Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,441 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 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")
+    })
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+if __name__ == "__main__":
+    main()