Update app.py

app.py

@@ -12,11 +12,6 @@ from reportlab.lib.pagesizes import letter, A4
 from reportlab.platypus import SimpleDocTemplate, Table, TableStyle, Paragraph, Spacer, Image, PageBreak
 from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
 from reportlab.lib.units import inch
-from reportlab.graphics.shapes import Drawing
-from reportlab.graphics.charts.linecharts import HorizontalLineChart
-from reportlab.graphics.charts.piecharts import Pie
-from reportlab.graphics.charts.barcharts import VerticalBarChart
-from reportlab.graphics import renderPDF
 import plotly.io as pio
 import tempfile
 import os
@@ -24,14 +19,14 @@ import os
 # Design System Configuration
 DESIGN_SYSTEM = {
     'colors': {
-        'primary': '#1E40AF',      # Blue
-        'secondary': '#059669',     # Green
-        'accent': '#DC2626',       # Red
-        'warning': '#D97706',      # Orange
-        'success': '#10B981',      # Emerald
-        'background': '#F8FAFC',   # Light gray
-        'text': '#1F2937',         # Dark gray
-        'border': '#E5E7EB'        # Light border
+        'primary': '#1E40AF',
+        'secondary': '#059669',
+        'accent': '#DC2626',
+        'warning': '#D97706',
+        'success': '#10B981',
+        'background': '#F8FAFC',
+        'text': '#1F2937',
+        'border': '#E5E7EB'
     },
     'fonts': {
         'title': 'font-family: "Inter", sans-serif; font-weight: 700;',
@@ -48,7 +43,7 @@ st.set_page_config(
     initial_sidebar_state="expanded"
 )
 
-# Custom CSS for design system
+# Custom CSS
 def load_css():
     st.markdown(f"""
     <style>
@@ -86,11 +81,6 @@ def load_css():
         transition: transform 0.2s ease;
     }}
     
-    .metric-card:hover {{
-        transform: translateY(-2px);
-        box-shadow: 0 4px 12px rgba(0,0,0,0.15);
-    }}
-    
     .section-header {{
         {DESIGN_SYSTEM['fonts']['subtitle']}
         color: {DESIGN_SYSTEM['colors']['text']};
@@ -124,11 +114,6 @@ def load_css():
         color: {DESIGN_SYSTEM['colors']['warning']};
     }}
     
-    .stSelectbox > div > div {{
-        border-radius: 8px;
-        border: 1px solid {DESIGN_SYSTEM['colors']['border']};
-    }}
-    
     .stButton > button {{
         background: {DESIGN_SYSTEM['colors']['primary']};
         color: white;
@@ -138,11 +123,6 @@ def load_css():
         font-weight: 500;
         transition: all 0.2s ease;
     }}
-    
-    .stButton > button:hover {{
-        background: {DESIGN_SYSTEM['colors']['secondary']};
-        transform: translateY(-1px);
-    }}
     </style>
     """, unsafe_allow_html=True)
 
@@ -341,66 +321,66 @@ def query_ai(model, stats, question, df=None):
     except:
         return "Error getting AI response"
 
-def create_production_pie_chart():
-    """Create production distribution pie chart for PDF"""
-    drawing = Drawing(400, 200)
-    pie = Pie()
-    pie.x = 50
-    pie.y = 50
-    pie.width = 120
-    pie.height = 120
-    
-    # Sample data - will be replaced with real data
-    pie.data = [60, 30, 10]
-    pie.labels = ['Liquid', 'Solid', 'Waste Water']
-    pie.slices.strokeWidth = 0.5
-    pie.slices[0].fillColor = colors.blue
-    pie.slices[1].fillColor = colors.green
-    pie.slices[2].fillColor = colors.orange
-    
-    drawing.add(pie)
-    return drawing
-
-def create_trend_chart():
-    """Create production trend chart for PDF"""
-    drawing = Drawing(400, 200)
-    chart = HorizontalLineChart()
-    chart.x = 50
-    chart.y = 50
-    chart.height = 120
-    chart.width = 300
-    chart.data = [
-        [100, 120, 140, 110, 160, 150, 180],
-    ]
-    chart.lines[0].strokeColor = colors.blue
-    chart.lines[0].strokeWidth = 2
-    
-    drawing.add(chart)
-    return drawing
-
 def save_plotly_as_image(fig, filename):
-    """Convert Plotly figure to image for PDF"""
+    """Convert Plotly figure to PNG for PDF"""
     try:
-        # Create temp directory if it doesn't exist
         temp_dir = tempfile.gettempdir()
         filepath = os.path.join(temp_dir, filename)
         
