import streamlit as st
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from datetime import datetime, timedelta

# Page config
st.set_page_config(
    page_title="Supply Chain Intelligence",
    page_icon="🔗",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Clean, professional CSS styling
st.markdown("""
<style>
    /* Import modern font */
    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap');
    
    /* Global styling */
    .stApp {
        font-family: 'Inter', sans-serif;
        background-color: #f8fafc;
    }
    
    /* Main container */
    .main-container {
        background: white;
        border-radius: 12px;
        padding: 2rem;
        margin: 1rem 0;
        box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1);
        border: 1px solid #e2e8f0;
    }
    
    /* Clean header */
    .modern-header {
        background: #1e293b;
        color: white;
        padding: 2rem;
        border-radius: 12px;
        margin-bottom: 2rem;
        border-left: 4px solid #3b82f6;
    }
    
    .header-title {
        font-size: 2rem;
        font-weight: 600;
        margin-bottom: 0.5rem;
        color: white;
    }
    
    .header-subtitle {
        font-size: 1rem;
        font-weight: 400;
        color: #94a3b8;
    }
    
    /* Clean metric cards */
    .metric-card {
        background: white;
        padding: 1.5rem;
        border-radius: 12px;
        border: 1px solid #e2e8f0;
        box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1);
        margin-bottom: 1rem;
        transition: all 0.2s ease;
    }
    
    .metric-card:hover {
        box-shadow: 0 4px 12px rgba(0, 0, 0, 0.15);
        border-color: #cbd5e1;
    }
    
    .metric-number {
        font-size: 2.5rem;
        font-weight: 700;
        color: #1e293b;
        margin-bottom: 0.5rem;
    }
    
    .metric-label {
        color: #64748b;
        font-size: 0.875rem;
        font-weight: 500;
        text-transform: uppercase;
        letter-spacing: 0.05em;
        margin-bottom: 0.5rem;
    }
    
    .metric-change {
        font-size: 0.875rem;
        font-weight: 600;
        padding: 0.25rem 0.75rem;
        border-radius: 6px;
        display: inline-block;
    }
    
    .metric-positive {
        background-color: #dcfce7;
        color: #166534;
    }
    
    .metric-negative {
        background-color: #fef2f2;
        color: #dc2626;
    }
    
    .metric-neutral {
        background-color: #f1f5f9;
        color: #475569;
    }
    
    /* Clean sidebar */
    .sidebar-content {
        background: white;
        color: #1e293b;
        border-radius: 12px;
        padding: 1.5rem;
        margin-bottom: 1rem;
        border: 1px solid #e2e8f0;
        box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1);
    }
    
    /* Filter section */
    .filter-container {
        background: white;
        padding: 1.5rem;
        border-radius: 12px;
        margin-bottom: 2rem;
        border: 1px solid #e2e8f0;
        box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1);
    }
    
    /* Section headers */
    .section-header {
        font-size: 1.25rem;
        font-weight: 600;
        color: #1e293b;
        margin-bottom: 1.5rem;
        padding-bottom: 0.75rem;
        border-bottom: 2px solid #e2e8f0;
    }
    
    /* Status indicators */
    .status-indicator {
        display: inline-block;
        width: 8px;
        height: 8px;
        border-radius: 50%;
        margin-right: 8px;
    }
    
    .status-good { background-color: #22c55e; }
    .status-warning { background-color: #f59e0b; }
    .status-critical { background-color: #ef4444; }
    
    /* Clean table styling */
    .dataframe table {
        border-collapse: collapse;
        margin: 0;
        font-size: 0.875rem;
        width: 100%;
        background: white;
        border-radius: 8px;
        overflow: hidden;
        box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1);
    }
    
    .dataframe th {
        background-color: #f8fafc;
        color: #374151;
        font-weight: 600;
        padding: 12px;
        text-align: left;
        border-bottom: 1px solid #e5e7eb;
    }
    
    .dataframe td {
        padding: 12px;
        border-bottom: 1px solid #f3f4f6;
    }
    
