Update app.py

app.py

@@ -7,882 +7,713 @@ import numpy as np
 from datetime import datetime
 import io
 
-# Page config
+# Page configuration
 st.set_page_config(
     page_title="FinanceGPT Analyzer",
     page_icon="📊",
-    layout="wide",
-    initial_sidebar_state="expanded"
+    layout="wide"
 )
 
-# Custom CSS
+# Custom CSS for better styling
 st.markdown("""
 <style>
     .metric-card {
-        background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
+        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
         padding: 1rem;
         border-radius: 10px;
         color: white;
         text-align: center;
-        margin: 0.5rem 0;
     }
     .insight-box {
         background: #f8f9fa;
-        border-left: 4px solid #007bff;
+        border-left: 4px solid #28a745;
         padding: 1rem;
         margin: 1rem 0;
-        border-radius: 5px;
     }
-    .risk-high { border-left-color: #dc3545; }
-    .risk-medium { border-left-color: #ffc107; }
-    .risk-low { border-left-color: #28a745; }
-    .stTab {
-        background-color: white;
-    }
-    .chat-message {
+    .risk-alert {
+        background: #fff3cd;
+        border-left: 4px solid #ffc107;
         padding: 1rem;
-        margin: 0.5rem 0;
-        border-radius: 10px;
-        background-color: #f0f2f6;
+        margin: 1rem 0;
     }
 </style>
 """, unsafe_allow_html=True)
 
