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.dockerignore

@@ -1,51 +1,51 @@
-# Python
-__pycache__/
-*.py[cod]
-*$py.class
-*.so
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-
-# Virtual environments
-venv/
-env/
-ENV/
-
-# IDE
-.vscode/
-.idea/
-*.swp
-*.swo
-
-# OS
-.DS_Store
-Thumbs.db
-
-# Git
-.git/
-.gitignore
-
-# Documentation
-*.md
-docs/
-
-# Logs
-*.log
-
-# Temporary files
-tmp/
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual environments
+venv/
+env/
+ENV/
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Git
+.git/
+.gitignore
+
+# Documentation
+*.md
+docs/
+
+# Logs
+*.log
+
+# Temporary files
+tmp/
 temp/

.gitattributes

@@ -1,35 +1,35 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
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-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -1,40 +1,40 @@
-# Use Python 3.9 slim image
-FROM python:3.9-slim
-
-# Set working directory
-WORKDIR /app
-
-# Set environment variables
-ENV PYTHONDONTWRITEBYTECODE=1
-ENV PYTHONUNBUFFERED=1
-ENV STREAMLIT_SERVER_PORT=7860
-ENV STREAMLIT_SERVER_ADDRESS=0.0.0.0
-
-# Install system dependencies
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    software-properties-common \
-    && rm -rf /var/lib/apt/lists/*
-
-# Copy requirements first for better caching
-COPY requirements.txt .
-
-# Install Python dependencies
-RUN pip install --no-cache-dir -r requirements.txt
-
-# Copy application files
-COPY . .
-
-# Create .streamlit directory and config
-RUN mkdir -p .streamlit
-COPY .streamlit/config.toml .streamlit/
-
-# Expose port
-EXPOSE 7860
-
-# Health check
-HEALTHCHECK CMD curl --fail http://localhost:7860/_stcore/health
-
-# Run the application
+# Use Python 3.9 slim image
+FROM python:3.9-slim
+
+# Set working directory
+WORKDIR /app
+
+# Set environment variables
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+ENV STREAMLIT_SERVER_PORT=7860
+ENV STREAMLIT_SERVER_ADDRESS=0.0.0.0
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    software-properties-common \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements first for better caching
+COPY requirements.txt .
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application files
+COPY . .
+
+# Create .streamlit directory and config
+RUN mkdir -p .streamlit
+COPY .streamlit/config.toml .streamlit/
+
+# Expose port
+EXPOSE 7860
+
+# Health check
+HEALTHCHECK CMD curl --fail http://localhost:7860/_stcore/health
+
+# Run the application
 CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]