    .dataframe tr:hover {
        background-color: #f9fafb;
    }
    
    /* Remove default streamlit styling */
    .stSelectbox > div > div {
        background-color: white;
        border: 1px solid #d1d5db;
        border-radius: 6px;
    }
    
    .stSelectbox > div > div:focus-within {
        border-color: #3b82f6;
        box-shadow: 0 0 0 3px rgba(59, 130, 246, 0.1);
    }
</style>
""", unsafe_allow_html=True)

# Sample data functions (same as before)
@st.cache_data
def get_material_data():
    return pd.DataFrame({
        'Material Group': ['Engine Components', 'Hydraulic Systems', 'Interior & Seating', 
                          'Battery Systems', 'Suspension Parts', 'Safety Components', 'Semiconductors',
                          'Smart Sensors', 'Brake Systems', 'Shock Absorbers'],
        'Current Rate': [72, 68, 65, 73, 75, 72, 78, 77, 80, 82],
        'Target Rate': [75, 70, 70, 75, 78, 75, 80, 80, 82, 85],
        'Trend': ['+2.1%', '+1.8%', '+0.9%', '+2.4%', '+1.2%', '+1.8%', '+2.1%', '+1.9%', '+1.1%', '+1.2%'],
        'Risk Level': ['Medium', 'High', 'High', 'Low', 'Low', 'Medium', 'Low', 'Low', 'Low', 'Low'],
        'Last Updated': ['2 hrs ago', '1 hr ago', '3 hrs ago', '1 hr ago', '2 hrs ago', '1 hr ago', '30 min ago', '45 min ago', '1 hr ago', '2 hrs ago']
    })

@st.cache_data
def get_enhanced_metrics():
    return {
        'fulfillment': 86,
        'mom_change': 2.3,
        'material_groups': 147,
        'skus': 12847,
        'material_groups_at_risk': 18,
        'risk_mom_change': -1.8,
        'skus_at_risk': 23,
        'sku_risk_mom_change': -2.1,
        'active_suppliers': 342,
        'on_time_delivery': 94.2
    }

# Clean, professional header
st.markdown("""
<div class="modern-header">
    <div class="header-title">Supply Chain Intelligence Hub</div>
    <div class="header-subtitle">Real-time Supply Chain Resilience Dashboard • Control Tower Analytics</div>
</div>
""", unsafe_allow_html=True)

# Professional sidebar
with st.sidebar:
    st.markdown("""
    <div class="sidebar-content">
        <h3 style="margin-top: 0; color: #1e293b;">Navigation</h3>
        <p style="color: #64748b; font-size: 0.875rem;">Select your workspace</p>
    </div>
    """, unsafe_allow_html=True)
    
    nav_options = [
        "Dashboard Home",
        "Supply Chain Resilience", 
        "Control Tower",
        "Material Groups",
        "Supplier Analytics", 
        "Demand Planning",
        "Insights & Trends",
        "Real-time Alerts",
        "Reports Center"
    ]
    
    selected_nav = st.selectbox("", nav_options, index=1)
    
    # Clean alerts section
    st.markdown("---")
    st.markdown("**System Status**")
    st.error("⚠️ 3 suppliers need attention")
    st.warning("📋 12 SKUs below safety stock")
    st.success("✅ 94% on-time delivery")

# Clean filters section
st.markdown("""
<div class="filter-container">
    <div class="section-header">Filters & Controls</div>
</div>
""", unsafe_allow_html=True)

col1, col2, col3, col4 = st.columns(4)
col5, col6, col7, col8 = st.columns(4)

with col1:
    plant_location = st.selectbox("Plant Location", ["Chennai Hub", "Mumbai Center", "Delhi North", "Bangalore Tech"], index=0)

with col2:
    material_group = st.selectbox("Material Category", ["All Categories", "Critical Components", "Standard Parts"], index=0)

with col3:
    time_period = st.selectbox("Time Period", ["Current Quarter", "FY2025", "Last 6 Months"], index=0)

with col4:
    supplier_tier = st.selectbox("Supplier Tier", ["All Tiers", "Tier 1 Strategic", "Tier 2 Operational"], index=0)

with col5:
    risk_level = st.selectbox("Risk Level", ["All Levels", "High Risk Only", "Medium Risk", "Low Risk"], index=0)

with col6:
    performance = st.selectbox("Performance", ["All Performance", "Above Target", "Below Target"], index=0)

with col7:
    geography = st.selectbox("Geography", ["Global View", "Asia Pacific", "Americas", "Europe"], index=0)

with col8:
    update_freq = st.selectbox("Update Frequency", ["Real-time", "Hourly", "Daily"], index=0)

