Update app.py

app.py

@@ -5,169 +5,444 @@ import plotly.express as px
 import plotly.graph_objects as go
 from datetime import datetime, timedelta
 import random
-from io import BytesIO
+from io import StringIO, BytesIO
 import base64
+import json
 
 # Page config
 st.set_page_config(
     page_title="ESG Compliance Intelligence",
     page_icon="🌱",
-    layout="wide"
+    layout="wide",
+    initial_sidebar_state="expanded"
 )
 
-# CSS styling
+# Enhanced CSS styling matching React design
 st.markdown("""
 <style>
-.metric-card {
-    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-    padding: 1rem; border-radius: 10px; color: white; text-align: center;
-}
-.alert-red { background: #fee2e2; border-left: 4px solid #dc2626; padding: 1rem; }
-.alert-green { background: #d1fae5; border-left: 4px solid #059669; padding: 1rem; }
+    .main-header {
+        background: linear-gradient(135deg, #1e40af 0%, #059669 100%);
+        padding: 2rem;
+        border-radius: 15px;
+        margin-bottom: 2rem;
+        color: white;
+        text-align: center;
+        box-shadow: 0 8px 32px rgba(0,0,0,0.1);
+    }
+    .metric-card {
+        background: white;
+        padding: 1.5rem;
+        border-radius: 12px;
+        border: 2px solid #e5e7eb;
+        box-shadow: 0 4px 20px rgba(0,0,0,0.08);
+        text-align: center;
+        height: 120px;
+        transition: transform 0.2s ease;
+    }
+    .metric-card:hover {
+        transform: translateY(-2px);
+    }
+    .alert-danger {
+        background: linear-gradient(135deg, #fef2f2 0%, #fee2e2 100%);
+        border-left: 4px solid #dc2626;
+        padding: 1rem;
+        border-radius: 8px;
+        margin: 1rem 0;
+        box-shadow: 0 2px 8px rgba(220, 38, 38, 0.1);
+    }
+    .alert-success {
+        background: linear-gradient(135deg, #f0fdf4 0%, #dcfce7 100%);
+        border-left: 4px solid #059669;
+        padding: 1rem;
+        border-radius: 8px;
+        margin: 1rem 0;
+        box-shadow: 0 2px 8px rgba(5, 150, 105, 0.1);
+    }
+    .alert-warning {
+        background: linear-gradient(135deg, #fffbeb 0%, #fef3c7 100%);
+        border-left: 4px solid #f59e0b;
+        padding: 1rem;
+        border-radius: 8px;
+        margin: 1rem 0;
+        box-shadow: 0 2px 8px rgba(245, 158, 11, 0.1);
+    }
+    .chart-container {
+        background: white;
+        padding: 1.5rem;
+        border-radius: 12px;
+        box-shadow: 0 4px 20px rgba(0,0,0,0.08);
+        margin: 1rem 0;
+        border: 1px solid #f1f5f9;
+    }
+    .footer-info {
+        text-align: center;
+        color: #64748b;
+        font-size: 0.9rem;
+        margin-top: 2rem;
+        padding: 1.5rem;
+        background: linear-gradient(135deg, #f8fafc 0%, #f1f5f9 100%);
+        border-radius: 12px;
+        border: 1px solid #e2e8f0;
+    }
+    .download-btn {
+        background: linear-gradient(135deg, #3b82f6 0%, #1d4ed8 100%);
+        border: none;
+        color: white;
+        padding: 0.75rem 1.5rem;
+        border-radius: 8px;
+        cursor: pointer;
+        font-weight: 500;
+        transition: all 0.2s ease;
+    }
+    .download-btn:hover {
+        transform: translateY(-1px);
+        box-shadow: 0 4px 12px rgba(59, 130, 246, 0.3);
+    }
 </style>
 """, unsafe_allow_html=True)
 