-# Sample data (real Norwegian company data)
-@st.cache_data
-def get_sample_data():
-    data = {
-        'Year': [2024] * 70,
-        'Statement_Type': (['Income Statement'] * 20 + ['Balance Sheet'] * 46 + ['Additional Info'] * 1 + ['Cash Flow Statement'] * 23),
-        'Account_Name_Norwegian': [
-            'Salgsinntekt', 'Sum inntekter', 'Varekostnad', 'Lønnskostnad', 'Avskrivning på varige driftsmidler og immaterielle eiendeler',
-            'Annen driftskostnad', 'Sum kostnader', 'Driftsresultat', 'Annen renteinntekt', 'Annen finansinntekt',
-            'Sum finansinntekter', 'Annen rentekostnad', 'Annen finanskostnad', 'Sum finanskostnader', 'Netto finans',
-            'Resultat før skattekostnad', 'Skattekostnad', 'Årsresultat', 'Overføringer til/fra annen egenkapital', 'Sum overføringer og disponeringer'
-        ] + [
-            'Tomter bygninger og annen fast eiendom', 'Maskiner og anlegg', 'Driftsløsøre inventar verktøy kontormaskiner og lignende',
-            'Sum varige driftsmidler', 'Andre fordringer', 'Sum finansielle anleggsmidler', 'Sum anleggsmidler',
-            'Kundefordringer', 'Andre fordringer', 'Sum fordringer', 'Bankinnskudd kontanter og lignende',
-            'Sum bankinnskudd kontanter og lignende', 'Sum omløpsmidler', 'SUM EIENDELER', 'Selskapskapital',
-            'Sum innskutt egenkapital', 'Annen egenkapital', 'Sum opptjent egenkapital', 'Sum egenkapital',
-            'Utsatt skatt', 'Sum avsetninger for forpliktelser', 'Gjeld til kredittinstitusjoner', 'Øvrig langsiktig gjeld',
-            'Sum annen langsiktig gjeld', 'Sum langsiktig gjeld', 'Leverandørgjeld', 'Betalbar skatt',
-            'Skyldige offentlige avgifter', 'Annen kortsiktig gjeld', 'Sum kortsiktig gjeld', 'Sum gjeld',
-            'SUM EGENKAPITAL OG GJELD'
-        ] + ['Antall årsverk i regnskapsåret'] + [
-            'Årsresultat', 'Avskrivninger', 'Endring i utsatt skatt', 'Endring i kundefordringer', 'Endring i andre fordringer',
-            'Endring i leverandørgjeld', 'Endring i betalbar skatt', 'Endring i skyldige offentlige avgifter', 'Endring i annen kortsiktig gjeld',
-            'Netto kontantstrøm fra driftsaktiviteter', 'Kjøp av varige driftsmidler', 'Endring i finansielle anleggsmidler',
-            'Netto kontantstrøm fra investeringsaktiviteter', 'Endring i langsiktig gjeld til kredittinstitusjoner', 'Endring i øvrig langsiktig gjeld',
-            'Netto kontantstrøm fra finansieringsaktiviteter', 'NETTO ENDRING I KONTANTER', 'Kontanter ved periodens begynnelse',
-            'Kontanter ved periodens slutt', 'Kontroll mot balanse', 'Differanse'
-        ],
-        'Account_Name_English': [
-            'Sales Revenue', 'Total Income', 'Cost of Goods Sold', 'Payroll Expenses', 'Depreciation on Fixed Assets and Intangible Assets',
-            'Other Operating Expenses', 'Total Expenses', 'Operating Result', 'Other Interest Income', 'Other Financial Income',
-            'Total Financial Income', 'Other Interest Expenses', 'Other Financial Expenses', 'Total Financial Expenses', 'Net Financial Result',
-            'Result Before Tax', 'Tax Expense', 'Annual Result', 'Transfers to/from Other Equity', 'Total Transfers and Allocations'
-        ] + [
-            'Land Buildings and Other Real Estate', 'Machinery and Equipment', 'Operating Equipment Inventory Tools Office Machines etc',
-            'Total Fixed Assets', 'Other Receivables', 'Total Financial Fixed Assets', 'Total Fixed Assets',
-            'Customer Receivables', 'Other Receivables', 'Total Receivables', 'Bank Deposits Cash and Similar',
-            'Total Bank Deposits Cash and Similar', 'Total Current Assets', 'TOTAL ASSETS', 'Share Capital',
-            'Total Paid-in Capital', 'Other Equity', 'Total Retained Earnings', 'Total Equity',
-            'Deferred Tax', 'Total Provisions for Liabilities', 'Debt to Credit Institutions', 'Other Long-term Debt',
-            'Total Other Long-term Debt', 'Total Long-term Debt', 'Accounts Payable', 'Payable Tax',
-            'Public Charges Payable', 'Other Short-term Debt', 'Total Short-term Debt', 'Total Debt',
-            'TOTAL EQUITY AND DEBT'
-        ] + ['Number of Full-time Employees'] + [
-            'Net Income', 'Depreciation', 'Change in Deferred Tax', 'Change in Customer Receivables', 'Change in Other Receivables',
-            'Change in Accounts Payable', 'Change in Payable Tax', 'Change in Public Charges Payable', 'Change in Other Short-term Debt',
-            'Net Cash Flow from Operating Activities', 'Purchase of Fixed Assets', 'Change in Financial Fixed Assets',
-            'Net Cash Flow from Investing Activities', 'Change in Long-term Debt to Credit Institutions', 'Change in Other Long-term Debt',
-            'Net Cash Flow from Financing Activities', 'NET CHANGE IN CASH', 'Cash at Beginning of Period',
-            'Cash at End of Period', 'Control vs Balance Sheet', 'Difference'
-        ],
-        '2024_Amount_NOK': [
-            25107008, 25107008, 9880032, 3700289, 316180, 4355621, 18252121, 6854887, 11439, 1230,
-            12669, 51288, 3916, 55205, -42536, 6812351, 1498717, 5313634, 5313634, 5313634,
-            394022, 928281, 1010616, 2332919, 1949477, 1949477, 4282396, 5575707, 178797, 5754504,
-            1595549, 1595549, 7350053, 11632449, 30000, 30000, 5572404, 5572404, 5602404,
-            89409, 89409, 364050, 200000, 564050, 653459, 3162252, 1421220, 526789, 266325,
-            5376586, 6030045, 11632449, 7, 5313634, 316180, 60522, -3575556, 54597,
-            1860815, 1438195, 17187, -1099762, 3385812, -1885519, -1660609, -3546128, -37736, 0,
-            -37736, 801948, 793599, 1595549, 1595549, 0
-        ],
-        '2023_Amount_NOK': [
-            4891891, 4891891, 770840, 2703253, 0, 1330101, 4804194, 87697, 385, 0,
-            385, 59498, 0, 59498, -59113, 28584, 32524, -3940, -3940, -3940,
-            0, 334817, 428763, 763580, 288868, 288868, 1052447, 2000151, 233394, 2233546,
-            793599, 793599, 3027145, 4079592, 30000, 30000, 258770, 258770, 288770,
-            28887, 28887, 401786, 200000, 601786, 630673, 1301437, -16975, 509602, 1366087,
-            3160150, 3790823, 4079592, 0, -3940, 0, 28887, -2000151, -233394,
-            1301437, -16975, 509602, 1366087, 951553, -763580, -288868, -1052448, 401786, 200000,
-            601786, 500891, 292708, 793599, 793599, 0
-        ]
-    }
-    return pd.DataFrame(data)
-
-# Data processing functions
-def get_account_value(df, english_name, year='2024'):
-    column = f'{year}_Amount_NOK'
-    try:
-        result = df[df['Account_Name_English'] == english_name][column]
-        return result.iloc[0] if len(result) > 0 else 0
-    except:
-        return 0
-
-def calculate_ratios(df):
-    ratios = {}
-    
-    # Get key financial figures
-    revenue = get_account_value(df, 'Sales Revenue', '2024')
-    revenue_prev = get_account_value(df, 'Sales Revenue', '2023')
-    net_income = get_account_value(df, 'Annual Result', '2024')
-    net_income_prev = get_account_value(df, 'Annual Result', '2023')
-    total_assets = get_account_value(df, 'TOTAL ASSETS', '2024')
-    total_assets_prev = get_account_value(df, 'TOTAL ASSETS', '2023')
-    current_assets = get_account_value(df, 'Total Current Assets', '2024')
-    current_liabilities = get_account_value(df, 'Total Short-term Debt', '2024')
-    total_debt = get_account_value(df, 'Total Debt', '2024')
-    total_equity = get_account_value(df, 'Total Equity', '2024')
-    cogs = get_account_value(df, 'Cost of Goods Sold', '2024')
-    operating_result = get_account_value(df, 'Operating Result', '2024')
-    
-    # Calculate ratios with error handling
-    ratios['Revenue Growth'] = ((revenue - revenue_prev) / revenue_prev * 100) if revenue_prev != 0 else 0
-    ratios['Net Margin'] = (net_income / revenue * 100) if revenue != 0 else 0
-    ratios['Operating Margin'] = (operating_result / revenue * 100) if revenue != 0 else 0
-    ratios['ROA'] = (net_income / total_assets * 100) if total_assets != 0 else 0
-    ratios['ROE'] = (net_income / total_equity * 100) if total_equity != 0 else 0
-    ratios['Current Ratio'] = (current_assets / current_liabilities) if current_liabilities != 0 else 0
-    ratios['Debt to Equity'] = (total_debt / total_equity) if total_equity != 0 else 0
-    ratios['Debt Ratio'] = (total_debt / total_assets * 100) if total_assets != 0 else 0
-    ratios['Gross Margin'] = ((revenue - cogs) / revenue * 100) if revenue != 0 else 0
-    ratios['Asset Turnover'] = (revenue / total_assets) if total_assets != 0 else 0
-    
-    return ratios
-
-def get_ratio_grade(ratio_name, value):
-    grades = {
-        'Revenue Growth': {'A': 20, 'B': 10, 'C': 5, 'D': 0},
-        'Net Margin': {'A': 15, 'B': 10, 'C': 5, 'D': 0},
-        'Operating Margin': {'A': 20, 'B': 15, 'C': 10, 'D': 5},
-        'ROA': {'A': 15, 'B': 10, 'C': 5, 'D': 0},
-        'ROE': {'A': 20, 'B': 15, 'C': 10, 'D': 0},
-        'Current Ratio': {'A': 2.0, 'B': 1.5, 'C': 1.2, 'D': 1.0},
-        'Debt Ratio': {'A': 30, 'B': 50, 'C': 70, 'D': 100},
-        'Debt to Equity': {'A': 0.5, 'B': 1.0, 'C': 1.5, 'D': 2.0},
-        'Gross Margin': {'A': 50, 'B': 30, 'C': 20, 'D': 10}
-    }
-    
-    if ratio_name not in grades:
-        return 'N/A'
-    
-    thresholds = grades[ratio_name]
-    if ratio_name in ['Debt Ratio', 'Debt to Equity']:  # Lower is better
-        if value <= thresholds['A']: return 'A'
-        elif value <= thresholds['B']: return 'B'
-        elif value <= thresholds['C']: return 'C'
-        else: return 'D'
-    else:  # Higher is better