-        # Save as PNG
-        pio.write_image(fig, filepath, format='png', width=800, height=400)
+        # Optimize for PDF
+        fig.update_layout(
+            font=dict(size=12, family="Arial"),
+            plot_bgcolor='white',
+            paper_bgcolor='white',
+            margin=dict(t=50, b=40, l=40, r=40)
+        )
+        
+        pio.write_image(fig, filepath, format='png', width=800, height=400, scale=2)
         return filepath
     except Exception as e:
-        print(f"Error saving chart: {e}")
+        st.error(f"Chart save error: {e}")
         return None
 
+def create_pdf_charts(df, stats):
+    """Generate charts for PDF report"""
+    charts = {}
+    
+    # Production distribution pie chart
+    materials = [k for k in stats.keys() if k != '_total_']
+    values = [stats[mat]['total'] for mat in materials]
+    labels = [mat.replace('_', ' ').title() for mat in materials]
+    
+    fig_pie = px.pie(values=values, names=labels, title="Production Distribution by Material")
+    charts['pie'] = save_plotly_as_image(fig_pie, "distribution.png")
+    
+    # Daily production trend
+    daily_data = df.groupby('date')['weight_kg'].sum().reset_index()
+    fig_trend = px.line(daily_data, x='date', y='weight_kg', title="Daily Production Trend")
+    charts['trend'] = save_plotly_as_image(fig_trend, "trend.png")
+    
+    # Material comparison bar chart
+    fig_bar = px.bar(x=labels, y=values, title="Production by Material Type")
+    fig_bar.update_xaxis(title="Material Type")
+    fig_bar.update_yaxis(title="Weight (kg)")
+    charts['bar'] = save_plotly_as_image(fig_bar, "materials.png")
+    
+    # Shift analysis if available
+    if 'shift' in df.columns:
+        shift_data = df.groupby('shift')['weight_kg'].sum().reset_index()
+        fig_shift = px.pie(shift_data, values='weight_kg', names='shift', title="Production by Shift")
+        charts['shift'] = save_plotly_as_image(fig_shift, "shifts.png")
+    
+    return charts
+
 def create_enhanced_pdf_report(df, stats, outliers):
-    """Generate enhanced PDF report with charts and detailed analysis"""
+    """Generate PDF report with Plotly charts only"""
     buffer = io.BytesIO()
     doc = SimpleDocTemplate(buffer, pagesize=A4, rightMargin=50, leftMargin=50, topMargin=50, bottomMargin=50)
     
     elements = []
-    
-    # Define custom styles
     styles = getSampleStyleSheet()
+    
+    # Custom styles
     title_style = ParagraphStyle(
         'CustomTitle',
         parent=styles['Heading1'],
@@ -415,29 +395,26 @@ def create_enhanced_pdf_report(df, stats, outliers):
         parent=styles['Heading2'],
         fontSize=16,
         spaceAfter=20,
-        textColor=colors.darkblue,
-        borderWidth=1,
-        borderColor=colors.darkblue,
-        borderPadding=10
+        textColor=colors.darkblue
     )
     
-    # Cover Page
+    # Cover page
     elements.append(Spacer(1, 100))
-    elements.append(Paragraph("🏭 PRODUCTION MONITOR", title_style))
-    elements.append(Paragraph("Comprehensive Production Analysis Report", styles['Heading3']))
+    elements.append(Paragraph("Production Monitor Report", title_style))
+    elements.append(Paragraph("Comprehensive Production Analysis", styles['Heading3']))
     elements.append(Spacer(1, 50))
     
