Update src/streamlit_app.py

src/streamlit_app.py

@@ -3,15 +3,14 @@ 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
 import time
 from faker import Faker
 from datetime import datetime, timedelta
 import random
-import openai
 import json
 from typing import Dict, List, Any
+import os
 
 # Page configuration
 st.set_page_config(
@@ -21,7 +20,7 @@ st.set_page_config(
     initial_sidebar_state="expanded"
 )
 
-# Custom CSS (same as before)
+# Custom CSS
 st.markdown("""
 <style>
     /* Main theme colors */
@@ -88,10 +87,10 @@ st.markdown("""
         color: #856404;
     }
     
-    .alert-danger {
-        background-color: #f8d7da;
-        border-color: var(--danger-color);
-        color: #721c24;
+    .alert-info {
+        background-color: #d1ecf1;
+        border-color: #17a2b8;
+        color: #0c5460;
     }
     
     /* Button styling */
@@ -112,11 +111,35 @@ st.markdown("""
 </style>
 """, unsafe_allow_html=True)
 
-# Data generation function (embedded in main file)
+# Function to safely get OpenAI API key
+def get_openai_api_key():
+    """Safely retrieve OpenAI API key from various sources"""
+    api_key = None
+    
+    # Method 1: Try from Streamlit secrets
+    try:
+        if hasattr(st, 'secrets') and 'OPENAI_API_KEY' in st.secrets:
+            api_key = st.secrets["OPENAI_API_KEY"]
+    except Exception:
+        pass
+    
+    # Method 2: Try from environment variables
+    if not api_key:
+        api_key = os.getenv('OPENAI_API_KEY')
+    
+    # Method 3: Try from Hugging Face Spaces environment
+    if not api_key:
+        api_key = os.getenv('OPENAI_API_TOKEN')  # Sometimes HF uses this
+    
+    return api_key
+
+# Data generation function
 @st.cache_data
 def generate_synthetic_procurement_data():
     """Generate synthetic SAP S/4HANA procurement data"""
     fake = Faker()
+    np.random.seed(42)  # For reproducible data
+    random.seed(42)
     
     # Vendors data
     vendors = [
@@ -171,47 +194,64 @@ def generate_synthetic_procurement_data():
     
     return pd.DataFrame(purchase_orders), pd.DataFrame(spend_data)
 
-# AI Agent Classes (embedded in main file)
+# AI Agent Classes
 class LLMPoweredProcurementAgent:
     """AI Agent powered by OpenAI GPT for intelligent procurement analysis"""
     
     def __init__(self, po_data: pd.DataFrame, spend_data: pd.DataFrame):
         self.po_data = po_data
         self.spend_data = spend_data
-        # Check if OpenAI API key is available
-        if 'OPENAI_API_KEY' in st.secrets:
-            self.client = openai.OpenAI(api_key=st.secrets["OPENAI_API_KEY"])
-            self.llm_available = True
+        
+        # Safely get OpenAI API key
+        self.api_key = get_openai_api_key()
+        self.llm_available = bool(self.api_key)
+        
+        if self.llm_available:
+            try:
+                import openai
+                self.client = openai.OpenAI(api_key=self.api_key)
+            except ImportError:
+                self.llm_available = False
+                self.client = None
         else:
             self.client = None
-            self.llm_available = False
     
     def generate_executive_summary(self) -> str:
-        """Generate an executive summary using GPT"""
+        """Generate an executive summary using GPT or fallback"""
         
         if not self.llm_available:
-            # Fallback to rule-based summary if no API key
+            # Enhanced rule-based summary if no API key
             total_spend = self.po_data['order_value'].sum()
             total_orders = len(self.po_data)
             on_time_rate = self.po_data['on_time_delivery'].mean() * 100
+            quality_avg = self.po_data['quality_score'].mean()
+            top_category = self.po_data.groupby('material_category')['order_value'].sum().idxmax()
+            top_vendor = self.po_data.groupby('vendor')['order_value'].sum().idxmax()
             
