Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,164 +1,104 @@
-# app.py (Updated sections)
 import streamlit as st
 import pandas as pd
+import numpy as np
 import plotly.express as px
 import plotly.graph_objects as go
+from plotly.subplots import make_subplots
 from streamlit_option_menu import option_menu
-from data_generator import generate_synthetic_procurement_data
-from ai_agents import LLMPoweredProcurementAgent, IntelligentRiskAgent
 import time
+from faker import Faker
+from datetime import datetime, timedelta
+import random
+import openai
+import json
+from typing import Dict, List, Any
 
-# Add API key configuration
-if 'OPENAI_API_KEY' not in st.secrets:
-    st.error("⚠️ OpenAI API key not found in secrets. Please add your OpenAI API key to continue.")
-    st.stop()
+# Page configuration
+st.set_page_config(
+    page_title="SAP S/4HANA Agentic AI Procurement Analytics",
+    page_icon="🤖",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
 
-# Initialize LLM-powered agents
-@st.cache_resource
-def initialize_llm_agents():
-    analytics_agent = LLMPoweredProcurementAgent(st.session_state.po_df, st.session_state.spend_df)
-    risk_agent = IntelligentRiskAgent(st.session_state.spend_df)
-    return analytics_agent, risk_agent
-
-analytics_agent, risk_agent = initialize_llm_agents()
-
-# Add new navigation option for AI Chat
-with st.sidebar:
-    selected = option_menu(
-        "Navigation",
-        ["🏠 Dashboard", "🤖 AI Insights", "💬 AI Chat", "📊 Analytics", "⚠️ Risk Management", "🎯 Recommendations"],
-        icons=['house', 'robot', 'chat', 'graph-up', 'exclamation-triangle', 'target'],
-        menu_icon="cast",
-        default_index=0,
-    )
-
-# Updated Dashboard with LLM-generated insights
-if selected == "🏠 Dashboard":
-    # ... existing dashboard code ...
-    
-    # Add LLM-generated executive summary
-    st.markdown("### 🧠 AI Executive Summary")
-    
-    with st.spinner('🤖 AI is analyzing your procurement data...'):
-        executive_summary = analytics_agent.generate_executive_summary()
-    
-    st.markdown(f"""
-    <div class="ai-insight">
-        <h4>📊 Intelligent Analysis</h4>
-        <p>{executive_summary}</p>
-    </div>
-    """, unsafe_allow_html=True)
-
-# New AI Chat Interface
-elif selected == "💬 AI Chat":
-    st.markdown("### 💬 Chat with Your Procurement Data")
-    
-    st.markdown("""
-    <div class="ai-insight">
-        <h4>🤖 Intelligent Procurement Assistant</h4>
-        <p>Ask me anything about your procurement data! I can help you understand trends, analyze vendor performance, identify opportunities, and more.</p>
-    </div>
-    """, unsafe_allow_html=True)
-    
-    # Chat interface
-    if "messages" not in st.session_state:
-        st.session_state.messages = [
-            {"role": "assistant", "content": "Hello! I'm your AI procurement analyst. What would you like to know about your procurement data?"}
-        ]
-    
-    # Display chat messages
-    for message in st.session_state.messages:
-        with st.chat_message(message["role"]):
-            st.markdown(message["content"])
-    
-    # Chat input
-    if prompt := st.chat_input("Ask about your procurement data..."):
-        # Add user message to chat history
-        st.session_state.messages.append({"role": "user", "content": prompt})
-        with st.chat_message("user"):
-            st.markdown(prompt)
-        
-        # Generate AI response
-        with st.chat_message("assistant"):
-            with st.spinner("🤖 Analyzing your question..."):
-                response = analytics_agent.chat_with_data(prompt)
-            st.markdown(response)
-        
-        # Add assistant response to chat history
-        st.session_state.messages.append({"role": "assistant", "content": response})
-    
-    # Suggested questions
-    st.markdown("#### 💡 Try asking:")
-    
-    col1, col2, col3 = st.columns(3)
-    
-    with col1:
-        if st.button("📊 What are my spending trends?"):
-            st.rerun()
-    
-    with col2:
-        if st.button("🏆 Who are my best vendors?"):
-            st.rerun()
-    
-    with col3:
-        if st.button("⚠️ What risks should I worry about?"):
-            st.rerun()
-