README.md

@@ -1,10 +1,10 @@
----
-title: Report
-emoji: 😻
-colorFrom: blue
-colorTo: indigo
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Report
+emoji: 😻
+colorFrom: blue
+colorTo: indigo
+sdk: docker
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,468 +1,476 @@
-import streamlit as st
-import pandas as pd
-import plotly.express as px
-import plotly.graph_objects as go
-from plotly.subplots import make_subplots
-import numpy as np
-from datetime import datetime
-import io
-
-# Initialize session state
-if 'data_loaded' not in st.session_state:
-    st.session_state.data_loaded = False
-if 'analyzer' not in st.session_state:
-    st.session_state.analyzer = None
-
-# Page configuration
-st.set_page_config(
-    page_title="📊 FinanceGPT Analyzer",
-    page_icon="📊",
-    layout="wide",
-    initial_sidebar_state="expanded"
-)
-
-# Custom CSS for better styling
-st.markdown("""
-<style>
-    .metric-card {
-        background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
-        padding: 1rem;
-        border-radius: 10px;
-        color: white;
-        text-align: center;
-        margin: 0.5rem 0;
-    }
-    .insight-box {
-        background: #f8f9fa;
-        padding: 1rem;
-        border-left: 4px solid #007bff;
-        border-radius: 5px;
-        margin: 1rem 0;
-    }
-    .warning-box {
-        background: #fff3cd;
-        padding: 1rem;
-        border-left: 4px solid #ffc107;
-        border-radius: 5px;
-        margin: 1rem 0;
-    }
-</style>
-""", unsafe_allow_html=True)
-
-class FinanceAnalyzer:
-    def __init__(self):
-        self.data = None
-        self.processed_data = {}
-        
-    def load_sample_data(self):
-        """Load sample financial data"""
-        sample_data = {
-            'Year': [2024, 2024, 2024, 2024, 2024, 2024, 2024, 2024, 2024, 2024,
-                    2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023],
-            'Statement_Type': ['Income Statement'] * 10 + ['Income Statement'] * 10,
-            'Account_Name_Norwegian': ['Salgsinntekt', 'Varekostnad', 'Bruttoresultat', 'Lønnskostnad', 
-                                     'Andre driftskostnader', 'Driftsresultat', 'Finansinntekter', 
-                                     'Finanskostnader', 'Ordinært resultat før skatt', 'Årsresultat'] * 2,
-            'Account_Name_English': ['Sales Revenue', 'Cost of Goods Sold', 'Gross Profit', 'Salary Costs',
-                                   'Other Operating Expenses', 'Operating Result', 'Financial Income',
-                                   'Financial Expenses', 'Profit Before Tax', 'Net Profit'] * 2,
-            '2024_Amount_NOK': [25107008, -15064205, 10042803, -3521456, -1987234, 4534113, 123456, 
-                               -234567, 4422002, 3537602, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
-            '2023_Amount_NOK': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4891891, -2934535, 1957356, -1234567,
-                               -654321, 68468, 45678, -123456, -9310, -7448]
-        }
-        return pd.DataFrame(sample_data)
-    
-    def process_financial_data(self, df):
-        """Process uploaded financial data"""
-        self.data = df
-        
-        # Create pivot tables for easier analysis
-        income_2024 = df[df['Year'] == 2024]['2024_Amount_NOK'].values
-        income_2023 = df[df['Year'] == 2023]['2023_Amount_NOK'].values
-        accounts = df[df['Year'] == 2024]['Account_Name_English'].values
-        
-        self.processed_data = {
-            'revenue_2024': income_2024[0] if len(income_2024) > 0 else 0,
-            'revenue_2023': income_2023[0] if len(income_2023) > 0 else 0,
-            'net_profit_2024': income_2024[9] if len(income_2024) > 9 else 0,
-            'net_profit_2023': income_2023[9] if len(income_2023) > 9 else 0,
-            'cogs_2024': abs(income_2024[1]) if len(income_2024) > 1 else 0,
-            'cogs_2023': abs(income_2023[1]) if len(income_2023) > 1 else 0,
-            'operating_profit_2024': income_2024[5] if len(income_2024) > 5 else 0,
-            'operating_profit_2023': income_2023[5] if len(income_2023) > 5 else 0,
-        }
-        
-    def calculate_metrics(self):
-        """Calculate key financial metrics"""
-        if not self.processed_data:
-            return {}
-            
-        data = self.processed_data
-        
-        # Growth rates
-        revenue_growth = ((data['revenue_2024'] - data['revenue_2023']) / 
-                         abs(data['revenue_2023']) * 100) if data['revenue_2023'] != 0 else 0
-        
-        # Profitability ratios
-        gross_margin_2024 = ((data['revenue_2024'] - data['cogs_2024']) / 
-                            data['revenue_2024'] * 100) if data['revenue_2024'] != 0 else 0
-        net_margin_2024 = (data['net_profit_2024'] / data['revenue_2024'] * 100) if data['revenue_2024'] != 0 else 0
-        
-        return {
-            'revenue_growth': revenue_growth,
-            'gross_margin_2024': gross_margin_2024,
-            'net_margin_2024': net_margin_2024,
-            'revenue_2024_m': data['revenue_2024'] / 1000000,
-            'net_profit_2024_m': data['net_profit_2024'] / 1000000,
-        }
-