-            return f"""
-            **Executive Summary - Procurement Performance**
-            
-            📊 **Key Metrics**: Processed {total_orders:,} orders worth €{total_spend:,.0f} across {len(self.po_data['vendor'].unique())} vendors.
-            
-            🎯 **Performance Highlights**: 
-            • On-time delivery rate: {on_time_rate:.1f}%
-            • Average quality score: {self.po_data['quality_score'].mean():.1f}/10
-            • Top spending category: {self.po_data.groupby('material_category')['order_value'].sum().idxmax()}
-            
-            💡 **Strategic Recommendations**:
-            • Focus on vendor consolidation opportunities
-            • Improve contract compliance monitoring
-            • Implement performance-based partnerships
-            
-            *Note: Connect OpenAI API for enhanced AI insights*
-            """
+            return f"""**🎯 Executive Summary - Procurement Performance Dashboard**
+
+📊 **Current Portfolio Overview**
+• Total procurement spend: €{total_spend:,.0f} across {total_orders:,} purchase orders
+• Active vendor network: {len(self.po_data['vendor'].unique())} strategic suppliers
+• Average order value: €{self.po_data['order_value'].mean():,.0f}
+
+🏆 **Performance Highlights**
+• On-time delivery performance: {on_time_rate:.1f}% (Industry benchmark: 85%)
+• Average supplier quality score: {quality_avg:.1f}/10
+• Leading spend category: {top_category}
+• Top strategic partner: {top_vendor}
+
+⚡ **Strategic Opportunities**
+• Vendor consolidation potential identified in {len(self.po_data['vendor'].unique())} supplier base
+• Contract optimization opportunities with top-tier vendors
+• Digital procurement automation possibilities for routine purchases
+
+💡 **AI-Powered Recommendations**
+• Implement strategic sourcing for {top_category} category
+• Develop performance-based contracts with high-performing suppliers
+• Establish automated approval workflows for orders under €10,000
+
+*🔧 Note: Connect OpenAI API for advanced AI insights and natural language analysis*"""
         
         # Prepare data summary for LLM
         data_summary = {
@@ -226,55 +266,111 @@ class LLMPoweredProcurementAgent:
         }
         
         prompt = f"""
-        As a senior procurement analyst, provide an executive summary of the procurement performance based on this data:
+        As a senior procurement analyst with expertise in SAP S/4HANA systems, provide an executive summary of procurement performance:
         
-        {json.dumps(data_summary, indent=2)}
+        Data: {json.dumps(data_summary, indent=2)}
         
-        Please provide:
-        1. A concise executive overview (2-3 sentences)
-        2. Key performance highlights
-        3. Areas of concern (if any)
-        4. Strategic recommendations (2-3 bullet points)
+        Provide:
+        1. Executive overview (2-3 sentences)
+        2. Key performance highlights with specific metrics
+        3. Critical areas needing attention
+        4. Strategic recommendations (3-4 actionable items)
         
-        Keep the tone professional and actionable. Focus on business impact.
+        Keep it professional, metrics-focused, and actionable for C-level executives.
         """
         
         try:
             response = self.client.chat.completions.create(
                 model="gpt-4",
                 messages=[
-                    {"role": "system", "content": "You are an expert procurement analyst with 15+ years of experience in SAP S/4HANA systems."},
+                    {"role": "system", "content": "You are a senior procurement analyst with 15+ years of SAP S/4HANA experience."},
                     {"role": "user", "content": prompt}
                 ],
-                max_tokens=500,
+                max_tokens=600,
                 temperature=0.7
             )
             return response.choices[0].message.content
         except Exception as e:
-            return f"AI Analysis temporarily unavailable: {str(e)}"
+            return f"🤖 AI Analysis temporarily unavailable. Using rule-based insights instead.\n\n{self.generate_executive_summary()}"
     
     def chat_with_data(self, user_question: str) -> str:
         """Natural language interface to query procurement data"""
         
         if not self.llm_available:
-            # Simple rule-based responses
+            # Enhanced rule-based responses
             question_lower = user_question.lower()
             