-    # Company info
-    company_info = f"""
+    # Report info
+    report_info = f"""
     <para alignment="center">
     <b>Nilsen Service & Consulting AS</b><br/>
     Production Analytics Division<br/><br/>
     <b>Report Period:</b> {df['date'].min().strftime('%B %d, %Y')} - {df['date'].max().strftime('%B %d, %Y')}<br/>
     <b>Generated:</b> {datetime.now().strftime('%B %d, %Y at %H:%M')}<br/>
-    <b>Total Records Analyzed:</b> {len(df):,}
+    <b>Total Records:</b> {len(df):,}
     </para>
     """
-    elements.append(Paragraph(company_info, styles['Normal']))
+    elements.append(Paragraph(report_info, styles['Normal']))
     elements.append(PageBreak())
     
     # Executive Summary
@@ -449,214 +426,106 @@ def create_enhanced_pdf_report(df, stats, outliers):
     
     exec_summary = f"""
     <para>
-    This report presents a comprehensive analysis of production data spanning <b>{work_days} working days</b>. 
-    Our production facilities achieved a total output of <b>{total_production:,.0f} kg</b> with an average 
+    This report analyzes production data spanning <b>{work_days} working days</b>. 
+    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/>
-    • Total production volume: {total_production:,.0f} kg<br/>
-    • Average daily output: {daily_avg:,.0f} kg<br/>
-    • Production efficiency: {(len([info for info in outliers.values() if info['count'] == 0]) / len(outliers)) * 100:.0f}% materials within normal range<br/>
-    • Data quality: {len(df):,} records processed with high accuracy
+    • 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))
     
-    # Production Overview Table
-    elements.append(Paragraph("Production Breakdown by Material", styles['Heading3']))
+    # Production Summary Table
+    elements.append(Paragraph("Production Summary", styles['Heading3']))
     
-    summary_data = [['Material Type', 'Total Output (kg)', 'Market Share (%)', 'Daily Average (kg)', 'Performance Rating']]
+    summary_data = [['Material Type', 'Total (kg)', 'Share (%)', 'Daily Avg (kg)']]
     
     for material, info in stats.items():
         if material != '_total_':
-            # Calculate performance rating
-            outlier_count = outliers.get(material, {}).get('count', 0)
-            if outlier_count == 0:
-                rating = "⭐⭐⭐⭐⭐ Excellent"
-            elif outlier_count <= 2:
-                rating = "⭐⭐⭐⭐ Good"
-            elif outlier_count <= 5:
-                rating = "⭐⭐⭐ Fair"
-            else:
-                rating = "⭐⭐ Needs Attention"
-            
             summary_data.append([
                 material.replace('_', ' ').title(),
                 f"{info['total']:,.0f}",
                 f"{info['percentage']:.1f}%",
-                f"{info['daily_avg']:,.0f}",
-                rating
+                f"{info['daily_avg']:,.0f}"
             ])
     
-    # Add total row
-    total_info = stats['_total_']
-    summary_data.append([
-        'TOTAL PRODUCTION',
-        f"{total_info['total']:,.0f}",
-        '100.0%',
-        f"{total_info['daily_avg']:,.0f}",
-        '📊 Combined'
-    ])
-    
-    summary_table = Table(summary_data, colWidths=[2*inch, 1.5*inch, 1*inch, 1.5*inch, 1.5*inch])
+    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'),
-        ('FONTSIZE', (0, 0), (-1, 0), 12),
-        ('BOTTOMPADDING', (0, 0), (-1, 0), 15),
-        ('BACKGROUND', (0, -1), (-1, -1), colors.lightblue),
-        ('FONTNAME', (0, -1), (-1, -1), 'Helvetica-Bold'),
         ('GRID', (0, 0), (-1, -1), 1, colors.black),
-        ('ROWBACKGROUNDS', (0, 1), (-1, -2), [colors.white, colors.lightgrey]),
+        ('ROWBACKGROUNDS', (0, 1), (-1, -1), [colors.white, colors.lightgrey])
     ]))
     