# Get data
material_df = get_material_data()
metrics = get_enhanced_metrics()

# Clean metrics section
st.markdown('<div class="section-header">Key Performance Indicators</div>', unsafe_allow_html=True)

col1, col2, col3, col4 = st.columns(4)

with col1:
    st.markdown(f"""
    <div class="metric-card">
        <div class="metric-number">{metrics['fulfillment']}%</div>
        <div class="metric-label">Overall Fulfillment</div>
        <div class="metric-change metric-positive">↗ +{metrics['mom_change']}% MoM</div>
    </div>
    """, unsafe_allow_html=True)

with col2:
    st.markdown(f"""
    <div class="metric-card">
        <div class="metric-number">{metrics['on_time_delivery']}%</div>
        <div class="metric-label">On-Time Delivery</div>
        <div class="metric-change metric-positive">↗ +1.2% WoW</div>
    </div>
    """, unsafe_allow_html=True)

with col3:
    st.markdown(f"""
    <div class="metric-card">
        <div class="metric-number">{metrics['material_groups_at_risk']}%</div>
        <div class="metric-label">At-Risk Categories</div>
        <div class="metric-change metric-positive">↘ {metrics['risk_mom_change']}% MoM</div>
    </div>
    """, unsafe_allow_html=True)

with col4:
    st.markdown(f"""
    <div class="metric-card">
        <div class="metric-number">{metrics['active_suppliers']:,}</div>
        <div class="metric-label">Active Suppliers</div>
        <div class="metric-change metric-neutral">+15 new</div>
    </div>
    """, unsafe_allow_html=True)

# Clean data table
st.markdown('<div class="section-header">Material Group Performance</div>', unsafe_allow_html=True)

# Display clean table
st.dataframe(material_df, use_container_width=True, hide_index=True)

# Professional charts
st.markdown('<div class="section-header">Performance Analytics</div>', unsafe_allow_html=True)

col1, col2 = st.columns(2)

with col1:
    # Clean bar chart
    fig1 = px.bar(
        material_df, 
        x='Material Group', 
        y=['Current Rate', 'Target Rate'],
        title="Fulfillment Rates: Current vs Target",
        color_discrete_sequence=['#3b82f6', '#64748b']
    )
    
    fig1.update_layout(
        plot_bgcolor='white',
        paper_bgcolor='white',
        font_family="Inter",
        title_font_size=14,
        title_font_color="#1e293b",
        xaxis_tickangle=-45,
        height=400,
        showlegend=True,
        legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1)
    )
    
    st.plotly_chart(fig1, use_container_width=True)

with col2:
    # Clean pie chart
    risk_counts = material_df['Risk Level'].value_counts()
    
    fig2 = px.pie(
        values=risk_counts.values,
        names=risk_counts.index,
        title="Risk Distribution",
        color_discrete_sequence=['#22c55e', '#f59e0b', '#ef4444']
    )
    
    fig2.update_layout(
        plot_bgcolor='white',
        paper_bgcolor='white',
        font_family="Inter",
        title_font_size=14,
        title_font_color="#1e293b",
        height=400
    )
    
    st.plotly_chart(fig2, use_container_width=True)

# Clean footer
st.markdown("""
<div style="text-align: center; padding: 1.5rem; color: #64748b; border-top: 1px solid #e2e8f0; margin-top: 2rem; font-size: 0.875rem;">
    Dashboard last updated: {timestamp} • Auto-refresh: Every 15 minutes • Data accuracy: 99.7%
</div>
""".format(timestamp=datetime.now().strftime("%B %d, %Y at %I:%M %p")), unsafe_allow_html=True)