-# Enhanced AI Insights with LLM
-elif selected == "🤖 AI Insights":
-    st.markdown("### 🧠 Advanced AI Analysis")
-    
-    # Vendor deep-dive analysis
-    st.markdown("#### 🔍 Vendor Performance Deep Dive")
-    
-    selected_vendor = st.selectbox(
-        "Select vendor for AI analysis:",
-        options=st.session_state.po_df['vendor'].unique()
-    )
-    
-    if st.button("🚀 Generate AI Analysis", type="primary"):
-        with st.spinner(f'🤖 AI is performing deep analysis of {selected_vendor}...'):
-            vendor_analysis = analytics_agent.analyze_vendor_performance(selected_vendor)
-        
-        st.markdown(f"""
-        <div class="ai-insight">
-            <h4>🎯 AI Analysis: {selected_vendor}</h4>
-            <div style="white-space: pre-line;">{vendor_analysis}</div>
-        </div>
-        """, unsafe_allow_html=True)
-    
-    # Anomaly insights with LLM
-    st.markdown("#### 🚨 Intelligent Anomaly Analysis")
-    
-    # Get anomalies from your existing detection logic
-    anomalies = []  # Your existing anomaly detection code here
-    
-    with st.spinner('🔍 AI is analyzing anomalies...'):
-        anomaly_insights = analytics_agent.generate_anomaly_insights(anomalies)
-    
-    st.markdown(f"""
-    <div class="alert alert-warning">
-        <h4>🤖 AI Anomaly Assessment</h4>
-        <div style="white-space: pre-line;">{anomaly_insights}</div>
-    </div>
-    """, unsafe_allow_html=True)
-
-# Enhanced Risk Management with LLM
-elif selected == "⚠️ Risk Management":
-    st.markdown("### 🛡️ AI-Powered Risk Intelligence")
-    
-    # ... existing risk metrics ...
-    
-    # LLM-generated risk report
-    st.markdown("#### 📋 Comprehensive Risk Assessment")
-    
-    if st.button("🚀 Generate AI Risk Report", type="primary"):
-        with st.spinner('🤖 AI is generating comprehensive risk assessment...'):
-            risk_report = risk_agent.generate_risk_report()
-        
-        st.markdown(f"""
-        <div class="ai-insight">
-            <h4>🛡️ Strategic Risk Analysis</h4>
-            <div style="white-space: pre-line;">{risk_report}</div>
-        </div>
-        """, unsafe_allow_html=True)
+# Custom CSS (same as before)
+st.markdown("""
+<style>
+    /* Main theme colors */
+    :root {
+        --primary-color: #0066cc;
+        --secondary-color: #f0f8ff;
+        --accent-color: #ff6b35;
+        --success-color: #28a745;
+        --warning-color: #ffc107;
+        --danger-color: #dc3545;
+    }
+    
+    /* Hide Streamlit branding */
+    #MainMenu {visibility: hidden;}
+    footer {visibility: hidden;}
+    header {visibility: hidden;}
+    
+    /* Custom header styling */
+    .main-header {
+        background: linear-gradient(90deg, #0066cc, #004c99);
+        padding: 1rem;
+        border-radius: 10px;
+        margin-bottom: 2rem;
+        color: white;
+        text-align: center;
+    }
+    
+    /* Metric cards styling */
+    .metric-card {
+        background: white;
+        padding: 1.5rem;
+        border-radius: 10px;
+        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+        border-left: 4px solid var(--primary-color);
+        margin-bottom: 1rem;
+    }
+    
+    /* AI insights styling */
+    .ai-insight {
+        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+        color: white;
+        padding: 1rem;
+        border-radius: 10px;
+        margin: 1rem 0;
+    }
+    
+    /* Alert styling */
+    .alert {
+        padding: 1rem;
+        border-radius: 8px;
+        margin: 1rem 0;
+        border-left: 4px solid;
+    }
+    
+    .alert-success {
+        background-color: #d4edda;
+        border-color: var(--success-color);
+        color: #155724;
+    }
+    
+    .alert-warning {
+        background-color: #fff3cd;
+        border-color: var(--warning-color);
+        color: #856404;
+    }
+    
+    .alert-danger {
+        background-color: #f8d7da;
+        border-color: var(--danger-color);
+        color: #721c24;
+    }
+    
+    /* Button styling */
+    .stButton > button {
+        background: linear-gradient(90deg, #0066cc, #004c99);
+        color: white;
+        border: none;
+        border-radius: 8px;
+        padding: 0.5rem 1rem;
+        font