-            if "spend" in question_lower or "cost" in question_lower:
+            if any(word in question_lower for word in ["spend", "cost", "money", "budget"]):
                 total_spend = self.po_data['order_value'].sum()
-                return f"Your total procurement spend is €{total_spend:,.0f}. The top spending category is {self.po_data.groupby('material_category')['order_value'].sum().idxmax()}. Connect OpenAI API for detailed analysis."
+                top_category = self.po_data.groupby('material_category')['order_value'].sum().idxmax()
+                monthly_avg = total_spend / 24  # Assuming 2 years of data
+                return f"""💰 **Spend Analysis:**
+
+• **Total procurement spend**: €{total_spend:,.0f}
+• **Monthly average**: €{monthly_avg:,.0f}
+• **Largest spend category**: {top_category} 
+• **Average order size**: €{self.po_data['order_value'].mean():,.0f}
+
+The spending is distributed across {len(self.po_data['material_category'].unique())} categories with {top_category} representing the highest investment area.
+
+*💡 Connect OpenAI API for detailed spend optimization strategies!*"""
             
-            elif "vendor" in question_lower or "supplier" in question_lower:
+            elif any(word in question_lower for word in ["vendor", "supplier", "partner"]):
                 top_vendor = self.po_data.groupby('vendor')['order_value'].sum().idxmax()
-                return f"Your top vendor by spend is {top_vendor}. You work with {len(self.po_data['vendor'].unique())} vendors total. Connect OpenAI API for detailed vendor analysis."
+                vendor_count = len(self.po_data['vendor'].unique())
+                top_vendor_performance = self.po_data[self.po_data['vendor'] == top_vendor]['on_time_delivery'].mean() * 100
+                return f"""🤝 **Vendor Analysis:**
+
+• **Total active vendors**: {vendor_count}
+• **Top strategic partner**: {top_vendor}
+• **{top_vendor} performance**: {top_vendor_performance:.1f}% on-time delivery
+• **Vendor diversity**: Well-distributed across multiple suppliers
+
+Your vendor portfolio shows good diversification with {top_vendor} as the leading partner.
+
+*💡 Connect OpenAI API for detailed vendor relationship strategies!*"""
+            
+            elif any(word in question_lower for word in ["risk", "compliance", "quality"]):
+                avg_quality = self.po_data['quality_score'].mean()
+                on_time_rate = self.po_data['on_time_delivery'].mean() * 100
+                return f"""⚠️ **Risk & Quality Analysis:**
+
+• **Average quality score**: {avg_quality:.1f}/10 
+• **On-time delivery rate**: {on_time_rate:.1f}%
+• **Performance status**: {'Excellent' if avg_quality > 8.5 else 'Good' if avg_quality > 7.5 else 'Needs Improvement'}
+
+Overall risk profile appears {'low' if on_time_rate > 85 else 'moderate'} based on delivery performance metrics.
+
+*💡 Connect OpenAI API for comprehensive risk assessment!*"""
             
-            elif "risk" in question_lower:
-                return "Risk analysis shows mixed performance across vendors. Connect OpenAI API for comprehensive risk assessment."
+            elif any(word in question_lower for word in ["trend", "pattern", "analysis"]):
+                return f"""📈 **Trend Analysis:**
+
+• **Data period**: {self.po_data['order_date'].min()} to {self.po_data['order_date'].max()}
+• **Total orders processed**: {len(self.po_data):,}
+• **Peak category**: {self.po_data.groupby('material_category')['order_value'].sum().idxmax()}
+• **Seasonal patterns**: Data shows consistent procurement activity
+
+Historical data indicates stable procurement operations with opportunities for optimization.
+
+*💡 Connect OpenAI API for advanced trend forecasting!*"""
             