-        if value >= thresholds['A']: return 'A'
-        elif value >= thresholds['B']: return 'B'
-        elif value >= thresholds['C']: return 'C'
-        else: return 'D'
-
-def generate_insights(df, ratios):
-    insights = []
-    
-    revenue_growth = ratios['Revenue Growth']
-    if revenue_growth > 400:
-        insights.append(("🚀 Explosive Growth", f"Revenue grew {revenue_growth:.0f}% - exceptional performance!", "success"))
-    elif revenue_growth > 50:
-        insights.append(("📈 Strong Growth", f"Revenue increased {revenue_growth:.1f}% year-over-year", "success"))
-    
-    if ratios['Current Ratio'] < 1.0:
-        insights.append(("⚠️ Liquidity Risk", "Current ratio below 1.0 indicates potential cash flow issues", "warning"))
-    elif ratios['Current Ratio'] < 1.5:
-        insights.append(("⚠️ Liquidity Watch", f"Current ratio of {ratios['Current Ratio']:.1f} is below optimal", "warning"))
-    
-    if ratios['Debt Ratio'] > 70:
-        insights.append(("🔴 High Leverage", "Debt ratio above 70% suggests high financial risk", "error"))
-    elif ratios['Debt Ratio'] > 50:
-        insights.append(("⚠️ Moderate Leverage", f"Debt ratio of {ratios['Debt Ratio']:.1f}% requires monitoring", "warning"))
-    
-    # Customer receivables analysis
-    receivables = get_account_value(df, 'Customer Receivables', '2024')
-    revenue = get_account_value(df, 'Sales Revenue', '2024')
-    if revenue > 0 and (receivables / revenue) > 0.2:
-        insights.append(("💳 Receivables Risk", f"Customer receivables is {receivables/1000000:.1f}M NOK ({receivables/revenue*100:.0f}% of revenue)", "warning"))
-    
-    if ratios['Net Margin'] > 20:
-        insights.append(("💰 Excellent Profitability", f"Net margin of {ratios['Net Margin']:.1f}% shows excellent profitability", "success"))
-    elif ratios['Net Margin'] > 10:
-        insights.append(("💰 Good Profitability", f"Net margin of {ratios['Net Margin']:.1f}% shows good profitability", "success"))
-    
-    # Turnaround story
-    net_prev = get_account_value(df, 'Annual Result', '2023')
-    net_current = get_account_value(df, 'Annual Result', '2024')
-    if net_prev < 0 and net_current > 0:
-        insights.append(("🔄 Successful Turnaround", "Company moved from loss to profit - remarkable recovery!", "success"))
-    
-    return insights
-
-# Sidebar navigation
-st.sidebar.title("📊 FinanceGPT Analyzer")
-page = st.sidebar.selectbox("Choose Analysis", [
-    "🏠 Dashboard", 
-    "💰 Income Analysis", 
-    "🏛️ Balance Sheet", 
-    "💸 Cash Flow", 
-    "📊 Financial Ratios",
-    "🤖 AI Assistant"
-])
-
-# File upload
-st.sidebar.subheader("📁 Data Upload")
-uploaded_file = st.sidebar.file_uploader("Upload CSV file", type=['csv'])
-use_sample = st.sidebar.button("Use Sample Data")
-
-# Load data
-if uploaded_file is not None:
-    try:
-        df = pd.read_csv(uploaded_file)
-        st.sidebar.success("✅ File uploaded successfully!")
-        # Validate required columns
-        required_cols = ['Account_Name_English', '2024_Amount_NOK', '2023_Amount_NOK']
-        if not all(col in df.columns for col in required_cols):
-            st.sidebar.error("❌ CSV must have columns: Account_Name_English, 2024_Amount_NOK, 2023_Amount_NOK")
-            df = get_sample_data()
-    except Exception as e:
-        st.sidebar.error(f"❌ Error reading file: {str(e)}")
-        df = get_sample_data()
-elif use_sample:
-    df = get_sample_data()
-    st.sidebar.success("✅ Sample data loaded!")
-else:
-    df = get_sample_data()
-
-# Calculate ratios and insights
-ratios = calculate_ratios(df)
-insights = generate_insights(df, ratios)
-
-# Main content based on selected page
-if page == "🏠 Dashboard":
-    st.title("📊 FinanceGPT Analyzer Dashboard")
-    
-    # Key metrics
-    col1, col2, col3, col4 = st.columns(4)
-    
-    with col1:
-        revenue = get_account_value(df, 'Sales Revenue', '2024')
-        revenue_prev = get_account_value(df, 'Sales Revenue', '2023')
-        growth = ((revenue - revenue_prev) / revenue_prev * 100) if revenue_prev != 0 else 0
-        st.metric("💰 Revenue", f"{revenue/1000000:.1f}M NOK", f"{growth:+.0f}%")
-    
-    with col2:
-        net_income = get_account_value(df, 'Annual Result', '2024')
-        net_income_prev = get_account_value(df, 'Annual Result', '2023')
-        profit_change = net_income - net_income_prev
-        st.metric("📈 Net Profit", f"{net_income/1000000:.1f}M NOK", f"{profit_change/1000000:+.1f}M NOK")
-    
-    with col3:
-        assets = get_account_value(df, 'TOTAL ASSETS', '2024')
-        assets_prev = get_account_value(df, 'TOTAL ASSETS', '2023')
-        asset_growth = ((assets - assets_prev) / assets_prev * 100) if assets_prev != 0 else 0
-        st.metric("🏛️ Total Assets", f"{assets/1000000:.1f}M NOK", f"{asset_growth:+.0f}%")
-    
-    with col4:
-        cash = get_account_value(df, 'Cash at End of Period', '2024')
-        cash_prev = get_account_value(df, 'Cash at Beginning of Period', '2024')
-        cash_change = ((cash - cash_prev) / cash_prev * 100) if cash_prev != 0 else 0
-        st.metric("💸 Cash", f"{cash/1000000:.1f}M NOK", f"{cash_change:+.0f}%")
-    
-    # Charts section
-    col1, col2 = st.columns(2)
-    
-    with col1:
-        st.subheader("📈 Revenue vs Profit Trend")
-        trend_data = pd.DataFrame({
-            'Year': ['2023', '2024'],
-            'Revenue': [revenue_prev/1000000, revenue/1000000],
-            'Net Income': [net_income_prev/1000000, net_income/1000000]
-        })
-        
-        fig = px.line(trend_data, x='Year', y=['Revenue', 'Net Income'], 
-                     title="Revenue vs Net Income (Million NOK)")
-        fig.update_layout(height=400)
-        st.plotly_chart(fig, use_container_width=True)
+class FinancialAnalyzer:
+    def __init__(self, df):
+        self.df = df.copy()
+        self.process_data()
+        
+    def process_data(self):
+        """Process and structure financial data"""
+        # Create pivot table for easier analysis
+        self.pivot_df = self.df.pivot_table(
+            index=['Year', 'Statement_Type'],
+            columns='Account_Name_English',
+            values=['2024_Amount_NOK', '2023_Amount_NOK'],
+            aggfunc='first'
+        ).fillna(0)
+        
+        # Flatten column names
+        self.pivot_df.columns = ['_'.join(col).strip() for col in self.pivot_df.columns]
+        self.pivot_df = self.pivot_df.reset_index()
+        
+    def get_key_metrics(self):
+        """Calculate key financial metrics"""
+        try:
+            # Extract key values
+            revenue_2024 = self.df[self.df['Account_Name_English'] == 'Sales Revenue']['2024_Amount_NOK'].iloc[0]
+            revenue_2023 = self.df[self.df['Account_Name_English'] == 'Sales Revenue']['2023_Amount_NOK'].iloc[0]
+            
+            # Try to get net profit (might have different names)
+            net_profit_2024 = 0
+            net_profit_2023 = 0
+            
+            profit_accounts = ['Net Profit', 'Net Income', 'Profit After Tax', 'Result']
+            for account in profit_accounts:
+                profit_data = self.df[self.df['Account_Name_English'].str.contains(account, case=False, na=False)]
+                if not profit_data.empty:
+                    net_profit_2024 = profit_data['2024_Amount_NOK'].iloc[0]
+                    net_profit_2023 = profit_data['2023_Amount_NOK'].iloc[0]
+                    break
+            
+            # Calculate growth rates
+            revenue_growth = ((revenue_2024 - revenue_2023) / revenue_2023 * 100) if revenue_2023 != 0 else 0
+            profit_growth = ((net_profit_2024 - net_profit_2023) / abs(net_profit_2023) * 100) if net_profit_2023 != 0 else 0
+            
+            return {
+                'revenue_2024': revenue_2024,
+                'revenue_2023': revenue_2023,
+                'revenue_growth': revenue_growth,
+                'net_profit_2024': net_profit_2024,
+                'net_profit_2023': net_profit_2023,
+                'profit_growth': profit_growth
+            }
+        except:
+            return {
+                'revenue_2024': 0, 'revenue_2023': 0, 'revenue_growth': 0,
+                'net_profit_2024': 0, 'net_profit_2023': 0, 'profit_growth': 0
+            }
+    
+    def create_revenue_trend_chart(self):
+        """Create revenue trend visualization"""
+        metrics = self.get_key_metrics()
+        
+        fig = go.Figure()
+        
+        years = ['2023', '2024']
+        revenues = [metrics['revenue_2023'], metrics['revenue_2024']]
+        profits = [metrics['net_profit_2023'], metrics['net_profit_2024']]
+        
+        # Revenue line
+        fig.add_trace(go.Scatter(
+            x=years, y=revenues,
+            mode='lines+markers',
+            name='Revenue',
+            line=dict(color='#1f77b4', width=3),
+            marker=dict(size=10)
+        ))
+        
+        # Profit line (secondary y-axis)
+        fig.add_trace(go.Scatter(
+            x=years, y=profits,
+            mode='lines+markers',
+            name='Net Profit',
+            line=dict(color='#ff7f0e', width=3),
+            marker=dict(size=10),
+            yaxis='y2'
+        ))
+        
+        fig.update_layout(
+            title='Revenue vs Profit Trend',
+            xaxis_title='Year',
+            yaxis=dict(title='Revenue (NOK)', side='left'),
+            yaxis2=dict(title='Net Profit (NOK)', side='right', overlaying='y'),
+            hovermode='x unified',
+            template='plotly_white'
+        )
+        
+        return fig
     
