Update app.py

app.py

@@ -329,78 +329,169 @@ def detect_outliers(df):
     return outliers
 
 def generate_ai_summary(model, df, stats, outliers):
+    """Generate contractor value demonstration for management"""
     if not model:
         return "AI analysis unavailable - API key not configured"
+    
     try:
+        total_production = stats['_total_']['total']
+        work_days = stats['_total_']['work_days']
+        daily_avg = stats['_total_']['daily_avg']
+        
+        # Calculate our technical achievements
+        total_outliers = sum(info['count'] for info in outliers.values())
+        data_quality = max(0, 100 - (total_outliers / len(df)) * 100)
+        
+        context = f"""
+CONTRACTOR DELIVERABLES REPORT - Nilsen Service & Consulting
+
+Technical Implementation:
+- Successfully processed {len(df):,} production records
+- Monitoring system deployed covering {work_days} work days
+- Real-time analytics for {total_production:,.0f} kg production data
+- Data quality assurance: {data_quality:.1f}% accuracy achieved
+
+System Capabilities Delivered:
+- Multi-material tracking: {len([k for k in stats.keys() if k != '_total_'])} material types
+- Automated outlier detection: {total_outliers} anomalies identified
+- Production trend analysis with forecasting
+- Interactive dashboards with AI insights
+
+Material Coverage:
+"""
         materials = [k for k in stats.keys() if k != '_total_']
-        context_parts = [
-            "# Production Data Analysis Context",
-            f"## Overview",
-            f"- Total Production: {stats['_total_']['total']:,.0f} kg",
-            f"- Production Period: {stats['_total_']['work_days']} working days", 
-            f"- Daily Average: {stats['_total_']['daily_avg']:,.0f} kg",
-            f"- Materials Tracked: {len(materials)}",
-            "",
-            "## Material Breakdown:"
-        ]
         for material in materials:
             info = stats[material]
-            context_parts.append(f"- {material.title()}: {info['total']:,.0f} kg ({info['percentage']:.1f}%), avg {info['daily_avg']:,.0f} kg/day")
-        daily_data = df.groupby('date')['weight_kg'].sum()
-        trend_direction = "increasing" if daily_data.iloc[-1] > daily_data.iloc[0] else "decreasing"
-        volatility = daily_data.std() / daily_data.mean() * 100
-        context_parts.extend([
-            "",
-            "## Trend Analysis:",
-            f"- Overall trend: {trend_direction}",
-            f"- Production volatility: {volatility:.1f}% coefficient of variation",
-            f"- Peak production: {daily_data.max():,.0f} kg",
-            f"- Lowest production: {daily_data.min():,.0f} kg"
-        ])
-        total_outliers = sum(info['count'] for info in outliers.values())
-        context_parts.extend([
-            "",
-            "## Quality Control:",
-            f"- Total outliers detected: {total_outliers}",
-            f"- Materials with quality issues: {sum(1 for info in outliers.values() if info['count'] > 0)}"
-        ])
-        if 'shift' in df.columns:
-            shift_stats = df.groupby('shift')['weight_kg'].sum()
-            context_parts.extend([
-                "",
-                "## Shift Performance:",
-                f"- Day shift: {shift_stats.get('day', 0):,.0f} kg",
-                f"- Night shift: {shift_stats.get('night', 0):,.0f} kg"
-            ])
-        context_text = "\n".join(context_parts)
+            context += f"- {material.title()}: {info['total']:,.0f} kg monitored ({info['percentage']:.1f}% coverage)\n"
+        
         prompt = f"""
-{context_text}
+{context}
+
+As Nilsen Service & Consulting's AI system, demonstrate the value we've delivered to management.
+
+**OUR TECHNICAL ACHIEVEMENTS**
+Highlight what our team has successfully implemented and delivered.
 
-As an expert AI analyst embedded within the "Production Monitor with AI Insights" platform, provide a comprehensive analysis based on the data provided. Your tone should be professional and data-driven. Your primary goal is to highlight how the platform's features reveal critical insights.
+**BUSINESS VALUE PROVIDED**
+Explain the operational improvements and insights our monitoring system enables.
 
-Structure your response in the following format:
+**SYSTEM CAPABILITIES**
+Showcase the advanced features and reliability of our solution.
 
-**PRODUCTION ASSESSMENT**
-Evaluate the overall production status (Excellent/Good/Needs Attention). Briefly justify your assessment using key metrics from the data summary.
+Focus on our professional competence, technical excellence, and delivered value. Show why our monitoring system is essential for their operations. Maximum 300 words.
+"""
 
-**KEY FINDINGS**
-Identify 3-4 of the most important insights. For each finding, explicitly mention the platform feature that made the discovery possible. Use formats like "(revealed by the 'Quality Check' module)" or "(visualized in the 'Production Trend' chart)".
+        response = model.generate_content(prompt)
+        return response.text
+        
+    except Exception as e:
+        return f"AI analysis error: {str(e)}"
+
+def query_ai(model, stats, question, df=None):
+    """AI responses showcasing our system capabilities"""
+    if not model:
+        return "AI assistant not available"
+    
+    try:
+        context = f"""
+NILSEN SERVICE & CONSULTING - PRODUCTION MONITORING SYSTEM
 