             else:
-                return "I can help with procurement analysis! Try asking about spending, vendors, or risks. Connect OpenAI API for advanced AI conversation."
+                return f"""🤖 **Procurement Assistant Ready!**
+
+I can help you analyze:
+• 💰 **Spending patterns** and budget optimization
+• 🤝 **Vendor performance** and relationship management  
+• ⚠️ **Risk assessment** and quality metrics
+• 📈 **Trends and forecasting** for strategic planning
+
+**Current data scope**: {len(self.po_data):,} orders across {len(self.po_data['vendor'].unique())} vendors
+
+Try asking: "What are my biggest spending areas?" or "How are my vendors performing?"
+
+*💡 Connect OpenAI API for natural language conversations and advanced insights!*"""
         
-        # Create a data context for the LLM
+        # LLM-powered response
         data_context = {
             "procurement_summary": {
                 "total_spend": float(self.po_data['order_value'].sum()),
@@ -297,24 +393,24 @@ class LLMPoweredProcurementAgent:
         Procurement Data Context:
         {json.dumps(data_context, indent=2)}
         
-        Please answer the user's question based on the procurement data provided. If you need specific calculations or data analysis that isn't available in the context, explain what additional analysis would be needed.
-        
-        Keep your response conversational but professional, and always relate back to business impact where possible.
+        Answer the user's question based on the procurement data. Be conversational yet professional. 
+        Include specific metrics when relevant and relate findings to business impact.
+        If you need additional data not available in the context, suggest what analysis would be helpful.
         """
         
         try:
             response = self.client.chat.completions.create(
                 model="gpt-4",
                 messages=[
-                    {"role": "system", "content": "You are an AI procurement analyst assistant. Answer questions about procurement data in a helpful, conversational way while maintaining professional expertise."},
+                    {"role": "system", "content": "You are an expert procurement analyst assistant. Provide helpful, professional responses about procurement data and strategy."},
                     {"role": "user", "content": prompt}
                 ],
-                max_tokens=400,
+                max_tokens=500,
                 temperature=0.7
             )
             return response.choices[0].message.content
         except Exception as e:
-            return f"I'm having trouble accessing the AI right now. Please try again later. Error: {str(e)}"
+            return f"I'm having trouble accessing advanced AI right now. Here's what I can tell you based on the data:\n\n{self.chat_with_data(user_question)}"
 
     def analyze_spend_patterns(self) -> Dict[str, Any]:
         """Analyze spending patterns and generate insights"""
@@ -340,7 +436,7 @@ class LLMPoweredProcurementAgent:
 
 # Initialize session state and data
 if 'data_loaded' not in st.session_state:
-    with st.spinner('🔄 Generating synthetic SAP S/4HANA data...'):
+    with st.spinner('🔄 Generating synthetic SAP S/4HANA procurement data...'):
         st.session_state.po_df, st.session_state.spend_df = generate_synthetic_procurement_data()
         st.session_state.data_loaded = True
 
@@ -353,14 +449,15 @@ def initialize_agents():
 analytics_agent = initialize_agents()
 
 # API Key status check
-api_key_status = "🟢 Connected" if 'OPENAI_API_KEY' in st.secrets else "🔴 Not Connected"
+api_key = get_openai_api_key()
+api_key_status = "🟢 Connected" if api_key else "🔴 Not Connected"
 
 # Main header
 st.markdown(f"""
 <div class="main-header">
     <h1>🤖 SAP S/4HANA Agentic AI Procurement Analytics</h1>
     <p>Autonomous Intelligence for Procurement Excellence</p>
-    <small>OpenAI Status: {api_key_status}</small>
+    <small>OpenAI Status: {api_key_status} | Data: {len(st.session_state.po_df):,} Purchase Orders</small>
 </div>
 """, unsafe_allow_html=True)
 
@@ -369,8 +466,15 @@ with st.sidebar:
     st.markdown("### 🤖 AI-Powered Analytics")
     st.markdown(f"**OpenAI Status:** {api_key_status}")
     
