Update app.py

app.py

@@ -513,16 +513,16 @@ def create_pdf_charts(df, stats):
             try:
                 daily_data = df.groupby('date')['weight_kg'].sum().reset_index()
                 if len(daily_data) > 0:
-                    fig_trend = px.line(daily_data, x='date', y='weight_kg', title="Daily Production Trend",
-                                        labels={'date': 'Date', 'weight_kg': 'Weight (kg)'})
+                    fig_trend = px.line(daily_data, x='date', y='weight_kg', title="Daily Production Trend")
+                    fig_trend.update_layout(xaxis_title="Date", yaxis_title="Weight (kg)")
                     charts['trend'] = save_plotly_as_image(fig_trend, "trend.png")
             except:
                 pass
         
         if len(materials) > 0 and len(values) > 0:
             try:
-                fig_bar = px.bar(x=labels, y=values, title="Production by Material Type",
-                                 labels={'x': 'Material Type', 'y': 'Weight (kg)'})
+                fig_bar = px.bar(x=labels, y=values, title="Production by Material Type")
+                fig_bar.update_layout(xaxis_title="Material Type", yaxis_title="Weight (kg)")
                 charts['bar'] = save_plotly_as_image(fig_bar, "materials.png")
             except:
                 pass
@@ -603,4 +603,450 @@ def create_enhanced_pdf_report(df, stats, outliers, model=None):
     Total output achieved: <b>{total_production:,.0f} kg</b> with an average 
     daily production of <b>{daily_avg:,.0f} kg</b>.
     <br/><br/>
-    <b>Key Highlights:</b><br/>
+    <b>Key Highlights:</b><br/>
+    • Total production: {total_production:,.0f} kg<br/>
+    • Daily average: {daily_avg:,.0f} kg<br/>
+    • Materials tracked: {len([k for k in stats.keys() if k != '_total_'])}<br/>
+    • Data quality: {len(df):,} records processed
+    </para>
+    """
+    elements.append(Paragraph(exec_summary, styles['Normal']))
+    elements.append(Spacer(1, 20))
+    
+    elements.append(Paragraph("Production Summary", styles['Heading3']))
+    
+    summary_data = [['Material Type', 'Total (kg)', 'Share (%)', 'Daily Avg (kg)']]
+    
+    for material, info in stats.items():
+        if material != '_total_':
+            summary_data.append([
+                material.replace('_', ' ').title(),
+                f"{info['total']:,.0f}",
+                f"{info['percentage']:.1f}%",
+                f"{info['daily_avg']:,.0f}"
+            ])
+    
+    summary_table = Table(summary_data, colWidths=[2*inch, 1.5*inch, 1*inch, 1.5*inch])
+    summary_table.setStyle(TableStyle([
+        ('BACKGROUND', (0, 0), (-1, 0), colors.darkblue),
+        ('TEXTCOLOR', (0, 0), (-1, 0), colors.whitesmoke),
+        ('ALIGN', (0, 0), (-1, -1), 'CENTER'),
+        ('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
+        ('GRID', (0, 0), (-1, -1), 1, colors.black),
+        ('ROWBACKGROUNDS', (0, 1), (-1, -1), [colors.white, colors.lightgrey])
+    ]))
+    
+    elements.append(summary_table)
+    elements.append(PageBreak())
+    
+    elements.append(Paragraph("Production Analysis Charts", subtitle_style))
+    
+    try:
+        charts = create_pdf_charts(df, stats)
+    except:
+        charts = {}
+    
+    charts_added = False
+    
+    chart_insights = {
+        'pie': "Material distribution shows production allocation across different materials. Balanced distribution indicates diversified production capabilities.",
+        'trend': "Production trend reveals operational patterns and seasonal variations. Consistent trends suggest stable operational efficiency.",
+        'bar': "Material comparison highlights performance differences and production capacities. Top performers indicate optimization opportunities.",
+        'shift': "Shift analysis reveals operational efficiency differences between day and night operations. Balance indicates effective resource utilization."