-    with col2:
-        st.subheader("🎯 Financial Health Score")
-        categories = ['Profitability', 'Liquidity', 'Efficiency', 'Leverage', 'Growth']
-        scores = [85, 65, 75, 60, 95]  # Based on ratios
+    def create_financial_health_radar(self):
+        """Create financial health radar chart"""
+        metrics = self.get_key_metrics()
+        
+        # Calculate health scores (0-100)
+        revenue_score = min(100, max(0, metrics['revenue_growth'] + 50))  # Base 50, adjust by growth
+        profit_score = 85 if metrics['net_profit_2024'] > 0 else 25
+        growth_score = min(100, max(0, metrics['revenue_growth']))
+        stability_score = 75  # Default for demo
+        efficiency_score = 70  # Default for demo
+        
+        categories = ['Revenue Growth', 'Profitability', 'Growth Rate', 'Stability', 'Efficiency']
+        values = [revenue_score, profit_score, growth_score, stability_score, efficiency_score]
         
-        fig = go.Figure(data=go.Scatterpolar(
-            r=scores,
+        fig = go.Figure()
+        
+        fig.add_trace(go.Scatterpolar(
+            r=values,
             theta=categories,
             fill='toself',
-            name='Current Performance'
+            name='Financial Health',
+            line=dict(color='#2E86AB'),
+            fillcolor='rgba(46, 134, 171, 0.3)'
         ))
+        
         fig.update_layout(
-            polar=dict(radialaxis=dict(visible=True, range=[0, 100])),
-            height=400
+            polar=dict(
+                radialaxis=dict(
+                    visible=True,
+                    range=[0, 100]
+                )),
+            showlegend=False,
+            title="Financial Health Score"
         )
-        st.plotly_chart(fig, use_container_width=True)
-    
-    # Second row of charts
-    col1, col2 = st.columns(2)
-    
-    with col1:
-        st.subheader("🏛️ Asset vs Liability Structure")
-        structure_data = pd.DataFrame({
-            'Category': ['Total Assets', 'Total Debt', 'Total Equity'],
-            'Amount': [assets/1000000, get_account_value(df, 'Total Debt', '2024')/1000000, get_account_value(df, 'Total Equity', '2024')/1000000]
-        })
-        
-        fig = px.bar(structure_data, x='Category', y='Amount',
-                    title="Financial Structure (Million NOK)")
-        fig.update_traces(marker_color=['#3498db', '#e74c3c', '#2ecc71'])
-        fig.update_layout(height=400)
-        st.plotly_chart(fig, use_container_width=True)
-    
-    with col2:
-        st.subheader("💸 Cash Flow Overview")
-        # Create sample cash flow data
-        operating_cf = get_account_value(df, 'Net Cash Flow from Operating Activities', '2024')
-        investing_cf = get_account_value(df, 'Net Cash Flow from Investing Activities', '2024')
-        financing_cf = get_account_value(df, 'Net Cash Flow from Financing Activities', '2024')
-        
-        categories = ['Operating', 'Investing', 'Financing']
-        values = [operating_cf/1000000, investing_cf/1000000, financing_cf/1000000]
-        colors = ['green' if v > 0 else 'red' for v in values]
-        
-        fig = go.Figure(data=[go.Bar(x=categories, y=values, marker_color=colors)])
-        fig.update_layout(title="Cash Flow by Activity (Million NOK)", height=400)
-        st.plotly_chart(fig, use_container_width=True)
-    
-    # Insights section
-    st.subheader("🎯 Key Insights")
-    for title, message, type in insights:
-        if type == "success":
-            st.success(f"**{title}**: {message}")
-        elif type == "warning":
-            st.warning(f"**{title}**: {message}")
-        else:
-            st.error(f"**{title}**: {message}")
-
-elif page == "💰 Income Analysis":
-    st.title("💰 Income Statement Analysis")
-    
-    # Income statement data
-    income_data = df[df['Statement_Type'] == 'Income Statement'].copy()
-    
-    col1, col2 = st.columns(2)
-    
-    with col1:
-        st.subheader("📊 Revenue Breakdown")
-        revenue = get_account_value(df, 'Sales Revenue', '2024')
-        cogs = get_account_value(df, 'Cost of Goods Sold', '2024')
-        other_opex = get_account_value(df, 'Other Operating Expenses', '2024')
-        payroll = get_account_value(df, 'Payroll Expenses', '2024')
-        
-        breakdown = pd.DataFrame({
-            'Category': ['Gross Profit', 'COGS', 'Payroll', 'Other OpEx'],
-            'Amount': [revenue - cogs, cogs, payroll, other_opex]
-        })
-        
-        fig = px.pie(breakdown, values='Amount', names='Category', 
-                    title="Revenue Allocation (2024)")
-        st.plotly_chart(fig, use_container_width=True)
-    
-    with col2:
-        st.subheader("📈 Profitability Metrics")
-        metrics_df = pd.DataFrame({
-            'Metric': ['Gross Margin', 'Net Margin', 'ROA', 'ROE'],
-            'Value': [ratios['Gross Margin'], ratios['Net Margin'], 
-                     ratios['ROA'], ratios['ROE']],
-            'Grade': [get_ratio_grade('Gross Margin', ratios['Gross Margin']),
-                     get_ratio_grade('Net Margin', ratios['Net Margin']),
-                     get_ratio_grade('ROA', ratios['ROA']),
-                     get_ratio_grade('ROE', ratios['ROE'])]
-        })
-        
-        for _, row in metrics_df.iterrows():
-            col_a, col_b, col_c = st.columns([2, 1, 1])
-            with col_a:
-                st.write(row['Metric'])
-            with col_b:
-                st.write(f"{row['Value']:.1f}%")
-            with col_c:
-                color = {"A": "🟢", "B": "🟡", "C": "🟠", "D": "🔴"}.get(row['Grade'], "⚪")
-                st.write(f"{color} {row['Grade']}")
-    
-    # Year over year comparison
-    st.subheader("📊 Year-over-Year Comparison")
-    comparison_data = pd.DataFrame({
-        'Account': ['Sales Revenue', 'Cost of Goods Sold', 'Operating Result', 'Annual Result'],
-        '2023': [
-            get_account_value(df, 'Sales Revenue', '2023')/1000000,
-            get_account_value(df, 'Cost of Goods Sold', '2023')/1000000,
-            get_account_value(df, 'Operating Result', '2023')/1000000,
-            get_account_value(df, 'Annual Result', '2023')/1000000
-        ],
-        '2024': [
-            get_account_value(df, 'Sales Revenue', '2024')/1000000,
-            get_account_value(df, 'Cost of Goods Sold', '2024')/1000000,
-            get_account_value(df, 'Operating Result', '2024')/1000000,
-            get_account_value(df, 'Annual Result', '2024')/1000000
-        ]
-    })
-    
-    fig = go.Figure()
-    fig.add_trace(go.Bar(name='2023', x=comparison_data['Account'], y=comparison_data['2023'], marker_color='lightblue'))
-    fig.add_trace(go.Bar(name='2024', x=comparison_data['Account'], y=comparison_data['2024'], marker_color='darkblue'))
-    fig.update_layout(title="Income Statement Comparison (Million NOK)", barmode='group')
-    st.plotly_chart(fig, use_container_width=True)
-
-elif page == "🏛️ Balance Sheet":
-    st.title("🏛️ Balance Sheet Analysis")
-    
-    col1, col2 = st.columns(2)
-    
-    with col1:
-        st.subheader("🏦 Asset Structure")
-        current_assets = get_account_value(df, 'Total Current Assets', '2024')
-        fixed_assets = get_account_value(df, 'Total Fixed Assets', '2024')
-        
-        asset_structure = pd.DataFrame({
-            'Asset Type': ['Current Assets', 'Fixed Assets'],
-            'Amount': [current_assets/1000000, fixed_assets/1000000]
-        })
-        
-        fig = px.bar(asset_structure, x='Asset Type', y='Amount',
-                    title="Asset Composition (Million NOK)")
-        fig.update_traces(marker_color=['#3498db', '#e74c3c'])
-        st.plotly_chart(fig, use_container_width=True)
-    
-    with col2:
-        st.subheader("⚖️ Solvency Ratios")
-        solvency_metrics = pd.DataFrame({
-            'Ratio': ['Current Ratio', 'Debt Ratio', 'Equity Ratio'],
-            'Value': [ratios['Current Ratio'], ratios['Debt Ratio'], 
-                     100 - ratios['Debt Ratio']],
-            'Benchmark': [1.5, 50, 50]
-        })
         
-        fig = go.Figure()
-        fig.add_trace(go.Bar(name='Current', x=solvency_metrics['Ratio'], 
-                            y=solvency_metrics['Value'], marker_color='lightblue'))
-        fig.add_trace(go.Bar(name='Benchmark', x=solvency_metrics['Ratio'], 
-                            y=solvency_metrics['Benchmark'], marker_color='orange'))
-        fig.update_layout(title="Solvency Analysis", barmode='group')
-        st.plotly_chart(fig, use_container_width=True)
-    
-    # Balance sheet details
-    st.subheader("📋 Balance Sheet Details")
-    
-    tab1, tab2, tab3 = st.tabs(["📊 Assets", "💳 Liabilities", "🏦 Equity"])
-    
-    with tab1:
-        st.write("**Asset Breakdown (2024)**")
-        asset_details = pd.DataFrame({
-            'Asset Category': [
-                'Customer Receivables',
-                'Other Receivables', 
-                'Bank Deposits Cash',
-                'Machinery and Equipment',
-                'Land Buildings'
-            ],
-            'Amount (NOK)': [
-                get_account_value(df, 'Customer Receivables', '2024'),
-                get_account_value(df, 'Other Receivables', '2024'),
-                get_account_value(df, 'Bank Deposits Cash and Similar', '2024'),
-                get_account_value(df, 'Machinery and Equipment', '2024'),
-                get_account_value(df, 'Land Buildings and Other Real Estate', '2024')
-            ]
-        })
-        asset_details['Amount (Million NOK)'] = asset_details['Amount (NOK)'] / 1000000
-        asset_details['Percentage'] = (asset_details['Amount (NOK)'] / asset_details['Amount (NOK)'].sum() * 100).round(1)
-        st.dataframe(asset_details[['Asset Category', 'Amount (Million NOK)', 'Percentage']], use_container_width=True)
-    
-    with tab2:
-        st.write("**Liability Breakdown (2024)**")
-        liability_details = pd.DataFrame({
-            'Liability Category': [
-                'Accounts Payable',
-                'Short-term Debt',
-                'Long-term Debt',
-                'Payable Tax'
-            ],
-            'Amount (NOK)': [
-                get_account_value(df, 'Accounts Payable', '2024'),
-                get_account_value(df, 'Total Short-term Debt', '2024'),
-                get_account_value(df, 'Total Long-term Debt', '2024'),
-                get_account_value(df, 'Payable Tax', '2024')
-            ]
-        })
-        liability_details['Amount (Million NOK)'] = liability_details['Amount (NOK)'] / 1000000
-        liability_details['Percentage'] = (liability_details['Amount (NOK)'] / liability_details['Amount (NOK)'].sum() * 100).round(1)
-        st.dataframe(liability_details[['Liability Category', 'Amount (Million NOK)', 'Percentage']], use_container_width=True)
-    
-    with tab3:
-        st.write("**Equity Breakdown (2024)**")
-        equity_details = pd.DataFrame({
-            'Equity Category': [
-                'Share Capital',
-                'Retained Earnings'
-            ],
-            'Amount (NOK)': [
-                get_account_value(df, 'Share Capital', '2024'),
-                get_account_value(df, 'Other Equity', '2024')
-            ]
-        })
-        equity_details['Amount (Million NOK)'] = equity_details['Amount (NOK)'] / 1000000
-        equity_details['Percentage'] = (equity_details['Amount (NOK)'] / equity_details['Amount (NOK)'].sum() * 100).round(1)
-        st.dataframe(equity_details[['Equity Category', 'Amount (Million NOK)', 'Percentage']], use_container_width=True)
-
-elif page == "💸 Cash Flow":
-    st.title("💸 Cash Flow Analysis")
-    
-    # Cash flow waterfall
-    st.subheader("🌊 Cash Flow Waterfall")
-    
-    operating_cf = get_account_value(df, 'Net Cash Flow from Operating Activities', '2024')
-    investing_cf = get_account_value(df, 'Net Cash Flow from Investing Activities', '2024')
-    financing_cf = get_account_value(df, 'Net Cash Flow from Financing Activities', '2024')
-    net_change = get_account_value(df, 'NET CHANGE IN CASH', '2024')
+        return fig
     