-    if 'OPENAI_API_KEY' not in st.secrets:
-        st.warning("⚠️ Add OpenAI API key in Spaces settings for full AI features")
+    if not api_key:
+        st.markdown("""
+        <div class="alert alert-info">
+            <small><strong>💡 Enhanced AI Features</strong><br>
+            Add OpenAI API key as OPENAI_API_KEY in your Hugging Face Space settings for advanced AI conversations and insights!</small>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    st.markdown("---")
     
     selected = option_menu(
         "Navigation",
@@ -385,18 +489,24 @@ with st.sidebar:
             "nav-link-selected": {"background-color": "#0066cc"},
         }
     )
+    
+    st.markdown("---")
+    st.markdown("### 📊 Quick Stats")
+    st.metric("Total Orders", f"{len(st.session_state.po_df):,}")
+    st.metric("Active Vendors", f"{len(st.session_state.po_df['vendor'].unique())}")
+    st.metric("Categories", f"{len(st.session_state.po_df['material_category'].unique())}")
 
 if selected == "🏠 Dashboard":
     # AI-generated insights at the top
     st.markdown("### 🧠 AI Executive Summary")
     
-    with st.spinner('🤖 AI is analyzing your procurement data...'):
+    with st.spinner('🤖 AI analyzing procurement data...'):
         executive_summary = analytics_agent.generate_executive_summary()
     
     st.markdown(f"""
     <div class="ai-insight">
         <h4>📊 Intelligent Analysis</h4>
-        <div style="white-space: pre-line;">{executive_summary}</div>
+        <div style="white-space: pre-line; line-height: 1.6;">{executive_summary}</div>
     </div>
     """, unsafe_allow_html=True)
     
@@ -410,7 +520,7 @@ if selected == "🏠 Dashboard":
         <div class="metric-card">
             <h3 style="color: var(--primary-color); margin: 0;">Total Spend</h3>
             <h2 style="margin: 0.5rem 0;">€{:,.0f}</h2>
-            <p style="color: #28a745; margin: 0;">📈 +12% vs last period</p>
+            <p style="color: #28a745; margin: 0;">📈 Active Portfolio</p>
         </div>
         """.format(insights['total_spend']), unsafe_allow_html=True)
     
@@ -419,7 +529,7 @@ if selected == "🏠 Dashboard":
         <div class="metric-card">
             <h3 style="color: var(--primary-color); margin: 0;">Avg Order Value</h3>
             <h2 style="margin: 0.5rem 0;">€{:,.0f}</h2>
-            <p style="color: #ffc107; margin: 0;">📊 Stable trend</p>
+            <p style="color: #17a2b8; margin: 0;">📊 Order Efficiency</p>
         </div>
         """.format(insights['avg_order_value']), unsafe_allow_html=True)
     
@@ -429,7 +539,7 @@ if selected == "🏠 Dashboard":
         <div class="metric-card">
             <h3 style="color: var(--primary-color); margin: 0;">Active Vendors</h3>
             <h2 style="margin: 0.5rem 0;">{}</h2>
-            <p style="color: #17a2b8; margin: 0;">🤝 Partnership focus</p>
+            <p style="color: #6f42c1; margin: 0;">🤝 Strategic Partners</p>
         </div>
         """.format(active_vendors), unsafe_allow_html=True)
     
@@ -439,7 +549,7 @@ if selected == "🏠 Dashboard":
         <div class="metric-card">
             <h3 style="color: var(--primary-color); margin: 0;">On-Time Delivery</h3>
             <h2 style="margin: 0.5rem 0;">{:.1f}%</h2>
-            <p style="color: #28a745; margin: 0;">⏰ Excellent performance</p>
+            <p style="color: #28a745; margin: 0;">⏰ Performance</p>
         </div>
         """.format(on_time_delivery), unsafe_allow_html=True)
     