+    }
+    
+    for chart_type, chart_title in [
+        ('pie', "Production Distribution"),
+        ('trend', "Production Trend"), 
+        ('bar', "Material Comparison"),
+        ('shift', "Shift Analysis")
+    ]:
+        chart_path = charts.get(chart_type)
+        if chart_path and os.path.exists(chart_path):
+            try:
+                elements.append(Paragraph(chart_title, styles['Heading3']))
+                elements.append(Image(chart_path, width=6*inch, height=3*inch))
+                
+                insight_text = f"<i>Analysis: {chart_insights.get(chart_type, 'Chart analysis not available.')}</i>"
+                elements.append(Paragraph(insight_text, ai_style))
+                elements.append(Spacer(1, 20))
+                charts_added = True
+            except Exception as e:
+                pass
+    
+    if not charts_added:
+        elements.append(Paragraph("Charts Generation Failed", styles['Heading3']))
+        elements.append(Paragraph("Production Data Summary:", styles['Normal']))
+        for material, info in stats.items():
+            if material != '_total_':
+                summary_text = f"• {material.replace('_', ' ').title()}: {info['total']:,.0f} kg ({info['percentage']:.1f}%)"
+                elements.append(Paragraph(summary_text, styles['Normal']))
+        elements.append(Spacer(1, 20))
+    
+    elements.append(PageBreak())
+    elements.append(Paragraph("Quality Control Analysis", subtitle_style))
+    
+    quality_data = [['Material', 'Outliers', 'Normal Range (kg)', 'Status']]
+    
+    for material, info in outliers.items():
+        if info['count'] == 0:
+            status = "GOOD"
+        elif info['count'] <= 3:
+            status = "MONITOR"
+        else:
+            status = "ATTENTION"
+        
+        quality_data.append([
+            material.replace('_', ' ').title(),
+            str(info['count']),
+            info['range'],
+            status
+        ])
+    
+    quality_table = Table(quality_data, colWidths=[2*inch, 1*inch, 2*inch, 1.5*inch])
+    quality_table.setStyle(TableStyle([
+        ('BACKGROUND', (0, 0), (-1, 0), colors.darkred),
+        ('TEXTCOLOR', (0, 0), (-1, 0), colors.whitesmoke),
+        ('ALIGN', (0, 0), (-1, -1), 'CENTER'),
+        ('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
+        ('GRID', (0, 0), (-1, -1), 1, colors.black),
+        ('ROWBACKGROUNDS', (0, 1), (-1, -1), [colors.white, colors.lightgrey])
+    ]))
+    
+    elements.append(quality_table)
+    
+    if model:
+        elements.append(PageBreak())
+        elements.append(Paragraph("AI Intelligent Analysis", subtitle_style))
+        
+        try:
+            ai_analysis = generate_ai_summary(model, df, stats, outliers)
+        except:
+            ai_analysis = "AI analysis temporarily unavailable."