-    categories = ['Operating', 'Investing', 'Financing', 'Net Change']
-    values = [operating_cf, investing_cf, financing_cf, net_change]
-    
-    fig = go.Figure(go.Waterfall(
-        name="Cash Flow", orientation="v",
-        measure=["relative", "relative", "relative", "total"],
-        x=categories,
-        textposition="outside",
-        text=[f"{v/1000000:.1f}M" for v in values],
-        y=[v/1000000 for v in values],
-        connector={"line": {"color": "rgb(63, 63, 63)"}},
-    ))
-    fig.update_layout(title="Cash Flow Waterfall (Million NOK)", showlegend=False)
-    st.plotly_chart(fig, use_container_width=True)
-    
-    # Cash flow quality metrics
-    col1, col2, col3 = st.columns(3)
-    
-    with col1:
-        net_income = get_account_value(df, 'Annual Result', '2024')
-        quality_ratio = operating_cf / net_income if net_income != 0 else 0
-        st.metric("💧 Cash Quality Ratio", f"{quality_ratio:.2f}", 
-                 "Healthy" if quality_ratio > 1 else "Needs attention")
-    
-    with col2:
-        fcf = operating_cf + investing_cf  # OCF + Investing
-        st.metric("🆓 Free Cash Flow", f"{fcf/1000000:.1f}M NOK",
-                 "Positive" if fcf > 0 else "Negative")
-    
-    with col3:
-        cash_current = get_account_value(df, 'Cash at End of Period', '2024')
-        cash_prev = get_account_value(df, 'Cash at Beginning of Period', '2024')
-        cash_growth = ((cash_current - cash_prev) / cash_prev * 100) if cash_prev != 0 else 0
-        st.metric("💰 Cash Growth", f"{cash_growth:+.1f}%")
-    
-    # Detailed cash flow analysis
-    st.subheader("📊 Cash Flow Components")
-    
-    tab1, tab2, tab3 = st.tabs(["🏭 Operating", "🏗️ Investing", "💰 Financing"])
-    
-    with tab1:
-        st.write("**Operating Cash Flow Components**")
-        operating_components = pd.DataFrame({
-            'Component': [
-                'Net Income',
-                'Depreciation',
-                'Change in Receivables',
-                'Change in Payables'
-            ],
-            'Amount (Million NOK)': [
-                get_account_value(df, 'Net Income', '2024')/1000000,
-                get_account_value(df, 'Depreciation', '2024')/1000000,
-                get_account_value(df, 'Change in Customer Receivables', '2024')/1000000,
-                get_account_value(df, 'Change in Accounts Payable', '2024')/1000000
-            ]
-        })
-        st.dataframe(operating_components, use_container_width=True)
-    
-    with tab2:
-        st.write("**Investing Cash Flow Components**")
-        investing_components = pd.DataFrame({
-            'Component': [
-                'Purchase of Fixed Assets',
-                'Change in Financial Assets'
-            ],
-            'Amount (Million NOK)': [
-                get_account_value(df, 'Purchase of Fixed Assets', '2024')/1000000,
-                get_account_value(df, 'Change in Financial Fixed Assets', '2024')/1000000
-            ]
-        })
-        st.dataframe(investing_components, use_container_width=True)
-    
-    with tab3:
-        st.write("**Financing Cash Flow Components**")
-        financing_components = pd.DataFrame({
-            'Component': [
-                'Change in Long-term Debt',
-                'Change in Other Long-term Debt'
-            ],
-            'Amount (Million NOK)': [
-                get_account_value(df, 'Change in Long-term Debt to Credit Institutions', '2024')/1000000,
-                get_account_value(df, 'Change in Other Long-term Debt', '2024')/1000000
-            ]
-        })
-        st.dataframe(financing_components, use_container_width=True)
+    def generate_insights(self):
+        """Generate AI-powered insights"""
+        metrics = self.get_key_metrics()
+        insights = []
+        
+        if metrics['revenue_growth'] > 100:
+            insights.append("🚀 Exceptional revenue growth of {:.1f}% indicates strong business expansion".format(metrics['revenue_growth']))
+        elif metrics['revenue_growth'] > 20:
+            insights.append("📈 Strong revenue growth of {:.1f}% shows healthy business development".format(metrics['revenue_growth']))
+        elif metrics['revenue_growth'] < 0:
+            insights.append("⚠️ Revenue declined by {:.1f}%, requires immediate attention".format(abs(metrics['revenue_growth'])))
+        
+        if metrics['net_profit_2024'] > 0 and metrics['net_profit_2023'] <= 0:
+            insights.append("✅ Company achieved profitability in 2024, turning around from previous losses")
+        elif metrics['net_profit_2024'] > metrics['net_profit_2023'] and metrics['net_profit_2023'] > 0:
+            insights.append("💰 Profit increased by {:.1f}%, showing improved efficiency".format(metrics['profit_growth']))
+        
+        if not insights:
+            insights.append("📊 Financial data processed successfully. Consider uploading more detailed statements for deeper analysis.")
+        
+        return insights
 
-elif page == "📊 Financial Ratios":
-    st.title("📊 Financial Ratios Hub")
-    
-    # Ratio categories
-    ratio_categories = {
-        'Profitability': ['Net Margin', 'ROA', 'ROE', 'Gross Margin', 'Operating Margin'],
-        'Liquidity': ['Current Ratio'],
-        'Efficiency': ['Asset Turnover'],
-        'Leverage': ['Debt Ratio', 'Debt to Equity']
+def create_sample_data():
+    """Create sample financial data"""
+    sample_data = {
+        'Year': [2024, 2024, 2024, 2024, 2024] * 2,
+        'Statement_Type': ['Income Statement'] * 5 + ['Balance Sheet'] * 5,
+        'Account_Name_Norwegian': ['Salgsinntekt', 'Varekostnad', 'Driftsresultat', 'Nettoresultat', 'Kontanter'] * 2,
+        'Account_Name_English': ['Sales Revenue', 'Cost of Goods Sold', 'Operating Result', 'Net Result', 'Cash'] * 2,
+        '2024_Amount_NOK': [25107008, -15000000, 8000000, 5300000, 1600000] * 2,
+        '2023_Amount_NOK': [4891891, -3000000, 1500000, -500000, 800000] * 2
     }
-    
-    selected_category = st.selectbox("Select Ratio Category", 
-                                   ['All'] + list(ratio_categories.keys()))
-    
-    # Display ratios
-    for category, ratio_names in ratio_categories.items():
-        if selected_category == 'All' or selected_category == category:
-            st.subheader(f"📈 {category} Ratios")
-            
-            cols = st.columns(min(len(ratio_names), 4))  # Max 4 columns
-            for i, ratio_name in enumerate(ratio_names):
-                with cols[i % 4]:
-                    value = ratios[ratio_name]
-                    grade = get_ratio_grade(ratio_name, value)
-                    grade_color = {"A": "🟢", "B": "🟡", "C": "🟠", "D": "🔴"}.get(grade, "⚪")
-                    
-                    if ratio_name in ['Current Ratio', 'Asset Turnover', 'Debt to Equity']:
-                        st.metric(ratio_name, f"{value:.2f}", f"Grade: {grade_color} {grade}")
-                    else:
-                        st.metric(ratio_name, f"{value:.1f}%", f"Grade: {grade_color} {grade}")
-    
-    # Industry benchmark comparison
-    st.subheader("🎯 Industry Benchmark Comparison")
-    
-    benchmark_data = pd.DataFrame({
-        'Ratio': ['ROE (%)', 'ROA (%)', 'Current Ratio', 'Debt Ratio (%)'],
-        'Your Company': [ratios['ROE'], ratios['ROA'], ratios['Current Ratio'], ratios['Debt Ratio']],
-        'Industry Average': [15.0, 8.0, 1.5, 45.0]
-    })
-    
-    fig = go.Figure()
-    fig.add_trace(go.Bar(name='Your Company', x=benchmark_data['Ratio'], 
-                        y=benchmark_data['Your Company'], marker_color='lightblue'))
-    fig.add_trace(go.Bar(name='Industry Average', x=benchmark_data['Ratio'], 
-                        y=benchmark_data['Industry Average'], marker_color='orange'))
-    fig.update_layout(title="Performance vs Industry Benchmarks", barmode='group')
-    st.plotly_chart(fig, use_container_width=True)
-    
-    # Ratio trend analysis
-    st.subheader("📈 Ratio Trends")
-    
-    # Create sample trend data
-    trend_ratios = pd.DataFrame({
-        'Year': ['2023', '2024'],
-        'ROE': [
-            (get_account_value(df, 'Annual Result', '2023') / get_account_value(df, 'Total Equity', '2023') * 100),
-            ratios['ROE']
-        ],
-        'Current Ratio': [
-            (get_account_value(df, 'Total Current Assets', '2023') / get_account_value(df, 'Total Short-term Debt', '2023')),
-            ratios['Current Ratio']
-        ],
-        'Debt Ratio': [
-            (get_account_value(df, 'Total Debt', '2023') / get_account_value(df, 'TOTAL ASSETS', '2023') * 100),
-            ratios['Debt Ratio']
-        ]
-    })
-    
-    fig = px.line(trend_ratios, x='Year', y=['ROE', 'Debt Ratio'], 
-                 title="Key Ratios Trend Analysis")
-    st.plotly_chart(fig, use_container_width=True)
+    return pd.DataFrame(sample_data)
 
-elif page == "🤖 AI Assistant":
-    st.title("🤖 AI Financial Assistant")
+def main():
+    st.title("📊 FinanceGPT Analyzer")
+    st.markdown("### Professional Financial Analysis Platform")
+    
+    # Sidebar navigation
+    pages = {
+        "🏠 Dashboard": "dashboard",
+        "💰 Income Analysis": "income",
+        "🏛️ Balance Sheet": "balance",
+        "💸 Cash Flow": "cashflow",
+        "📊 Financial Ratios": "ratios",
+        "🤖 AI Assistant": "ai"
+    }
     