@@ -493,7 +603,7 @@ elif selected == "💬 AI Chat":
     st.markdown(f"""
     <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>
+        <p>Ask me anything about your procurement data! I can analyze trends, vendor performance, spending patterns, and provide strategic recommendations.</p>
         <p><small>Status: {api_key_status}</small></p>
     </div>
     """, unsafe_allow_html=True)
@@ -501,7 +611,7 @@ elif selected == "💬 AI Chat":
     # 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?"}
+            {"role": "assistant", "content": "Hello! I'm your AI procurement analyst. I've analyzed your procurement portfolio and I'm ready to help! What would you like to explore?"}
         ]
     
     # Display chat messages
@@ -526,85 +636,205 @@ elif selected == "💬 AI Chat":
         st.session_state.messages.append({"role": "assistant", "content": response})
     
     # Suggested questions
-    st.markdown("#### 💡 Try asking:")
+    st.markdown("#### 💡 Try these sample questions:")
     
     col1, col2, col3 = st.columns(3)
     
     sample_questions = [
-        "📊 What are my spending trends?",
-        "🏆 Who are my best vendors?",
-        "⚠️ What risks should I worry about?"
+        "What are my biggest spending areas?",
+        "How are my vendors performing?",
+        "What risks should I be concerned about?"
     ]
     
     for i, (col, question) in enumerate(zip([col1, col2, col3], sample_questions)):
         with col:
-            if st.button(question, key=f"q_{i}"):
+            if st.button(f"💭 {question}", key=f"q_{i}"):
                 # Add the question to chat
                 st.session_state.messages.append({"role": "user", "content": question})
                 with st.spinner("🤖 Analyzing..."):
                     response = analytics_agent.chat_with_data(question)
                 st.session_state.messages.append({"role": "assistant", "content": response})
                 st.rerun()
+    
+    # Clear chat button
+    if st.button("🗑️ Clear Chat History"):
+        st.session_state.messages = [
+            {"role": "assistant", "content": "Chat cleared! What would you like to know about your procurement data?"}
+        ]
+        st.rerun()
 
 elif selected == "📊 Analytics":
     st.markdown("### 📈 Advanced Analytics Dashboard")
     
     # Vendor performance analysis
+    st.markdown("#### 🏆 Vendor Performance Scorecard")
+    
     vendor_performance = st.session_state.po_df.groupby('vendor').agg({
         'order_value': 'sum',
         'on_time_delivery': 'mean',
         'quality_score': 'mean',
         'po_number': 'count'
     }).round(2)
-    vendor_performance.columns = ['Total Spend', 'On-Time Delivery', 'Quality Score', 'Order Count']
-    
-    st.markdown("#### 🏆 Vendor Performance Analysis")
-    st.dataframe(vendor_performance.sort_values('Total Spend', ascending=False), use_container_width=True)
-    
-    # Performance scatter plot
-    fig_scatter = px.scatter(
-        st.session_state.po_df,
-        x='on_time_delivery',
-        y='quality_score',
-        size='order_value',
-        color='vendor',
-        title='Vendor Performance: Quality vs Delivery',
-        labels={'on_time_delivery': 'On-Time Delivery Rate', 'quality_score': 'Quality Score'}
+    vendor_performance.columns = ['Total Spend (€)', 'On-Time Delivery (%)', 'Quality Score', 'Order Count']
+    vendor_performance['On-Time Delivery (%)'] = (vendor_performance['On-Time Delivery (%)'] * 100).round(1)
+    vendor_performance = vendor_performance.sort_values('Total Spend (€)', ascending=False)
+    
+    st.dataframe(
+        vendor_performance.head(10), 
+        use_container_width=True,
+        column_config={
+            "Total Spend (€)": st.column_config.NumberColumn(
+                "Total Spend (€)",
+                help="Total procurement spend with vendor",
+                format="€%.0f",
+            ),
+            "On-Time Delivery (%)": st.column_config.NumberColumn(
+                "On-Time Delivery (%)",
+                help="Percentage of on-time deliveries",
+                format="%.1f%%",
+            ),
+            "Quality Score": st.column_config.NumberColumn(
+                "Quality Score",
+                help="Average quality rating (1-10)",
+                format="%.1f/10",
+            )
+        }
     )
-    fig_scatter.update_layout(height=500)
-    st.plotly_chart(fig_scatter, use_container_width=True)
+    
+    # Performance charts
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        # Performance scatter plot
+        fig_scatter = px.scatter(
+            st.session_state.po_df,
+            x='on_time_delivery',
+            y='quality_score',
+            size='order_value',
+            color='vendor',
+            title='Vendor Performance Matrix',
+            labels={'on_time_delivery': 'On-Time Delivery Rate', 'quality_score': 'Quality Score (1-10)'},
+            hover_data=['vendor', 'order_value']
+        )
+        fig_scatter.update_layout(height=500, showlegend=False)
+        st.plotly_chart(fig_scatter, use_container_width=True)
+    
+    with col2:
+        # Monthly trend
+        st.session_state.po_df['order_month'] = pd.to_datetime(st.session_state.po_df['order_date']).dt.to_period('M')
+        monthly_trend = st.session_state.po_df.groupby('order_month')['order_value'].sum().reset_index()
+        monthly_trend['order_month'] = monthly_trend['order_month'].astype(str)
+        
+        fig_trend = px.line(
+            monthly_trend,
+            x='order_month',
+            y='order_value',
+            title='Monthly Procurement Spend Trend',
+            markers=True
+        )
+        fig_trend.update_layout(
+            height=500,
+            xaxis_tickangle=45
+        )
+        fig_trend.update_traces(line_color='#0066cc', line_width=3, marker_size=8)
+        st.plotly_chart(fig_trend, use_container_width=True)
 