     elements.append(summary_table)
-    elements.append(Spacer(1, 30))
+    elements.append(PageBreak())
+    
+    # Generate and add charts
+    elements.append(Paragraph("Production Analysis Charts", subtitle_style))
+    
+    with st.spinner("Generating charts for PDF..."):
+        charts = create_pdf_charts(df, stats)
+    
+    # Add charts to PDF
+    if charts['pie'] and os.path.exists(charts['pie']):
+        elements.append(Paragraph("Production Distribution", styles['Heading3']))
+        elements.append(Image(charts['pie'], width=6*inch, height=3*inch))
+        elements.append(Spacer(1, 20))
+    
+    if charts['trend'] and os.path.exists(charts['trend']):
+        elements.append(Paragraph("Production Trend", styles['Heading3']))
+        elements.append(Image(charts['trend'], width=6*inch, height=3*inch))
+        elements.append(Spacer(1, 20))
     
-    # Add simple charts
-    elements.append(Paragraph("Production Distribution", styles['Heading3']))
+    if charts['bar'] and os.path.exists(charts['bar']):
+        elements.append(Paragraph("Material Comparison", styles['Heading3']))
+        elements.append(Image(charts['bar'], width=6*inch, height=3*inch))
+        elements.append(Spacer(1, 20))
     
-    # Create a simple pie chart
-    pie_chart = create_production_pie_chart()
-    elements.append(pie_chart)
-    elements.append(Spacer(1, 30))
+    if 'shift' in charts and charts['shift'] and os.path.exists(charts['shift']):
+        elements.append(Paragraph("Shift Analysis", styles['Heading3']))
+        elements.append(Image(charts['shift'], width=6*inch, height=3*inch))
+        elements.append(Spacer(1, 20))
     
-    # Quality Analysis Section
+    # Quality Analysis
     elements.append(PageBreak())
     elements.append(Paragraph("Quality Control Analysis", subtitle_style))
     
-    quality_intro = """
-    <para>
-    Our quality control systems continuously monitor production values to identify anomalies 
-    and ensure consistent output. The following analysis uses statistical methods (IQR-based 
-    outlier detection) to identify production values that deviate significantly from normal patterns.
-    </para>
-    """
-    elements.append(Paragraph(quality_intro, styles['Normal']))
-    elements.append(Spacer(1, 20))
-    
-    # Detailed Quality Table
-    quality_data = [['Material', 'Total Outliers', 'Normal Range (kg)', 'Outlier Rate (%)', 'Status', 'Action Required']]
+    quality_data = [['Material', 'Outliers', 'Normal Range (kg)', 'Status']]
     
     for material, info in outliers.items():
-        outlier_rate = (info['count'] / stats[material]['records']) * 100 if stats[material]['records'] > 0 else 0
-        
         if info['count'] == 0:
-            status = "✅ EXCELLENT"
-            action = "Continue monitoring"
-        elif info['count'] <= 2:
-            status = "🟡 ACCEPTABLE"
-            action = "Routine check"
-        elif info['count'] <= 5:
-            status = "🟠 ATTENTION"
-            action = "Review procedures"
+            status = "✅ GOOD"
+        elif info['count'] <= 3:
+            status = "⚠️ MONITOR"
         else:
-            status = "🔴 CRITICAL"
-            action = "Immediate investigation"
+            status = "🔴 ATTENTION"
         
         quality_data.append([
             material.replace('_', ' ').title(),
             str(info['count']),
             info['range'],
-            f"{outlier_rate:.1f}%",
-            status,
-            action
+            status
         ])
     