-    # Quick analysis buttons
-    st.subheader("🎯 Quick Analysis")
-    col1, col2, col3 = st.columns(3)
+    selected_page = st.sidebar.selectbox("Navigation", list(pages.keys()))
+    current_page = pages[selected_page]
     
-    with col1:
-        if st.button("📊 Overall Health Check", key="health_check"):
-            st.success("**Financial Health Assessment:**")
-            st.write("• Revenue growth of 413% indicates explosive business growth")
-            st.write("• Strong profitability with 21.2% net margin")
-            st.write("• Good liquidity with current ratio of 1.37")
-            st.write("• Moderate leverage at 51.8% debt ratio")
-            st.write("• **Overall Grade: A- (Excellent performance)**")
+    # File upload section
+    st.sidebar.markdown("---")
+    st.sidebar.subheader("📁 Data Upload")
     
-    with col2:
-        if st.button("⚠️ Risk Analysis", key="risk_analysis"):
-            st.warning("**Key Risk Factors:**")
-            st.write("• High accounts receivable (5.6M NOK - 22% of revenue)")
-            st.write("• Rapid growth may strain working capital")
-            st.write("• Customer concentration risk needs monitoring")
-            st.write("• **Recommendation: Strengthen cash collection processes**")
+    uploaded_file = st.sidebar.file_uploader(
+        "Upload CSV file",
+        type=['csv'],
+        help="Upload your financial statement CSV file"
+    )
     
-    with col3:
-        if st.button("💡 Strategic Recommendations", key="recommendations"):
-            st.info("**Strategic Recommendations:**")
-            st.write("• Implement automated invoicing and follow-up systems")
-            st.write("• Consider factoring high-quality receivables")
-            st.write("• Build cash reserves to 15-20% of revenue")
-            st.write("• **Focus: Scale operations while maintaining profitability**")
+    use_sample = st.sidebar.button("📊 Use Sample Data")
     
-    # Interactive chat interface
-    st.subheader("💬 Ask Me Anything About Your Finances")
+    # Load data
+    if uploaded_file is not None:
+        df = pd.read_csv(uploaded_file)
+        st.sidebar.success("✅ File uploaded successfully!")
+    elif use_sample:
+        df = create_sample_data()
+        st.sidebar.success("✅ Sample data loaded!")
+    else:
+        st.info("👆 Please upload a CSV file or use sample data to get started")
+        st.markdown("""
+        ### Expected CSV Format:
+        - **Year**: Year of the financial statement
+        - **Statement_Type**: Income Statement, Balance Sheet, Cash Flow
+        - **Account_Name_Norwegian**: Norwegian account name
+        - **Account_Name_English**: English account name  
+        - **2024_Amount_NOK**: Amount for 2024 in NOK
+        - **2023_Amount_NOK**: Amount for 2023 in NOK
+        """)
+        return
+    
+    # Initialize analyzer
+    analyzer = FinancialAnalyzer(df)
+    
+    # Dashboard Page
+    if current_page == "dashboard":
+        st.markdown("## 🏠 Financial Dashboard")
+        
+        # Key Metrics Cards
+        metrics = analyzer.get_key_metrics()
+        
+        col1, col2, col3, col4 = st.columns(4)
+        
+        with col1:
+            st.metric(
+                "💰 Revenue 2024",
+                f"{metrics['revenue_2024']:,.0f} NOK",
+                delta=f"{metrics['revenue_growth']:+.1f}%"
+            )
+            
+        with col2:
+            st.metric(
+                "📈 Net Profit 2024", 
+                f"{metrics['net_profit_2024']:,.0f} NOK",
+                delta="Turnaround ✅" if metrics['net_profit_2024'] > 0 and metrics['net_profit_2023'] <= 0 else f"{metrics['profit_growth']:+.1f}%"
+            )
+            
+        with col3:
+            st.metric(
+                "📊 Revenue Growth",
+                f"{metrics['revenue_growth']:+.1f}%",
+                delta="Strong Growth 🚀" if metrics['revenue_growth'] > 100 else "Growth 📈"
+            )
+            
+        with col4:
+            total_accounts = len(df['Account_Name_English'].unique())
+            st.metric(
+                "📋 Accounts Analyzed",
+                f"{total_accounts}",
+                delta="Data Points"
+            )
+        
+        # Charts Section
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            fig_trend = analyzer.create_revenue_trend_chart()
+            st.plotly_chart(fig_trend, use_container_width=True)
+            
+        with col2:
+            fig_radar = analyzer.create_financial_health_radar()
+            st.plotly_chart(fig_radar, use_container_width=True)
+        
+        # Quick Insights
+        st.markdown("## 🎯 Quick Insights")
+        insights = analyzer.generate_insights()
+        
+        for insight in insights:
+            st.markdown(f"""
+            <div class="insight-box">
+                <strong>{insight}</strong>
+            </div>
+            """, unsafe_allow_html=True)
+    
+    # Income Analysis Page
+    elif current_page == "income":
+        st.markdown("## 💰 Income Analysis")
+        
+        # Filter income statement data
+        income_data = df[df['Statement_Type'] == 'Income Statement']
+        
+        if income_data.empty:
+            st.warning("No income statement data found in the uploaded file.")
+            return
+        
+        # Revenue analysis
+        st.subheader("📈 Revenue Trend Analysis")
+        
+        revenue_data = income_data[income_data['Account_Name_English'].str.contains('Revenue|Sales', case=False, na=False)]
+        
+        if not revenue_data.empty:
+            fig = go.Figure()
+            
+            for _, row in revenue_data.iterrows():
+                fig.add_trace(go.Bar(
+                    x=['2023', '2024'],
+                    y=[row['2023_Amount_NOK'], row['2024_Amount_NOK']],
+                    name=row['Account_Name_English'],
+                    text=[f"{row['2023_Amount_NOK']:,.0f}", f"{row['2024_Amount_NOK']:,.0f}"],
+                    textposition='auto'
+                ))
+            
+            fig.update_layout(
+                title="Revenue Comparison 2023 vs 2024",
+                xaxis_title="Year",
+                yaxis_title="Amount (NOK)",
+                template='plotly_white'
+            )
+            
+            st.plotly_chart(fig, use_container_width=True)
+        
+        # Cost analysis
+        st.subheader("🥧 Cost Structure Analysis") 
+        
+        cost_data = income_data[income_data['Account_Name_English'].str.contains('Cost|Expense', case=False, na=False)]
+        
+        if not cost_data.empty:
+            # Create pie chart for 2024 costs
+            fig_pie = px.pie(
+                cost_data,
+                values='2024_Amount_NOK',
+                names='Account_Name_English',
+                title="Cost Structure 2024"
+            )
+            st.plotly_chart(fig_pie, use_container_width=True)
+        
+        # Profitability metrics
+        st.subheader("📊 Profitability Indicators")
+        
+        metrics = analyzer.get_key_metrics()
+        
+        col1, col2, col3 = st.columns(3)
+        
+        with col1:
+            if metrics['revenue_2024'] > 0:
+                profit_margin = (metrics['net_profit_2024'] / metrics['revenue_2024']) * 100
+                st.metric("Net Profit Margin", f"{profit_margin:.1f}%")
+        
+        with col2:
+            st.metric("Revenue Growth", f"{metrics['revenue_growth']:+.1f}%")
+        
+        with col3:
+            if metrics['net_profit_2023'] != 0:
+                st.metric("Profit Growth", f"{metrics['profit_growth']:+.1f}%")
     
-    # Initialize chat history
-    if "messages" not in st.session_state:
-        st.session_state.messages = [
-            {"role": "assistant", "content": "Hello! I'm your AI Financial Assistant. I can help you analyze your financial data, identify trends, and provide strategic recommendations. What would you like to know?"}
+    # Balance Sheet Page
+    elif current_page == "balance":
+        st.markdown("## 🏛️ Balance Sheet Analysis")
+        
+        balance_data = df[df['Statement_Type'] == 'Balance Sheet']
+        
+        if balance_data.empty:
+            st.warning("No balance sheet data found. Please upload data with Balance Sheet entries.")
+            st.markdown("### Sample Balance Sheet Accounts:")
+            st.markdown("- Cash, Accounts Receivable, Inventory (Assets)")
+            st.markdown("- Accounts Payable, Long-term Debt (Liabilities)")  
+            st.markdown("- Equity, Retained Earnings (Equity)")
+            return
+        
+        # Asset analysis
+        st.subheader("📊 Asset Structure")
+        
+        asset_keywords = ['Cash', 'Receivable', 'Inventory', 'Asset', 'Equipment', 'Property']
+        asset_data = balance_data[
+            balance_data['Account_Name_English'].str.contains('|'.join(asset_keywords), case=False, na=False)
         ]
-    
-    # Display chat history
-    for message in st.session_state.messages:
-        with st.chat_message(message["role"]):
-            st.markdown(message["content"])
-    
-    # Chat input
-    if prompt := st.chat_input("Ask about your financial data..."):
-        # Add user message to chat history
-        st.session_state.messages.append({"role": "user", "content": prompt})
-        
-        # Display user message
-        with st.chat_message("user"):
-            st.markdown(prompt)
-        
-        # Generate AI response
-        with st.chat_message("assistant"):
-            # Simple rule-based responses (in real app, this would use LLM)
-            prompt_lower = prompt.lower()
+        
+        if not asset_data.empty:
+            fig_assets = px.bar(
+                asset_data,
+                x='Account_Name_English',
+                y=['2023_Amount_NOK', '2024_Amount_NOK'],
+                title="Asset Comparison 2023 vs 2024",
+                barmode='group'
+            )
+            st.plotly_chart(fig_assets, use_container_width=True)
+        
+        # Liquidity ratios (if we have the right accounts)
+        st.subheader("⚖️ Financial Ratios")
+        
+        cash_data = balance_data[balance_data['Account_Name_English'].str.contains('Cash', case=False, na=False)]
+        debt_data = balance_data[balance_data['Account_Name_English'].str.contains('Debt|Payable', case=False, na=False)]
+        
+        if not cash_data.empty:
+            cash_2024 = cash_data['2024_Amount_NOK'].iloc[0]
+            cash_2023 = cash_data['2023_Amount_NOK'].iloc[0]
             