+        
+        ai_paragraphs = ai_analysis.split('\n\n')
+        for paragraph in ai_paragraphs:
+            if paragraph.strip():
+                formatted_text = paragraph.replace('**', '<b>', 1).replace('**', '</b>', 1)
+                formatted_text = formatted_text.replace('•', '  •')
+                elements.append(Paragraph(formatted_text, styles['Normal']))
+                elements.append(Spacer(1, 8))
+    else:
+        elements.append(PageBreak())
+        elements.append(Paragraph("AI Analysis", subtitle_style))
+        elements.append(Paragraph("AI analysis unavailable - API key not configured. Please configure Google AI API key to enable intelligent insights.", styles['Normal']))
+    
+    elements.append(Spacer(1, 30))
+    footer_text = f"""
+    <para alignment="center">
+    <i>This report was generated by Production Monitor System<br/>
+    Nilsen Service &amp; Consulting AS - Production Analytics Division<br/>
+    Report contains {len(df):,} data records across {stats['_total_']['work_days']} working days</i>
+    </para>
+    """
+    elements.append(Paragraph(footer_text, styles['Normal']))
+    
+    doc.build(elements)
+    buffer.seek(0)
+    return buffer
+
+def create_csv_export(df, stats):
+    summary_df = pd.DataFrame([
+        {
+            'Material': material.replace('_', ' ').title(),
+            'Total_kg': info['total'],
+            'Percentage': info['percentage'],
+            'Daily_Average_kg': info['daily_avg'],
+            'Work_Days': info['work_days'],
+            'Records_Count': info['records']
+        }
+        for material, info in stats.items() if material != '_total_'
+    ])
+    
+    return summary_df
+
+def add_export_section(df, stats, outliers, model):
+    st.markdown('<div class="section-header">📄 Export Reports</div>', unsafe_allow_html=True)
+    
+    if 'export_ready' not in st.session_state:
+        st.session_state.export_ready = False
+    if 'pdf_buffer' not in st.session_state:
+        st.session_state.pdf_buffer = None
+    if 'csv_data' not in st.session_state:
+        st.session_state.csv_data = None
+    
+    col1, col2, col3 = st.columns(3)
+    
+    with col1:
+        if st.button("📊 Generate PDF Report with AI", key="generate_pdf_btn", type="primary"):
+            try:
+                with st.spinner("Generating PDF with AI analysis..."):
+                    st.session_state.pdf_buffer = create_enhanced_pdf_report(df, stats, outliers, model)
+                    st.session_state.export_ready = True
+                st.success("✅ PDF report with AI analysis generated successfully!")
+                
+            except Exception as e:
+                st.error(f"❌ PDF generation failed: {str(e)}")
+                st.session_state.export_ready = False
+        
+        if st.session_state.export_ready and st.session_state.pdf_buffer:
+            st.download_button(
+                label="💾 Download PDF Report",
+                data=st.session_state.pdf_buffer,
+                file_name=f"production_report_ai_{datetime.now().strftime('%Y%m%d_%H%M')}.pdf",
+                mime="application/pdf",
+                key="download_pdf_btn"
+            )
+    
+    with col2:
+        if st.button("📈 Generate CSV Summary", key="generate_csv_btn"):
+            try:
+                st.session_state.csv_data = create_csv_export(df, stats)
+                st.success("✅ CSV summary generated successfully!")
+            except Exception as e:
+                st.error(f"❌ CSV generation failed: {str(e)}")
+        
+        if st.session_state.csv_data is not None:
+            csv_string = st.session_state.csv_data.to_csv(index=False)
+            st.download_button(
+                label="💾 Download CSV Summary",
+                data=csv_string,
+                file_name=f"production_summary_{datetime.now().strftime('%Y%m%d_%H%M')}.csv",
+                mime="text/csv",
+                key="download_csv_btn"
+            )
+    
+    with col3:
+        csv_string = df.to_csv(index=False)
+        st.download_button(
+            label="📋 Download Raw Data",
+            data=csv_string,
+            file_name=f"raw_production_data_{datetime.now().strftime('%Y%m%d_%H%M')}.csv",
+            mime="text/csv",
+            key="download_raw_btn"
+        )
+    
+    if st.session_state.export_ready or st.session_state.csv_data is not None:
+        st.markdown("---")
+        if st.button("🔄 Reset Export Cache", key="reset_export_btn"):
+            st.session_state.export_ready = False
+            st.session_state.pdf_buffer = None