-    quality_table = Table(quality_data, colWidths=[1.3*inch, 0.8*inch, 1.2*inch, 0.8*inch, 1*inch, 1.4*inch])
+    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'),
-        ('FONTSIZE', (0, 0), (-1, 0), 10),
-        ('BOTTOMPADDING', (0, 0), (-1, 0), 12),
         ('GRID', (0, 0), (-1, -1), 1, colors.black),
-        ('ROWBACKGROUNDS', (0, 1), (-1, -1), [colors.white, colors.lightgrey]),
-        ('FONTSIZE', (0, 1), (-1, -1), 9),
+        ('ROWBACKGROUNDS', (0, 1), (-1, -1), [colors.white, colors.lightgrey])
     ]))
     
     elements.append(quality_table)
-    elements.append(Spacer(1, 30))
-    
-    # Statistical Analysis
-    elements.append(Paragraph("Statistical Performance Metrics", styles['Heading3']))
-    
-    # Calculate detailed statistics
-    daily_totals = df.groupby('date')['weight_kg'].sum()
-    stats_desc = daily_totals.describe()
-    
-    # Create statistics table
-    stats_data = [
-        ['Metric', 'Value (kg)', 'Interpretation'],
-        ['Mean Daily Production', f"{stats_desc['mean']:,.0f}", 'Average daily output'],
-        ['Median Daily Production', f"{stats_desc['50%']:,.0f}", 'Typical daily output'],
-        ['Standard Deviation', f"{stats_desc['std']:,.0f}", 'Production variability'],
-        ['Minimum Daily Output', f"{stats_desc['min']:,.0f}", 'Lowest single day'],
-        ['Maximum Daily Output', f"{stats_desc['max']:,.0f}", 'Highest single day'],
-        ['25th Percentile', f"{stats_desc['25%']:,.0f}", 'Lower quartile'],
-        ['75th Percentile', f"{stats_desc['75%']:,.0f}", 'Upper quartile'],
-    ]
-    
-    # Add coefficient of variation
-    cv = (stats_desc['std'] / stats_desc['mean']) * 100
-    stats_data.append(['Coefficient of Variation', f"{cv:.1f}%", 'Production consistency'])
-    
-    stats_table = Table(stats_data, colWidths=[2.5*inch, 1.5*inch, 2.5*inch])
-    stats_table.setStyle(TableStyle([
-        ('BACKGROUND', (0, 0), (-1, 0), colors.darkgreen),
-        ('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(stats_table)
-    elements.append(Spacer(1, 30))
-    
-    # Recommendations Section
-    elements.append(PageBreak())
-    elements.append(Paragraph("Recommendations & Action Items", subtitle_style))
-    
-    # Generate intelligent recommendations
-    recommendations = []
-    
-    # Check for high-variability materials
-    high_var_materials = [mat for mat, info in outliers.items() if info['count'] > 5]
-    if high_var_materials:
-        recommendations.append(f"🔧 <b>Equipment Review:</b> Materials {', '.join(high_var_materials)} show high variability. Consider equipment calibration.")
-    
-    # Check for low production days
-    if stats_desc['min'] < stats_desc['mean'] * 0.7:
-        recommendations.append(f"📊 <b>Process Optimization:</b> Minimum daily output ({stats_desc['min']:,.0f} kg) is significantly below average. Investigate bottlenecks.")
-    
-    # Check consistency
-    if cv > 20:
-        recommendations.append(f"⚖️ <b>Consistency Improvement:</b> High production variability (CV: {cv:.1f}%). Implement process standardization.")
-    else:
-        recommendations.append(f"✅ <b>Process Stability:</b> Good production consistency (CV: {cv:.1f}%). Maintain current procedures.")
-    
-    # Material-specific recommendations
-    top_material = max([k for k in stats.keys() if k != '_total_'], key=lambda x: stats[x]['total'])
-    recommendations.append(f"🎯 <b>Focus Area:</b> {top_material.replace('_', ' ').title()} is your primary material ({stats[top_material]['percentage']:.1f}% of production). Optimize this line for maximum impact.")
-    
-    for i, rec in enumerate(recommendations, 1):
-        elements.append(Paragraph(f"{i}. {rec}", styles['Normal']))
-        elements.append(Spacer(1, 10))
-    
-    # Footer
-    elements.append(Spacer(1, 50))
-    footer_text = """
-    <para alignment="center">
-    <i>This report was generated automatically by the Production Monitor system.<br/>
-    For questions or additional analysis, contact the Production Analytics team.</i>
-    </para>
-    """
-    elements.append(Paragraph(footer_text, styles['Normal']))
     