-            if any(word in prompt_lower for word in ["revenue", "sales", "income"]):
-                response = f"""**Revenue Analysis:**
-• Your revenue grew from {get_account_value(df, 'Sales Revenue', '2023')/1000000:.1f}M to {get_account_value(df, 'Sales Revenue', '2024')/1000000:.1f}M NOK ({ratios['Revenue Growth']:.1f}% growth)
-• This is exceptional growth indicating strong market demand
-• Gross margin is {ratios['Gross Margin']:.1f}%, showing good pricing power
-• **Key insight:** Focus on maintaining this growth trajectory while improving operational efficiency"""
-                
-            elif any(word in prompt_lower for word in ["profit", "profitability", "margin"]):
-                response = f"""**Profitability Analysis:**
-• Net margin improved to {ratios['Net Margin']:.1f}% (excellent level)
-• ROE of {ratios['ROE']:.1f}% shows strong returns to shareholders
-• Operating margin of {ratios['Operating Margin']:.1f}% indicates efficient operations
-• **Recommendation:** Continue optimizing cost structure while scaling"""
+            col1, col2 = st.columns(2)
+            with col1:
+                st.metric("Cash 2024", f"{cash_2024:,.0f} NOK")
+            with col2:
+                cash_growth = ((cash_2024 - cash_2023) / cash_2023 * 100) if cash_2023 != 0 else 0
+                st.metric("Cash Growth", f"{cash_growth:+.1f}%")
+    
+    # Cash Flow Page  
+    elif current_page == "cashflow":
+        st.markdown("## 💸 Cash Flow Analysis")
+        
+        cash_flow_data = df[df['Statement_Type'] == 'Cash Flow']
+        
+        if cash_flow_data.empty:
+            st.warning("No cash flow statement data found.")
+            st.markdown("### Cash Flow Analysis from Available Data:")
+            
+            # Analyze cash changes from balance sheet
+            cash_data = df[df['Account_Name_English'].str.contains('Cash', case=False, na=False)]
+            
+            if not cash_data.empty:
+                cash_2024 = cash_data['2024_Amount_NOK'].iloc[0] 
+                cash_2023 = cash_data['2023_Amount_NOK'].iloc[0]
+                cash_change = cash_2024 - cash_2023
                 
-            elif any(word in prompt_lower for word in ["cash", "liquidity", "flow"]):
-                cash = get_account_value(df, 'Cash at End of Period', '2024')
-                operating_cf = get_account_value(df, 'Net Cash Flow from Operating Activities', '2024')
-                response = f"""**Cash Flow Analysis:**
-• Cash position: {cash/1000000:.1f}M NOK (strong)
-• Operating cash flow: {operating_cf/1000000:.1f}M NOK
-• Current ratio: {ratios['Current Ratio']:.1f} (acceptable but could improve)
-• **Focus area:** Monitor working capital management closely"""
+                fig = go.Figure(go.Waterfall(
+                    name="Cash Flow",
+                    orientation="v",
+                    measure=["absolute", "relative", "absolute"],
+                    x=["Starting Cash (2023)", "Net Change", "Ending Cash (2024)"],
+                    textposition="outside",
+                    text=[f"{cash_2023:,.0f}", f"{cash_change:+,.0f}", f"{cash_2024:,.0f}"],
+                    y=[cash_2023, cash_change, cash_2024],
+                    connector={"line":{"color":"rgb(63, 63, 63)"}},
+                ))
                 
-            elif any(word in prompt_lower for word in ["risk", "debt", "leverage"]):
-                response = f"""**Risk Assessment:**
-• Debt ratio: {ratios['Debt Ratio']:.1f}% (moderate level)
-• Key risk: High receivables ({get_account_value(df, 'Customer Receivables', '2024')/1000000:.1f}M NOK)
-• Liquidity risk: Current ratio could be stronger
-• **Priority:** Improve collection processes and build cash reserves"""
+                fig.update_layout(
+                    title="Cash Position Change 2023-2024",
+                    showlegend=False,
+                    yaxis_title="Amount (NOK)"
+                )
                 
-            elif any(word in prompt_lower for word in ["compare", "benchmark", "industry"]):
-                response = f"""**Benchmark Comparison:**
-• Your ROE ({ratios['ROE']:.1f}%) vs Industry avg (15%): **Excellent**
-• Your ROA ({ratios['ROA']:.1f}%) vs Industry avg (8%): **Outstanding**
-• Your current ratio ({ratios['Current Ratio']:.1f}) vs Industry avg (1.5): **Needs improvement**
-• **Overall:** You're outperforming on profitability, focus on liquidity"""
+                st.plotly_chart(fig, use_container_width=True)
                 
-            elif any(word in prompt_lower for word in ["improve", "strategy", "plan"]):
-                response = """**Strategic Improvement Plan:**
-1. **Short-term (3 months):**
-   - Implement automated invoice management
-   - Negotiate better payment terms with major customers
-   
-2. **Medium-term (6-12 months):**
-   - Build cash reserves to 15-20% of revenue
-   - Consider factoring receivables if needed
-   
-3. **Long-term (12+ months):**
-   - Diversify customer base to reduce concentration risk
-   - Optimize working capital cycle"""
+                # Cash metrics
+                col1, col2, col3 = st.columns(3)
                 
-            else:
-                response = """I can help you analyze various aspects of your financial data:
-
-• **Revenue & Growth:** Ask about sales trends, growth drivers
-• **Profitability:** Inquire about margins, ROE, ROA analysis  
-• **Cash Flow:** Questions about liquidity, working capital
-• **Risk Assessment:** Ask about leverage, debt, financial risks
-• **Benchmarking:** Compare your performance to industry standards
-• **Strategy:** Get recommendations for improvement
-
-What specific area interests you most?"""
+                with col1:
+                    st.metric("Starting Cash", f"{cash_2023:,.0f} NOK")
+                with col2:
+                    st.metric("Ending Cash", f"{cash_2024:,.0f} NOK") 
+                with col3:
+                    growth = (cash_change / cash_2023 * 100) if cash_2023 != 0 else 0
+                    st.metric("Cash Growth", f"{growth:+.1f}%")
+            
+            return
+        
+        # If we have cash flow data, display it
+        st.subheader("🌊 Cash Flow Structure")
+        
+        operating_cf = cash_flow_data[cash_flow_data['Account_Name_English'].str.contains('Operating', case=False, na=False)]
+        investing_cf = cash_flow_data[cash_flow_data['Account_Name_English'].str.contains('Investing', case=False, na=False)]
+        financing_cf = cash_flow_data[cash_flow_data['Account_Name_English'].str.contains('Financing', case=False, na=False)]
+        
+        if not operating_cf.empty or not investing_cf.empty or not financing_cf.empty:
+            fig = go.Figure()
+            
+            categories = []
+            values_2024 = []
+            values_2023 = []
+            
+            for cf_type, cf_data in [("Operating", operating_cf), ("Investing", investing_cf), ("Financing", financing_cf)]:
+                if not cf_data.empty:
+                    categories.append(cf_type)
+                    values_2024.append(cf_data['2024_Amount_NOK'].iloc[0])
+                    values_2023.append(cf_data['2023_Amount_NOK'].iloc[0])
+            
+            fig.add_trace(go.Bar(name='2023', x=categories, y=values_2023))
+            fig.add_trace(go.Bar(name='2024', x=categories, y=values_2024))
+            
+            fig.update_layout(
+                title="Cash Flow by Category",
+                xaxis_title="Cash Flow Type",
+                yaxis_title="Amount (NOK)",
+                barmode='group'
+            )
             
-            st.markdown(response)
-            st.session_state.messages.append({"role": "assistant", "content": response})
+            st.plotly_chart(fig, use_container_width=True)
     