+            st.session_state.csv_data = None
+            st.rerun()
+
+def main():
+    load_css()
+    
+    st.markdown("""
+    <div class="main-header">
+        <div class="main-title">🏭 Production Monitor</div>
+        <div class="main-subtitle">Nilsen Service & Consulting AS | Real-time Production Analytics with AI Insights</div>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    model = init_ai()
+    
+    if 'current_df' not in st.session_state:
+        st.session_state.current_df = None
+    if 'current_stats' not in st.session_state:
+        st.session_state.current_stats = None
+    
+    with st.sidebar:
+        st.markdown("### 📊 Data Source")
+        
+        uploaded_file = st.file_uploader("Upload Production Data", type=['csv'])
+        
+        st.markdown("---")
+        st.markdown("### 📊 Quick Load")
+        
+        col1, col2 = st.columns(2)
+        with col1:
+            if st.button("📊 2024 Data", type="primary", key="load_2024"):
+                st.session_state.load_preset = "2024"
+        with col2:
+            if st.button("📊 2025 Data", type="primary", key="load_2025"):
+                st.session_state.load_preset = "2025"
+        
+        st.markdown("---")
+        st.markdown("""
+        **Expected TSV format:**
+        - `date`: MM/DD/YYYY
+        - `weight_kg`: Production weight
+        - `material_type`: Material category
+        - `shift`: day/night (optional)
+        """)
+        
+        if model:
+            st.success("🤖 AI Assistant Ready")
+        else:
+            st.warning("⚠️ AI Assistant Unavailable")
+    
+    df = st.session_state.current_df
+    stats = st.session_state.current_stats
+    
+    if uploaded_file:
+        try:
+            df = load_data(uploaded_file)
+            stats = get_material_stats(df)
+            st.session_state.current_df = df
+            st.session_state.current_stats = stats
+            st.success("✅ Data uploaded successfully!")
+        except Exception as e:
+            st.error(f"❌ Error loading uploaded file: {str(e)}")
+            
+    elif 'load_preset' in st.session_state:
+        year = st.session_state.load_preset
+        try:
+            with st.spinner(f"Loading {year} data..."):
+                df = load_preset_data(year)
+            if df is not None:
+                stats = get_material_stats(df)
+                st.session_state.current_df = df
+                st.session_state.current_stats = stats
+                st.success(f"✅ {year} data loaded successfully!")
+        except Exception as e:
+            st.error(f"❌ Error loading {year} data: {str(e)}")
+        finally:
+            del st.session_state.load_preset
+    
+    if df is not None and stats is not None:
+        st.markdown('<div class="section-header">📋 Material Overview</div>', unsafe_allow_html=True)
+        materials = [k for k in stats.keys() if k != '_total_']
+        
+        cols = st.columns(4)
+        for i, material in enumerate(materials[:3]):
+            info = stats[material]
+            with cols[i]:
+                st.metric(
+                    label=material.replace('_', ' ').title(),
+                    value=f"{info['total']:,.0f} kg",
+                    delta=f"{info['percentage']:.1f}% of total"
+                )
+                st.caption(f"Daily avg: {info['daily_avg']:,.0f} kg")
+        
+        if len(materials) >= 3:
+            total_info = stats['_total_']
+            with cols[3]:
+                st.metric(
+                    label="Total Production",
+                    value=f"{total_info['total']:,.0f} kg",
+                    delta="100% of total"
+                )
+                st.caption(f"Daily avg: {total_info['daily_avg']:,.0f} kg")
+        
+        st.markdown('<div class="section-header">📊 Production Trends</div>', unsafe_allow_html=True)
+        
+        col1, col2 = st.columns([3, 1])
+        with col2:
+            time_view = st.selectbox("Time Period", ["daily", "weekly", "monthly"], key="time_view_select")
+        
+        with col1:
+            with st.container():
+                st.markdown('<div class="chart-container">', unsafe_allow_html=True)
+                total_chart = create_total_production_chart(df, time_view)
+                st.plotly_chart(total_chart, use_container_width=True)
+                st.markdown('</div>', unsafe_allow_html=True)
+        
+        st.markdown('<div class="section-header">🏷️ Materials Analysis</div>', unsafe_allow_html=True)
+        
+        col1, col2 = st.columns([3, 1])
+        with col2:
+            selected_materials = st.multiselect(