 elif selected == "🎯 Recommendations":
-    st.markdown("### 🚀 AI-Generated Strategic Recommendations")
+    st.markdown("### 🚀 Strategic Procurement Recommendations")
     
     st.markdown("""
     <div class="ai-insight">
-        <h3>🎯 Strategic Procurement Optimization</h3>
-        <p>Based on data analysis, here are actionable recommendations to optimize your procurement strategy.</p>
+        <h3>🎯 AI-Powered Strategic Optimization</h3>
+        <p>Based on comprehensive data analysis, here are prioritized recommendations to enhance your procurement strategy and drive business value.</p>
     </div>
     """, unsafe_allow_html=True)
     
+    # Calculate some metrics for recommendations
+    vendor_count = len(st.session_state.po_df['vendor'].unique())
+    avg_order_value = st.session_state.po_df['order_value'].mean()
+    low_value_orders = len(st.session_state.po_df[st.session_state.po_df['order_value'] < 5000])
+    total_orders = len(st.session_state.po_df)
+    
     recommendations = [
-        "🎯 **Vendor Consolidation**: Consider consolidating to top 5-7 vendors to reduce costs by 10-15%",
-        "📈 **Contract Optimization**: Renegotiate contracts with top 3 spending vendors for volume discounts",
-        "⚡ **Process Automation**: Implement automated PO approval for orders under €5,000",
-        "📊 **Performance Monitoring**: Set up real-time dashboards for vendor performance tracking",
-        "🤝 **Strategic Partnerships**: Develop long-term partnerships with high-performing vendors"
+        {
+            "priority": "🔥 High Priority",
+            "title": "Vendor Consolidation Strategy",
+            "description": f"With {vendor_count} active vendors, consolidating to 5-7 strategic partners could reduce costs by 12-18% and improve relationship management.",
+            "impact": "💰 Cost Reduction: €50K-75K annually",
+            "timeline": "3-6 months"
+        },
+        {
+            "priority": "⚡ Quick Win", 
+            "title": "Procurement Process Automation",
+            "description": f"{low_value_orders}/{total_orders} orders ({low_value_orders/total_orders*100:.1f}%) are under €5,000. Implementing automated approval workflows could save 40+ hours weekly.",
+            "impact": "⏱️ Efficiency Gain: 160 hours/month",
+            "timeline": "4-8 weeks"
+        },
+        {
+            "priority": "📈 Strategic",
+            "title": "Performance-Based Contracts",
+            "description": "Implement KPI-driven contracts with top 5 vendors focusing on quality scores >8.5 and delivery performance >90%.",
+            "impact": "📊 Performance Improvement: 15-25%",
+            "timeline": "6-9 months"
+        },
+        {
+            "priority": "🛡️ Risk Management",
+            "title": "Supplier Risk Monitoring",
+            "description": "Deploy real-time risk assessment tools to monitor supplier financial health, compliance, and performance metrics.",
+            "impact": "⚠️ Risk Reduction: 30-40%",
+            "timeline": "2-4 months"
+        },
+        {
+            "priority": "🎯 Innovation",
+            "title": "Digital Procurement Platform",
+            "description": "Upgrade to AI-powered procurement platform with predictive analytics, spend optimization, and automated sourcing capabilities.",
+            "impact": "🚀 Digital Transformation: 25-35% efficiency",
+            "timeline": "9-12 months"
+        }
     ]
     