     # Build PDF
     doc.build(elements)
@@ -665,7 +534,6 @@ def create_enhanced_pdf_report(df, stats, outliers):
 
 def create_csv_export(df, stats):
     """Create CSV export of summary data"""
-    
     summary_df = pd.DataFrame([
         {
             'Material': material.replace('_', ' ').title(),
@@ -678,22 +546,10 @@ def create_csv_export(df, stats):
         for material, info in stats.items() if material != '_total_'
     ])
     
-    total_info = stats['_total_']
-    total_row = pd.DataFrame([{
-        'Material': 'TOTAL',
-        'Total_kg': total_info['total'],
-        'Percentage': 100.0,
-        'Daily_Average_kg': total_info['daily_avg'],
-        'Work_Days': total_info['work_days'],
-        'Records_Count': total_info['records']
-    }])
-    
-    summary_df = pd.concat([summary_df, total_row], ignore_index=True)
-    
     return summary_df
 
 def add_export_section(df, stats, outliers):
-    """Add export functionality to the main app"""
+    """Add export functionality"""
     st.markdown('<div class="section-header">📄 Export Reports</div>', unsafe_allow_html=True)
     
     col1, col2, col3 = st.columns(3)
@@ -701,7 +557,7 @@ def add_export_section(df, stats, outliers):
     with col1:
         if st.button("📊 Download PDF Report", type="primary"):
             try:
-                with st.spinner("Generating PDF..."):
+                with st.spinner("Generating PDF with charts..."):
                     pdf_buffer = create_enhanced_pdf_report(df, stats, outliers)
                     
                 st.download_button(
@@ -710,27 +566,22 @@ def add_export_section(df, stats, outliers):
                     file_name=f"production_report_{datetime.now().strftime('%Y%m%d')}.pdf",
                     mime="application/pdf"
                 )
-                st.success("PDF report ready for download!")
+                st.success("PDF report ready!")
                 
             except Exception as e:
                 st.error(f"PDF generation failed: {str(e)}")
     
     with col2:
         if st.button("📈 Download CSV Summary"):
-            try:
-                csv_data = create_csv_export(df, stats)
-                csv_string = csv_data.to_csv(index=False)
-                
-                st.download_button(
-                    label="💾 Download CSV",
-                    data=csv_string,
-                    file_name=f"production_summary_{datetime.now().strftime('%Y%m%d')}.csv",
-                    mime="text/csv"
-                )
-                st.success("CSV summary ready for download!")
-                
-            except Exception as e:
-                st.error(f"CSV generation failed: {str(e)}")
+            csv_data = create_csv_export(df, stats)
+            csv_string = csv_data.to_csv(index=False)
+            
+            st.download_button(
+                label="💾 Download CSV",
+                data=csv_string,
+                file_name=f"production_summary_{datetime.now().strftime('%Y%m%d')}.csv",
+                mime="text/csv"
+            )
     
     with col3:
         if st.button("📋 Download Raw Data"):
@@ -739,14 +590,14 @@ def add_export_section(df, stats, outliers):
             st.download_button(
                 label="💾 Download Raw CSV",
                 data=csv_string,
-                file_name=f"raw_production_data_{datetime.now().strftime('%Y%m%d')}.csv",
+                file_name=f"raw_data_{datetime.now().strftime('%Y%m%d')}.csv",
                 mime="text/csv"
             )
 
 def main():
     load_css()
     
-    # Modern header
+    # Header
     st.markdown("""
     <div class="main-header">
         <div class="main-title">🏭 Production Monitor</div>
@@ -764,7 +615,7 @@ def main():
         st.markdown("""
         **Expected TSV format:**
         - `date`: MM/DD/YYYY
-        - `weight_kg`: Production weight in kg
+        - `weight_kg`: Production weight
         - `material_type`: Material category
         - `shift`: day/night (optional)
         """)
@@ -890,7 +741,7 @@ def main():
                     st.write(f"**A:** {answer}")
     
     else:
-        # Platform usage guide
+        # Usage guide
         st.markdown('<div class="section-header">📖 How to Use This Platform</div>', unsafe_allow_html=True)
         
         col1, col2 = st.columns(2)