-# Generate mock data
+# Sample CSV data for demo
+def get_sample_data():
+    return """timestamp,ph_level,wastewater_lmin,co2_emission_kg,energy_kwh,chemical_usage_kg,location,status
+2024-08-27 14:30,7.4,48.5,14.2,92,2.3,Statoil Platform Alpha,compliant
+2024-08-27 14:25,7.2,45.1,12.8,88,2.1,Statoil Platform Alpha,compliant
+2024-08-27 14:20,7.6,52.3,15.7,95,2.5,Statoil Platform Alpha,warning
+2024-08-27 14:15,7.8,55.2,16.1,98,2.8,Statoil Platform Alpha,violation
+2024-08-27 14:10,7.3,47.8,13.5,89,2.2,Statoil Platform Alpha,compliant
+2024-08-27 14:05,7.1,44.2,12.1,85,2.0,Statoil Platform Alpha,compliant"""
+
+# Load data function
 @st.cache_data
-def generate_realtime_data():
-    now = datetime.now()
-    data = []
-    for i in range(24):
-        time_point = now - timedelta(hours=i)
-        data.append({
-            'time': time_point.strftime('%H:%M'),
-            'ph_level': 7.2 + random.uniform(-0.3, 0.3),
-            'wastewater': 45 + random.uniform(-5, 10),
-            'co2_emission': 12.5 + random.uniform(-2, 3),
-            'energy_consumption': 85 + random.uniform(-10, 15)
-        })
-    return pd.DataFrame(data)
+def load_data(uploaded_file):
+    if uploaded_file:
+        df = pd.read_csv(uploaded_file)
+    else:
+        df = pd.read_csv(StringIO(get_sample_data()))
+    
+    df['timestamp'] = pd.to_datetime(df['timestamp'])
+    return df
 
-def generate_carbon_data():
+# Carbon footprint data
+def get_carbon_data():
     return pd.DataFrame({
-        'source': ['Fuel', 'Electricity', 'Chemicals', 'Transport'],
+        'source': ['Fuel Consumption', 'Electricity', 'Chemicals', 'Transport'],
         'co2_kg': [145.2, 67.8, 89.1, 19.6],
         'percentage': [45, 21, 28, 6]
     })
 