-    # Data context sidebar
-    with st.expander("📋 Current Financial Context", expanded=False):
-        col1, col2 = st.columns(2)
+    # Financial Ratios Page
+    elif current_page == "ratios":
+        st.markdown("## 📊 Financial Ratios Center")
+        
+        # Ratio categories
+        ratio_type = st.selectbox(
+            "Select Ratio Category",
+            ["Profitability", "Liquidity", "Growth", "All Ratios"]
+        )
+        
+        metrics = analyzer.get_key_metrics()
+        
+        st.subheader(f"📈 {ratio_type} Ratios")
+        
+        # Calculate ratios based on available data
+        ratios = {}
+        
+        if metrics['revenue_2024'] > 0:
+            ratios['Net Profit Margin'] = (metrics['net_profit_2024'] / metrics['revenue_2024']) * 100
+            ratios['Revenue Growth Rate'] = metrics['revenue_growth']
+        
+        if metrics['net_profit_2023'] != 0:
+            ratios['Profit Growth Rate'] = metrics['profit_growth']
+        
+        # Display ratios
+        if ratios:
+            cols = st.columns(len(ratios))
+            
+            for i, (ratio_name, ratio_value) in enumerate(ratios.items()):
+                with cols[i]:
+                    # Determine rating
+                    if 'Margin' in ratio_name:
+                        rating = 'A' if ratio_value > 20 else 'B' if ratio_value > 10 else 'C'
+                    elif 'Growth' in ratio_name:
+                        rating = 'A+' if ratio_value > 50 else 'A' if ratio_value > 20 else 'B'
+                    else:
+                        rating = 'B'
+                    
+                    st.metric(
+                        ratio_name,
+                        f"{ratio_value:.1f}%",
+                        delta=f"Rating: {rating}"
+                    )
+        
+        # Ratio definitions
+        st.subheader("📖 Ratio Definitions")
+        
+        with st.expander("Net Profit Margin"):
+            st.markdown("""
+            **Formula:** (Net Profit / Revenue) × 100
+            
+            **Interpretation:** Measures how much profit a company makes for every NOK of revenue.
+            - **A (>20%):** Excellent profitability
+            - **B (10-20%):** Good profitability  
+            - **C (<10%):** Needs improvement
+            """)
+        
+        with st.expander("Revenue Growth Rate"):
+            st.markdown("""
+            **Formula:** ((Current Year Revenue - Previous Year Revenue) / Previous Year Revenue) × 100
+            
+            **Interpretation:** Measures the rate of revenue increase year-over-year.
+            - **A+ (>50%):** Exceptional growth
+            - **A (20-50%):** Strong growth
+            - **B (0-20%):** Moderate growth
+            """)
+    
+    # AI Assistant Page
+    elif current_page == "ai":
+        st.markdown("## 🤖 AI Financial Assistant")
+        
+        st.markdown("### 💬 Ask me about your financial data")
+        
+        # Quick action buttons
+        st.markdown("#### 🎯 Quick Analysis")
+        
+        col1, col2, col3 = st.columns(3)
         
         with col1:
-            st.write("**Key Figures (2024):**")
-            st.write(f"• Revenue: {get_account_value(df, 'Sales Revenue', '2024')/1000000:.1f}M NOK")
-            st.write(f"• Net Income: {get_account_value(df, 'Annual Result', '2024')/1000000:.1f}M NOK") 
-            st.write(f"• Total Assets: {get_account_value(df, 'TOTAL ASSETS', '2024')/1000000:.1f}M NOK")
-            st.write(f"• Cash: {get_account_value(df, 'Cash at End of Period', '2024')/1000000:.1f}M NOK")
+            if st.button("🏥 Health Check"):
+                st.markdown("### Financial Health Analysis")
+                insights = analyzer.generate_insights()
+                for insight in insights:
+                    st.success(insight)
         
         with col2:
-            st.write("**Key Ratios:**")
-            st.write(f"• Revenue Growth: {ratios['Revenue Growth']:.1f}%")
-            st.write(f"• Net Margin: {ratios['Net Margin']:.1f}%")
-            st.write(f"• ROE: {ratios['ROE']:.1f}%")
-            st.write(f"• Current Ratio: {ratios['Current Ratio']:.1f}")
-
-# Footer
-st.sidebar.markdown("---")
-st.sidebar.markdown("📊 **FinanceGPT Analyzer**")
-st.sidebar.markdown("AI-Powered Financial Analysis")
-st.sidebar.markdown("Built with Streamlit & Plotly")
-
-# Additional sidebar info
-with st.sidebar.expander("ℹ️ How to Use"):
-    st.write("""
-    **Getting Started:**
-    1. Upload your CSV file or use sample data
-    2. Navigate through different analysis pages
-    3. Use the AI Assistant for specific questions
-    
-    **Required CSV Format:**
-    - Account_Name_English
-    - 2024_Amount_NOK  
-    - 2023_Amount_NOK
-    - Statement_Type (optional)
-    """)
+            if st.button("⚠️ Risk Assessment"):
+                st.markdown("### Risk Analysis")
+                metrics = analyzer.get_key_metrics()
+                
+                risks = []
+                if metrics['revenue_growth'] < 0:
+                    risks.append("🔴 Revenue Decline Risk - Immediate attention needed")
+                if metrics['net_profit_2024'] < 0:
+                    risks.append("🔴 Profitability Risk - Company is operating at a loss")
+                
+                if not risks:
+                    risks.append("🟢 No major risks identified in current data")
+                
+                for risk in risks:
+                    if "🔴" in risk:
+                        st.error(risk)
+                    else:
+                        st.success(risk)
+        
+        with col3:
+            if st.button("💡 Recommendations"):
+                st.markdown("### Strategic Recommendations")
+                metrics = analyzer.get_key_metrics()
+                
+                recommendations = []
+                
+                if metrics['revenue_growth'] > 100:
+                    recommendations.append("🚀 Scale operations to support rapid growth")
+                    recommendations.append("💰 Consider investing in infrastructure")
+                
+                if metrics['net_profit_2024'] > 0 and metrics['net_profit_2023'] <= 0:
+                    recommendations.append("✅ Maintain current profitable strategies")
+                    recommendations.append("📊 Focus on sustainable growth")
+                
+                recommendations.append("📈 Continue monitoring key financial metrics")
+                recommendations.append("🔍 Consider more detailed financial analysis")
+                
+                for rec in recommendations:
+                    st.info(rec)
+        
+        # Chat interface
+        st.markdown("---")
+        st.markdown("#### 💭 Custom Analysis")
+        
+        user_question = st.text_area(
+            "Ask a specific question about your financial data:",
+            placeholder="e.g., What are the main drivers of our revenue growth?"
+        )
+        
+        if st.button("🔍 Analyze") and user_question:
+            st.markdown("### Analysis Result")
+            
+            # Simple rule-based responses
+            question_lower = user_question.lower()
+            
+            if 'revenue' in question_lower and 'growth' in question_lower:
+                metrics = analyzer.get_key_metrics()
+                st.markdown(f"""
+                **Revenue Growth Analysis:**
+                
+                Your revenue grew from {metrics['revenue_2023']:,.0f} NOK in 2023 to {metrics['revenue_2024']:,.0f} NOK in 2024, 
+                representing a {metrics['revenue_growth']:.1f}% increase.
+                
+                This indicates {"exceptional" if metrics['revenue_growth'] > 100 else "strong" if metrics['revenue_growth'] > 20 else "moderate"} 
+                business growth.
+                """)
+            
+            elif 'profit' in question_lower:
+                metrics = analyzer.get_key_metrics()
+                st.markdown(f"""
+                **Profitability Analysis:**
+                
+                Your net profit changed from {metrics['net_profit_2023']:,.0f} NOK in 2023 to {metrics['net_profit_2024']:,.0f} NOK in 2024.
+                
+                {"The company achieved profitability turnaround! 🎉" if metrics['net_profit_2024'] > 0 and metrics['net_profit_2023'] <= 0 else 
+                 "Profit increased significantly! 📈" if metrics['profit_growth'] > 50 else
+                 "Company maintains profitability ✅" if metrics['net_profit_2024'] > 0 else
+                 "Focus needed on achieving profitability ⚠️"}
+                """)
+            
+            else:
+                st.markdown(f"""
+                **General Financial Overview:**
+                
+                Based on your uploaded data, here's what I can tell you:
+                
+                {analyzer.generate_insights()[0] if analyzer.generate_insights() else "Your financial data has been processed successfully."}
+                
+                For more specific analysis, try asking about:
+                - Revenue growth trends
+                - Profitability analysis  
+                - Cost structure
+                - Financial health assessment
+                """)
+    
+    # Data preview in sidebar
+    st.sidebar.markdown("---")
+    st.sidebar.subheader("📋 Data Preview")
+    
+    if st.sidebar.checkbox("Show uploaded data"):
+        st.sidebar.dataframe(df.head(), use_container_width=True)
+    
+    # Export functionality
+    st.sidebar.markdown("---")
+    st.sidebar.subheader("📤 Export")
+    
+    if st.sidebar.button("📊 Generate Report"):
+        st.sidebar.success("Report functionality will be implemented in future versions!")
 
-with st.sidebar.expander("📈 Key Features"):
-    st.write("""
-    • **Dashboard:** Overview & key metrics
-    • **Income Analysis:** Profitability deep-dive
-    • **Balance Sheet:** Asset/liability structure  
-    • **Cash Flow:** Liquidity analysis
-    • **Ratios Hub:** Comprehensive ratio analysis
-    • **AI Assistant:** Interactive financial insights
-    """)
+if __name__ == "__main__":
+    main()