     for i, rec in enumerate(recommendations, 1):
+        priority_color = {"🔥 High Priority": "#dc3545", "⚡ Quick Win": "#28a745", "📈 Strategic": "#0066cc", "🛡️ Risk Management": "#ffc107", "🎯 Innovation": "#6f42c1"}
+        
         st.markdown(f"""
         <div class="alert alert-success">
-            <h4>Recommendation #{i}</h4>
-            <p>{rec}</p>
+            <h4 style="color: {priority_color[rec['priority']]};">{rec['priority']}</h4>
+            <h3>{rec['title']}</h3>
+            <p style="margin-bottom: 1rem;">{rec['description']}</p>
+            <div style="display: flex; justify-content: space-between; font-size: 0.9rem;">
+                <span><strong>{rec['impact']}</strong></span>
+                <span><strong>⏱️ Timeline: {rec['timeline']}</strong></span>
+            </div>
         </div>
         """, unsafe_allow_html=True)
+    
+    # Implementation roadmap
+    st.markdown("#### 🗺️ Implementation Roadmap")
+    
+    roadmap_data = {
+        "Phase": ["Phase 1 (0-3 months)", "Phase 2 (3-6 months)", "Phase 3 (6-12 months)"],
+        "Focus Areas": [
+            "Process Automation, Quick Wins",
+            "Vendor Consolidation, Risk Management", 
+            "Strategic Contracts, Digital Platform"
+        ],
+        "Expected ROI": ["15-20%", "20-30%", "30-40%"],
+        "Key Deliverables": [
+            "Automated workflows, Spend visibility",
+            "Strategic partnerships, Risk framework",
+            "AI-powered platform, Performance management"
+        ]
+    }
+    
+    roadmap_df = pd.DataFrame(roadmap_data)
+    st.dataframe(roadmap_df, use_container_width=True, hide_index=True)
 
 # Footer
 st.markdown("---")
-st.markdown("""
+st.markdown(f"""
 <div style="text-align: center; padding: 1rem; color: #666;">
-    <p>🤖 Powered by Agentic AI | Built with Streamlit | SAP S/4HANA Integration Demo</p>
-    <p><em>This is a demonstration application with synthetic data for learning purposes</em></p>
+    <p>🤖 <strong>Agentic AI Procurement Analytics</strong> | Built with Streamlit & Python | SAP S/4HANA Integration Demo</p>
+    <p><em>Synthetic data demonstration • {len(st.session_state.po_df):,} orders • {len(st.session_state.po_df['vendor'].unique())} vendors • OpenAI {api_key_status}</em></p>
+    <p><small>💡 Add your OpenAI API key as 'OPENAI_API_KEY' in Hugging Face Space settings for enhanced AI features</small></p>
 </div>
 """, unsafe_allow_html=True)