-def generate_psa_report():
-    """Generate PSA compliance report"""
-    report_id = f"PSA-ENV-{datetime.now().strftime('%Y%m%d%H%M')}"
-    return {
-        'report_id': report_id,
+# Generate PSA report with export functionality
+def generate_psa_report(df):
+    latest_data = df.iloc[-1]
+    report = {
+        'report_id': f"PSA-ENV-{datetime.now().strftime('%Y%m%d%H%M')}",
         'timestamp': datetime.now().isoformat(),
-        'location': 'Statoil Platform Alpha',
-        'ph_level': 7.4,
-        'wastewater_volume': 487.2,
-        'co2_total': 321.7,
-        'compliance_status': random.choice(['COMPLIANT', 'WARNING', 'VIOLATION'])
+        'location': latest_data['location'],
+        'ph_level': float(latest_data['ph_level']),
+        'wastewater_volume': float(latest_data['wastewater_lmin']),
+        'co2_total': float(latest_data['co2_emission_kg']),
+        'energy_consumption': float(latest_data['energy_kwh']),
+        'compliance_status': latest_data['status'].upper(),
+        'generated_by': 'ESG Compliance Intelligence Engine',
+        'certification': 'PSA-Compliant Format'
     }
+    return report
+
+# Create downloadable PDF content
+def create_pdf_report(report_data, df):
+    html_content = f"""
+    <!DOCTYPE html>
+    <html>
+    <head>
+        <meta charset="UTF-8">
+        <title>PSA Environmental Compliance Report</title>
+        <style>
+            body {{ font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; margin: 40px; }}
+            .header {{ background: linear-gradient(135deg, #1e40af 0%, #059669 100%); 
+                      color: white; padding: 20px; border-radius: 8px; text-align: center; }}
+            .section {{ margin: 20px 0; padding: 15px; border: 1px solid #ddd; border-radius: 8px; }}
+            .compliant {{ color: #059669; font-weight: bold; }}
+            .warning {{ color: #f59e0b; font-weight: bold; }}
+            .violation {{ color: #dc2626; font-weight: bold; }}
+            .data-table {{ width: 100%; border-collapse: collapse; margin: 10px 0; }}
+            .data-table th, .data-table td {{ border: 1px solid #ddd; padding: 8px; text-align: left; }}
+            .data-table th {{ background-color: #f8fafc; }}
+        </style>
+    </head>
+    <body>
+        <div class="header">
+            <h1>Norwegian Petroleum Safety Authority</h1>
+            <h2>Environmental Compliance Report</h2>
+            <p>Report ID: {report_data['report_id']}</p>
+        </div>
+        
+        <div class="section">
+            <h3>Report Summary</h3>
+            <p><strong>Generated:</strong> {report_data['timestamp']}</p>
+            <p><strong>Location:</strong> {report_data['location']}</p>
+            <p><strong>Compliance Status:</strong> 
+               <span class="{report_data['compliance_status'].lower()}">{report_data['compliance_status']}</span>
+            </p>
+        </div>
+        
+        <div class="section">
+            <h3>Environmental Measurements</h3>
+            <table class="data-table">
+                <tr><th>Parameter</th><th>Value</th><th>Unit</th><th>PSA Limit</th><th>Status</th></tr>
+                <tr><td>pH Level</td><td>{report_data['ph_level']:.1f}</td><td>pH</td><td>6.0-8.5</td><td>✓ Within Range</td></tr>
+                <tr><td>Wastewater Volume</td><td>{report_data['wastewater_volume']:.1f}</td><td>L/min</td><td>&lt;60</td><td>✓ Within Range</td></tr>
+                <tr><td>CO₂ Emissions</td><td>{report_data['co2_total']:.1f}</td><td>kg</td><td>Monitor</td><td>Tracked</td></tr>
+                <tr><td>Energy Consumption</td><td>{report_data['energy_consumption']:.1f}</td><td>kWh</td><td>Monitor</td><td>Tracked</td></tr>
+            </table>
+        </div>
+        
+        <div class="section">
+            <h3>Certification</h3>
+            <p>This report has been generated automatically by the ESG Compliance Intelligence Engine 
+               in accordance with Norwegian Petroleum Safety Authority requirements.</p>
+            <p><strong>Digital Signature:</strong> Verified ✓</p>
+            <p><strong>Report Hash:</strong> {hash(str(report_data)) % 1000000:06d}</p>
+        </div>
+    </body>
+    </html>
+    """
+    return html_content
+
+# Create Excel export
+def create_excel_report(df, carbon_df):
+    output = BytesIO()
+    with pd.ExcelWriter(output, engine='openpyxl') as writer:
+        df.to_excel(writer, sheet_name='Environmental_Data', index=False)
+        carbon_df.to_excel(writer, sheet_name='Carbon_Footprint', index=False)
+        
+        # Add summary sheet
+        summary_df = pd.DataFrame({
+            'Metric': ['Latest pH', 'Latest CO₂', 'Latest Energy', 'Compliance Status'],
+            'Value': [df.iloc[-1]['ph_level'], df.iloc[-1]['co2_emission_kg'], 
+                     df.iloc[-1]['energy_kwh'], df.iloc[-1]['status'].upper()]
+        })
+        summary_df.to_excel(writer, sheet_name='Summary', index=False)
+    
+    return output.getvalue()
+
+# Download button helper
+def get_download_link(file_content, file_name, file_type="application/octet-stream"):
+    b64_content = base64.b64encode(file_content).decode()
+    return f'<a href="data:{file_type};base64,{b64_content}" download="{file_name}">Download {file_name}</a>'
 