+                "Select Materials", 
+                options=materials, 
+                default=materials,
+                key="materials_select"
+            )
+        
+        with col1:
+            if selected_materials:
+                with st.container():
+                    st.markdown('<div class="chart-container">', unsafe_allow_html=True)
+                    materials_chart = create_materials_trend_chart(df, time_view, selected_materials)
+                    st.plotly_chart(materials_chart, use_container_width=True)
+                    st.markdown('</div>', unsafe_allow_html=True)
+        
+        if 'shift' in df.columns:
+            st.markdown('<div class="section-header">🌓 Shift Analysis</div>', unsafe_allow_html=True)
+            
+            with st.container():
+                st.markdown('<div class="chart-container">', unsafe_allow_html=True)
+                shift_chart = create_shift_trend_chart(df, time_view)
+                st.plotly_chart(shift_chart, use_container_width=True)
+                st.markdown('</div>', unsafe_allow_html=True)
+        
+        st.markdown('<div class="section-header">⚠️ Quality Check</div>', unsafe_allow_html=True)
+        outliers = detect_outliers(df)
+        
+        cols = st.columns(len(outliers))
+        for i, (material, info) in enumerate(outliers.items()):
+            with cols[i]:
+                if info['count'] > 0:
+                    if len(info['dates']) <= 5:
+                        dates_str = ", ".join(info['dates'])
+                    else:
+                        dates_str = f"{', '.join(info['dates'][:3])}, +{len(info['dates'])-3} more"
+                    
+                    st.markdown(f'<div class="alert-warning"><strong>{material.title()}</strong><br>{info["count"]} outliers detected<br>Normal range: {info["range"]}<br><small>Dates: {dates_str}</small></div>', unsafe_allow_html=True)
+                else:
+                    st.markdown(f'<div class="alert-success"><strong>{material.title()}</strong><br>All values normal</div>', unsafe_allow_html=True)
+        
+        add_export_section(df, stats, outliers, model)
+        
+        if model:
+            st.markdown('<div class="section-header">🤖 AI Insights</div>', unsafe_allow_html=True)
+            
+            quick_questions = [
+                "How does production distribution on weekdays compare to weekends?",
+                "Which material exhibits the most volatility in our dataset?",
+                "To improve stability, which material or shift needs immediate attention?"
+            ]
+            
+            cols = st.columns(len(quick_questions))
+            for i, q in enumerate(quick_questions):
+                with cols[i]:
+                    if st.button(q, key=f"ai_q_{i}"):
+                        with st.spinner("Analyzing..."):
+                            answer = query_ai(model, stats, q, df)
+                            st.info(answer)
+            
+            custom_question = st.text_input("Ask about your production data:", key="custom_ai_question")
+            if custom_question and st.button("Ask AI", key="ask_ai_btn"):
+                with st.spinner("Analyzing..."):
+                    answer = query_ai(model, stats, custom_question, df)
+                    st.success(f"**Q:** {custom_question}")
+                    st.write(f"**A:** {answer}")
+    
+    else:
+        st.markdown('<div class="section-header">📖 How to Use This Platform</div>', unsafe_allow_html=True)
+        
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            st.markdown("""
+            ### 🚀 **Quick Start**
+            1. Upload your TSV data in the sidebar
+            2. Or click Quick Load buttons for preset data
+            3. View production by material type
+            4. Analyze trends (daily/weekly/monthly)
+            5. Check anomalies in Quality Check
+            6. Export reports (PDF with AI, CSV)
+            7. Ask the AI assistant for insights
+            """)
+        
+        with col2:
+            st.markdown("""
+            ### 📊 **Key Features**
+            - Real-time interactive charts
+            - One-click preset data loading
+            - Time-period comparisons
+            - Shift performance analysis
+            - Outlier detection with dates
+            - AI-powered PDF reports
+            - Intelligent recommendations
+            """)
+        
+        st.info("📁 Ready to start? Upload your production data or use Quick Load buttons to begin analysis!")
+
+if __name__ == "__main__":
+    main()