-Example Finding format:
-• Finding X: [Your insight, e.g., "Liquid-Ctu production shows high volatility..."] (as identified by the 'Materials Analysis' view).
+Our System Performance:
+- Total Production Monitored: {stats['_total_']['total']:,.0f} kg
+- Daily Processing Capacity: {stats['_total_']['daily_avg']:,.0f} kg average
+- Operational Coverage: {stats['_total_']['work_days']} work days
+- Data Accuracy: Professional-grade monitoring with real-time analytics
 
-**RECOMMENDATIONS**
-Provide 2-3 actionable recommendations. Frame these as steps the management can take, encouraging them to use the platform for further investigation.
+Our Technical Solutions:
+"""
+        materials = [k for k in stats.keys() if k != '_total_']
+        for material in materials:
+            info = stats[material]
+            context += f"- {material.title()}: {info['percentage']:.1f}% production coverage\n"
+        
+        prompt = f"""
+{context}
 
-Example Recommendation format:
-• Recommendation Y: [Your recommendation, e.g., "Investigate the root causes of the 11 outliers..."] We recommend using the platform's interactive charts to drill down into the specific dates identified by the 'Quality Check' module.
+Question: {question}
 
-Keep the entire analysis concise and under 300 words.
+Respond as Nilsen Service & Consulting's AI system. Emphasize our technical expertise, system reliability, and the professional insights we provide. Show how our monitoring solution delivers superior operational intelligence. Keep under 120 words.
 """
+
         response = model.generate_content(prompt)
         return response.text
+        
     except Exception as e:
-        return f"AI analysis error: {str(e)}"
+        return f"System response unavailable: {str(e)}"
+
+def render_ai_section(df, stats, model):
+    """Showcase our AI capabilities and technical achievements"""
+    
+    if model:
+        st.markdown('<div class="section-header">🚀 Our AI-Powered Solution</div>', unsafe_allow_html=True)
+        
+        # Demonstrate our capabilities
+        st.markdown("### 🎯 **Nilsen Service & Consulting - Technical Achievements**")
+        outliers = detect_outliers(df)
+        with st.spinner("Demonstrating our AI capabilities..."):
+            ai_summary = generate_ai_summary(model, df, stats, outliers)
+            st.success(ai_summary)
+        
+        # Show our system's intelligence
+        st.markdown("### 🧠 **Experience Our Advanced Analytics**")
+        st.markdown("*See how our AI system provides professional insights for your operations:*")
+        
+        demo_questions = [
+            "How does our monitoring system ensure production quality?",
+            "What operational advantages does our platform provide?",
+            "How does our AI deliver cost savings and efficiency gains?"
+        ]
+        
+        cols = st.columns(len(demo_questions))
+        for i, question in enumerate(demo_questions):
+            with cols[i]:
+                if st.button(f"▶️ {question}", key=f"demo_q_{i}"):
+                    with st.spinner("Our AI analyzing..."):
+                        answer = query_ai(model, stats, question, df)
+                        st.info(f"**Nilsen AI Response:** {answer}")
+        
+        # Interactive demonstration
+        st.markdown("### 💼 **Test Our AI Intelligence**")
+        col1, col2 = st.columns([3, 1])
+        
+        with col1:
+            custom_question = st.text_input("Ask our AI system about production management:", 
+                                            placeholder="e.g., 'How can your system improve our operational efficiency?'",
+                                            key="demo_question")
+        with col2:
+            st.markdown("<br>", unsafe_allow_html=True)  # Add spacing
+            if st.button("🚀 **See Our AI in Action**", key="demo_btn", type="primary"):
+                if custom_question:
+                    with st.spinner("Nilsen AI processing..."):
+                        answer = query_ai(model, stats, custom_question, df)
+                        st.success(f"**Your Question:** {custom_question}")
+                        st.info(f"**Our AI Solution:** {answer}")
+                else:
+                    st.warning("Please enter a question to see our AI capabilities")
+        
+        # Value proposition
+        st.markdown("---")
+        st.markdown("""
+        <div style="background: linear-gradient(135deg, #1E40AF15, #05966925); padding: 1rem; border-radius: 8px; border: 1px solid #1E40AF;">
+        <h4>🏆 Why Choose Nilsen Service & Consulting?</h4>
+        <ul>
+        <li>✅ <strong>Advanced AI Integration:</strong> Cutting-edge analytics for your operations</li>
+        <li>✅ <strong>Professional Reliability:</strong> Enterprise-grade monitoring systems</li>
+        <li>✅ <strong>Proven Results:</strong> Data-driven insights that deliver ROI</li>
+        <li>✅ <strong>Complete Solution:</strong> From data processing to actionable recommendations</li>
+        </ul>
+        </div>
+        """, unsafe_allow_html=True)
+        
+    else:
+        st.markdown('<div class="section-header">🚀 Our AI-Powered Solution</div>', unsafe_allow_html=True)
+        st.error("⚠️ AI demonstration requires API configuration")
+        st.info("💡 **Our AI system provides:** Advanced analytics, predictive insights, automated reporting, and intelligent recommendations for production optimization.")
+
 
 def query_ai(model, stats, question, df=None):
     if not model: