabs(["📈 Trends", "🏢 Suppliers", "📦 Categories"])
    
    with tab1:
        col1, col2 = st.columns(2)
        with col1:
            # Monthly trend
            fig_trend = charts.create_spend_trend_chart(filtered_data)
            st.plotly_chart(fig_trend, use_container_width=True)
        
        with col2:
            # Delivery performance over time
            monthly_delivery = filtered_data.groupby(filtered_data['PO_Date'].dt.to_period('M'))['Delivery_Performance'].mean().reset_index()
            monthly_delivery['PO_Date'] = monthly_delivery['PO_Date'].astype(str)
            
            fig = px.bar(monthly_delivery, x='PO_Date', y='Delivery_Performance',
                        title='🚚 Monthly Delivery Performance',
                        color='Delivery_Performance',
                        color_continuous_scale='RdYlGn')
            fig.update_layout(height=400, plot_bgcolor='rgba(0,0,0,0)')
            st.plotly_chart(fig, use_container_width=True)
    
    with tab2:
        # Supplier analysis
        supplier_summary = filtered_data.groupby('Supplier').agg({
            'Total_Value': ['sum', 'mean', 'count'],
            'Delivery_Performance': 'mean'
        }).round(2)
        
        supplier_summary.columns = ['Total Spend', 'Avg PO Value', 'PO Count', 'Delivery %']
        supplier_summary = supplier_summary.reset_index()
        
        st.dataframe(
            supplier_summary.style.highlight_max(axis=0),
            use_container_width=True,
            height=400
        )
    
    with tab3:
        # Category deep dive
        category_analysis = filtered_data.groupby('Category').agg({
            'Total_Value': 'sum',
            'Quantity': 'sum',
            'Unit_Price': 'mean',
            'Delivery_Performance': 'mean'
        }).round(2)
        
        st.dataframe(
            category_analysis.style.highlight_max(axis=0),
            use_container_width=True,
            height=400
        )

def show_ai_insights(po_data, supplier_data, agent):
    st.subheader("🤖 AI-Powered Procurement Insights")
    
    # Generate insights
    with st.spinner("🧠 AI Agent is analyzing your procurement data..."):
        insights = agent.generate_insights(po_data, supplier_data)
    
    # Executive Summary
    st.markdown(f"""
    <div class="insight-box">
        <h3>📋 Executive Summary</h3>
        {insights['summary']}
    </div>
    """, unsafe_allow_html=True)
    
    # Tabs for different insights
    tab1, tab2, tab3 = st.tabs(["💰 Spend Analysis", "🏢 Supplier Intelligence", "⚠️ Risk Alerts"])
    
    with tab1:
        spend_insights = insights['spend_analysis']
        
        col1, col2 = st.columns(2)
        with col1:
            st.metric("Total Spend", f"${spend_insights.get('total_spend', 0):,.2f}")
            st.metric("Avg PO Value", f"${spend_insights.get('avg_po_value', 0):,.2f}")
        
        with col2:
            st.metric("Spending Trend", spend_insights.get('monthly_trend', 'N/A').title())
            st.metric("Top Category", spend_insights.get('top_category', 'N/A'))
        
        st.subheader("🎯 AI Recommendations")
        for recommendation in spend_insights.get('recommendations', []):
            st.markdown(f"""
            <div class="recommendation-box">
                {recommendation}
            </div>
            """, unsafe_allow_html=True)
    
    with tab2:
        supplier_insights = insights['supplier_analysis']
        
        col1, col2 = st.columns(2)
        with col1:
            best = supplier_insights.get('best_performer', {})
            st.success(f"🏆 Best Performer: {best.get('name', 'N/A')} ({best.get('performance', 0):.1f}%)")
        
        with col2:
            worst = supplier_insights.get('worst_performer', {})
            st.error(f"⚠️ Needs Improvement: {worst.get('name', 'N/A')} ({worst.get('performance', 0):.1f}%)")
        
        st.subheader("📈 Supplier Recommendations")
        for recommendation in supplier_insights.get('recommendations', []):
            st.markdown(f"""
            <div class="recommendation-box">
                {recommendation}
            </div>
            """, unsafe_allow_html=True)
    
    with tab3:
        anomalies = insights['anomalies']
        
        if anomalies:
            st.subheader(f"🚨 {len(anomalies)} Critical Issues Detected")
            
            for anomaly in anomalies:
                risk_color = {"High": "🔴", "Medium": "🟡", "Low": "🟢"}
                
                st.markdown(f"""
                <div class="recommendation-box">
                    <strong>{risk_color.get(anomaly.get('risk_level', 'Medium'), '🟡')} {anomaly['type']}</strong><br>
                    PO: {anomaly.get('po_number', 'N/A')} | Supplier: {anomaly.get('supplier', 'N/A')}<br>
                    Risk Level: {anomaly.get('risk_level', 'Unknown')}
                </div>
                """, unsafe_allow_html=True)
        else:
            st.success("🎉 No critical issues detected in your procurement data!")

def show_deep_dive(po_data, supplier_data):
    st.subheader("🔍 Deep Dive Analysis")
    
    # Data explorer
    st.subheader("📊 Purchase Orders Data Explorer")
    
    # Search and filter
    col1, col2, col3 = st.columns(3)
    with col1:
        search_po = st.text_input("🔍 Search PO Number:")
    with col2:
        filter_status = st.selectbox("Filter by Status:", ['All'] + list(po_data['Status'].unique()))
    with col3:
        min_value = st.number_input("Min PO Value:", min_value=0, value=0)
    
    # Apply filters
    filtered_po = po_data.copy()
    
    if search_po:
        filtered_po = filtered_po[filtered_po['PO_Number'].str.contains(search_po, case=False)]
    
    if filter_status != 'All':
        filtered_po = filtered_po[filtered_po['Status'] == filter_status]
    
    if min_value > 0:
        filtered_po = filtered_po[filtered_po['Total_Value'] >= min_value]
    
    # Display filtered data
    st.dataframe(
        filtered_po.style.highlight_max(axis=0),
        use_container_width=True,
        height=400
    )
    
    # Download data
    csv = filtered_po.to_csv(index=False)
    st.download_button(
        label="📥 Download Filtered Data",
        data=csv,
        file_name=f"procurement_data_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv",
        mime="text/csv"
    )

def show_settings():
    st.subheader("⚙️ Application Settings")
    
    st.info("🚀 **Demo Configuration**")
    
    col1, col2 = st.columns(2)
    
    with col1:
        st.markdown("### 📊 Data Settings")
        data_refresh = st.button("🔄 Refresh Synthetic Data")
        if data_refresh:
            st.session_state.data_loaded = False
            st.rerun()
        
        st.markdown("### 🎨 Theme Settings")
        theme = st.selectbox("Choose Theme:", ["Default", "Dark", "Light"])
        
    with col2:
        st.markdown("### 🤖 AI Settings")
        ai_model = st.selectbox("AI Model:", ["GPT-4", "Claude", "Local Model"])
        confidence = st.slider("Confidence Threshold:", 0.0, 1.0, 0.8)
        
        st.markdown("### 📈 Chart Settings")
        chart_style = st.selectbox("Chart Style:", ["Modern", "Classic", "Minimal"])
    
    st.markdown("---")
    st.markdown("### 📋 Application Info")
    st.json({
        "version": "1.0.0",
        "framework": "Streamlit",
        "data_source": "Synthetic SAP S/4HANA",
        "ai_agent": "Custom Procurement Agent",
        "last_updated": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    })

if __name__ == "__main__":
    main()