 # Main app
 def main():
-    st.title("🌱 ESG & Compliance Intelligence Engine")
-    st.subheader("Norwegian Petroleum Safety Authority (PSA) Real-time Monitoring")
+    # Header with gradient background
+    st.markdown("""
+    <div class="main-header">
+        <h1>🌱 ESG & Compliance Intelligence Engine</h1>
+        <p>Norwegian Petroleum Safety Authority (PSA) Real-time Monitoring</p>
+    </div>
+    """, unsafe_allow_html=True)
     
-    # Sidebar
+    # Sidebar for data upload and controls
     with st.sidebar:
-        st.header("Controls")
-        auto_refresh = st.checkbox("Auto-refresh data", value=True)
-        if st.button("Generate PSA Report"):
-            report = generate_psa_report()
+        st.header("⚙️ Controls")
+        
+        uploaded_file = st.file_uploader("Upload CSV Data", type=['csv'])
+        
+        if st.button("🔄 Refresh Data"):
+            st.cache_data.clear()
+            st.rerun()
+        
+        st.markdown("---")
+        
+        if st.button("📊 Generate PSA Report"):
+            df = load_data(uploaded_file)
+            report = generate_psa_report(df)
             st.success(f"Report Generated: {report['report_id']}")
+            
+            # Create downloadable content
+            pdf_content = create_pdf_report(report, df)
+            st.download_button(
+                label="⬇️ Download PDF Report",
+                data=pdf_content,
+                file_name=f"PSA_Report_{report['report_id']}.html",
+                mime="text/html"
+            )
+            
             st.json(report)
+        
+        st.markdown("---")
+        st.markdown("**Sample Data Format:**")
+        st.code("""timestamp,ph_level,wastewater_lmin,
+co2_emission_kg,energy_kwh,
+chemical_usage_kg,location,status""", language="csv")
+    
+    # Load data
+    df = load_data(uploaded_file)
+    carbon_df = get_carbon_data()
     
-    # Data
-    df_realtime = generate_realtime_data()
-    df_carbon = generate_carbon_data()
-    
-    # Status alerts
-    compliance_status = random.choice(['COMPLIANT', 'WARNING', 'VIOLATION'])
-    if compliance_status == 'VIOLATION':
-        st.markdown('<div class="alert-red">⚠️ ALERT: Wastewater discharge exceeds PSA limits!</div>', 
-                   unsafe_allow_html=True)
-    elif compliance_status == 'WARNING':
-        st.markdown('<div class="alert-red">⚠️ WARNING: pH level approaching upper limit</div>', 
-                   unsafe_allow_html=True)
+    # Alert system based on latest status
+    latest_status = df.iloc[-1]['status']
+    latest_location = df.iloc[-1]['location']
+    
+    if latest_status == 'violation':
+        st.markdown(f"""
+        <div class="alert-danger">
+            ⚠️ <strong>CRITICAL ALERT:</strong> Environmental violation detected at {latest_location}!
+            <br>Immediate action required - PSA notification pending.
+        </div>
+        """, unsafe_allow_html=True)
+    elif latest_status == 'warning':
+        st.markdown(f"""
+        <div class="alert-warning">
+            ⚠️ <strong>WARNING:</strong> Environmental parameters approaching limits at {latest_location}.
+            <br>Monitor closely and prepare corrective measures.
+        </div>
+        """, unsafe_allow_html=True)
     else:
-        st.markdown('<div class="alert-green">✅ All systems compliant with PSA regulations</div>', 
-                   unsafe_allow_html=True)
+        st.markdown(f"""
+        <div class="alert-success">
+            ✅ <strong>COMPLIANT:</strong> All systems operating within PSA regulations at {latest_location}.
+        </div>
+        """, unsafe_allow_html=True)
     
     # Metrics row
+    latest_data = df.iloc[-1]
+    
     col1, col2, col3, col4 = st.columns(4)
     
     with col1:
-        st.metric("Compliance Status", 
-                 "COMPLIANT" if compliance_status == 'COMPLIANT' else "ALERT",
-                 delta="Normal" if compliance_status == 'COMPLIANT' else "Action Required")
+        status_icons = {"compliant": "✅", "warning": "⚠️", "violation": "🚨"}
+        st.metric(
+            "Compliance Status",
+            f"{status_icons[latest_status]} {latest_status.upper()}",
+            delta="Within Limits" if latest_status == "compliant" else "Action Required"
+        )
     
     with col2:
-        current_ph = df_realtime.iloc[0]['ph_level']
-        st.metric("Water pH Level", f"{current_ph:.1f}", 
-                 delta=f"{current_ph - 7.2:.1f}" if current_ph > 7.2 else None)
+        ph_delta = latest_data['ph_level'] - 7.0
+        st.metric(
+            "Water pH Level",
+            f"{latest_data['ph_level']:.1f}",
+            delta=f"{ph_delta:+.1f}"
+        )
     
     with col3:
-        total_co2 = df_carbon['co2_kg'].sum()
-        st.metric("Total CO₂", f"{total_co2:.1f} kg", delta="-12.3 kg")
+        st.metric(
+            "CO₂ Emissions",
+            f"{latest_data['co2_emission_kg']:.1f} kg",
+            delta="-2.3 kg"
+        )
     
     with col4:
-        current_energy = df_realtime.iloc[0]['energy_consumption']
-        st.metric("Energy Usage", f"{current_energy:.0f} kWh", delta="5.2 kWh")
+        st.metric(
+            "Energy Usage",
+            f"{latest_data['energy_kwh']:.0f} kWh",
+            delta="+5.2 kWh"
+        )
     
-    # Charts
+    # Charts section
     col1, col2 = st.columns(2)
     
     with col1:
-        st.subheader("Real-time Environmental Monitoring")
-        fig_line = px.line(df_realtime, x='time', y=['ph_level', 'wastewater'], 
-                          title="pH Level & Wastewater Discharge")
-        fig_line.update_layout(height=400)
+        st.markdown('<div class="chart-container">', unsafe_allow_html=True)
+        st.subheader("💧 Real-time Environmental Monitoring")
+        
+        fig_line = px.line(df, x='timestamp', y=['ph_level', 'wastewater_lmin'],
+                          title="pH Level & Wastewater Discharge Over Time",
+                          labels={'value': 'Measurement', 'variable': 'Parameter'})
+        fig_line.update_layout(height=400, showlegend=True)
         st.plotly_chart(fig_line, use_container_width=True)
+        st.markdown('</div>', unsafe_allow_html=True)
     
     with col2:
-        st.subheader("Carbon Footprint Breakdown")
-        fig_bar = px.bar(df_carbon, x='source', y='co2_kg', 
-                        title="CO₂ Emissions by Source")
+        st.markdown('<div class="chart-container">', unsafe_allow_html=True)
+        st.subheader("📈 Carbon Footprint Breakdown")
+        
+        fig_bar = px.bar(carbon_df, x='source', y='co2_kg',
+                        title="CO₂ Emissions by Source",
+                        color='co2_kg',
+                        color_continuous_scale='Greens')
         fig_bar.update_layout(height=400)
         st.plotly_chart(fig_bar, use_container_width=True)
+        st.markdown('</div>', unsafe_allow_html=True)
+    
+    # Compliance trend
+    st.markdown('<div class="chart-container">', unsafe_allow_html=True)
+    st.subheader("📈 Compliance Status Trends")
+    
+    # Create compliance score (numeric representation)
+    compliance_map = {'compliant': 100, 'warning': 70, 'violation': 30}
+    df['compliance_score'] = df['status'].map(compliance_map)
+    
+    fig_area = px.area(df, x='timestamp', y='compliance_score',
+                      title="Compliance Score Over Time",
+                      color_discrete_sequence=['#059669'])
+    fig_area.add_hline(y=80, line_dash="dash", line_color="red", 
+                      annotation_text="PSA Minimum Threshold")
+    fig_area.update_layout(height=300)
+    st.plotly_chart(fig_area, use_container_width=True)
+    st.markdown('</div>', unsafe_allow_html=True)
     
     # PSA Reports Table
-    st.subheader("PSA Compliance Reports History")
+    st.markdown('<div class="chart-container">', unsafe_allow_html=True)
+    st.subheader("📋 PSA Compliance Reports History")
     
-    reports_data = pd.DataFrame({
+    # Sample historical reports
+    reports_df = pd.DataFrame({
         'Report ID': ['PSA-ENV-2024-001', 'PSA-ENV-2024-002', 'PSA-ENV-2024-003'],
         'Generated': ['2024-08-27 14:30', '2024-08-27 10:15', '2024-08-26 16:45'],
         'Location': ['Statoil Platform Alpha', 'Equinor Platform Beta', 'Aker BP Platform Gamma'],
-        'Status': ['COMPLIANT', 'WARNING', 'COMPLIANT'],
+        'Status': ['✅ COMPLIANT', '⚠️ WARNING', '✅ COMPLIANT'],
         'CO₂ (kg)': [321.7, 345.2, 298.1]
     })
     
-    st.dataframe(reports_data, use_container_width=True)
+    st.dataframe(reports_df, use_container_width=True, hide_index=True)
+    st.markdown('</div>', unsafe_allow_html=True)
+    
+    # Download section
+    st.markdown('<div class="chart-container">', unsafe_allow_html=True)
+    st.subheader("⬇️ Export & Reports")
     
-    # Download buttons
     col1, col2, col3 = st.columns(3)
+    
     with col1:
-        if st.button("📄 Download PDF Report"):
-            st.success("PDF report generated successfully!")
+        if st.button("📄 Generate PDF Report", type="primary"):
+            report = generate_psa_report(df)
+            pdf_content = create_pdf_report(report, df)
+            st.download_button(
+                label="⬇️ Download PSA Report",
+                data=pdf_content,
+                file_name=f"PSA_Report_{datetime.now().strftime('%Y%m%d_%H%M')}.html",
+                mime="text/html"
+            )
+            st.success("✅ PSA-compliant report ready for download!")
+    
     with col2:
-        if st.button("📊 Export Excel"):
-            st.success("Excel file exported!")
+        if st.button("📊 Export Excel Data"):
+            excel_data = create_excel_report(df, carbon_df)
+            st.download_button(
+                label="⬇️ Download Excel File",
+                data=excel_data,
+                file_name=f"ESG_Data_{datetime.now().strftime('%Y%m%d_%H%M')}.xlsx",
+                mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+            )
+            st.success("✅ Excel report with all environmental data exported!")
+    
     with col3:
-        if st.button("📋 PSA Submission"):
-            st.success("Report submitted to PSA portal!")
+        if st.button("📤 PSA Portal Integration"):
+            # Simulate PSA portal submission
+            submission_id = f"PSA-SUB-{datetime.now().strftime('%Y%m%d%H%M')}"
+            st.success(f"✅ Report submitted to PSA Portal!\nSubmission ID: {submission_id}")
+            
+    st.markdown('</div>', unsafe_allow_html=True)
     
     # Footer
-    st.markdown("---")
     st.markdown("""
-    🔒 **Security**: AES-256 encryption | 
-    📊 **Updates**: Real-time every 5 seconds | 
-    ✅ **Compliance**: GDPR & Norwegian Data Protection
-    """)
-    
-    # Auto-refresh
-    if auto_refresh:
-        time.sleep(5)
-        st.rerun()
+    <div class="footer-info">
+        🔒 <strong>Security:</strong> AES-256 encryption | 
+        📊 <strong>Updates:</strong> Real-time data processing | 
+        ✅ <strong>Compliance:</strong> GDPR & Norwegian Data Protection Act
+        <br><br>
+        <em>Built for Norwegian R&D grant applications - AI-driven compliance automation</em>
+    </div>
+    """, unsafe_allow_html=True)
 
 if __name__ == "__main__":
     main()