-    def create_revenue_trend_chart(self):
-        """Create revenue trend visualization"""
-        if not self.processed_data:
-            return go.Figure()
-            
-        fig = go.Figure()
-        
-        years = [2023, 2024]
-        revenues = [self.processed_data['revenue_2023']/1000000, 
-                   self.processed_data['revenue_2024']/1000000]
-        net_profits = [self.processed_data['net_profit_2023']/1000000,
-                      self.processed_data['net_profit_2024']/1000000]
-        
-        fig.add_trace(go.Scatter(x=years, y=revenues, mode='lines+markers',
-                                name='Revenue (M NOK)', line=dict(color='#1f77b4', width=3)))
-        fig.add_trace(go.Scatter(x=years, y=net_profits, mode='lines+markers',
-                                name='Net Profit (M NOK)', line=dict(color='#ff7f0e', width=3)))
-        
-        fig.update_layout(title='Revenue vs Profit Trend', xaxis_title='Year', 
-                         yaxis_title='Amount (M NOK)', height=400)
-        return fig
-
-    def create_financial_health_radar(self):
-        """Create financial health radar chart"""
-        metrics = self.calculate_metrics()
-        
-        categories = ['Revenue Growth', 'Gross Margin', 'Net Margin', 'Profitability', 'Efficiency']
-        values = [
-            min(metrics.get('revenue_growth', 0) / 5, 100),  # Scale revenue growth
-            metrics.get('gross_margin_2024', 0),
-            max(metrics.get('net_margin_2024', 0), 0),
-            70,  # Sample value
-            65   # Sample value
-        ]
-        
-        fig = go.Figure()
-        fig.add_trace(go.Scatterpolar(
-            r=values,
-            theta=categories,
-            fill='toself',
-            name='Financial Health'
-        ))
-        
-        fig.update_layout(
-            polar=dict(
-                radialaxis=dict(visible=True, range=[0, 100])
-            ),
-            title="Financial Health Score",
-            height=400
-        )
-        return fig
-
-def main():
-    st.title("📊 FinanceGPT Analyzer")
-    st.markdown("### Professional Financial Analysis Dashboard")
-    
-    # Initialize analyzer
-    if st.session_state.analyzer is None:
-        st.session_state.analyzer = FinanceAnalyzer()
-    
-    analyzer = st.session_state.analyzer
-    
-    # Sidebar navigation
-    with st.sidebar:
-        st.header("Navigation")
-        page = st.selectbox("Choose Analysis Page", [
-            "🏠 Dashboard",
-            "💰 Income Analysis", 
-            "🏛️ Balance Sheet Analysis",
-            "💸 Cash Flow Analysis",
-            "📊 Financial Ratios Hub",
-            "🤖 AI Finance Assistant"
-        ])
-        
-        st.header("Data Upload")
-        uploaded_file = st.file_uploader("Upload CSV file", type=['csv'])
-        
-        if st.button("Use Sample Data"):
-            analyzer.data = analyzer.load_sample_data()
-            analyzer.process_financial_data(analyzer.data)
-            st.session_state.data_loaded = True
-            st.success("Sample data loaded!")
-            st.rerun()
-        
-        if uploaded_file is not None:
-            try:
-                df = pd.read_csv(uploaded_file)
-                analyzer.data = df
-                analyzer.process_financial_data(df)
-                st.session_state.data_loaded = True
-                st.success("Data uploaded successfully!")
-                st.rerun()
-            except Exception as e:
-                st.error(f"Error loading file: {e}")
-    
-    # Main content based on selected page
-    if page == "🏠 Dashboard":
-        dashboard_page(analyzer)
-    elif page == "💰 Income Analysis":
-        income_analysis_page(analyzer)
-    elif page == "🏛️ Balance Sheet Analysis":
-        balance_sheet_page(analyzer)
-    elif page == "💸 Cash Flow Analysis":
-        cash_flow_page(analyzer)
-    elif page == "📊 Financial Ratios Hub":
-        ratios_page(analyzer)
-    elif page == "🤖 AI Finance Assistant":
-        ai_assistant_page(analyzer)
-
-def dashboard_page(analyzer):
-    """Main dashboard page"""
-    st.header("📊 Financial Dashboard")
-    
-    if analyzer.data is None:
-        st.warning("Please upload data or use sample data to begin analysis.")
-        return
-    
-    metrics = analyzer.calculate_metrics()
-    
-    # Key metrics cards
-    col1, col2, col3, col4 = st.columns(4)
-    
-    with col1:
-        st.markdown("""
-        <div class="metric-card">
-            <h3>💰 Revenue</h3>
-            <h2>{:.1f}M NOK</h2>
-            <p>+{:.0f}% 🔥</p>
-        </div>
-        """.format(metrics.get('revenue_2024_m', 0), metrics.get('revenue_growth', 0)), 
-        unsafe_allow_html=True)
-    
-    with col2:
-        st.markdown("""
-        <div class="metric-card">
-            <h3>📈 Net Profit</h3>
-            <h2>{:.1f}M NOK</h2>
-            <p>Profitable ✅</p>
-        </div>
-        """.format(metrics.get('net_profit_2024_m', 0)), unsafe_allow_html=True)
-    
-    with col3:
-        st.markdown("""
-        <div class="metric-card">
-            <h3>📊 Gross Margin</h3>
-            <h2>{:.1f}%</h2>
-            <p>Healthy 💪</p>
-        </div>
-        """.format(metrics.get('gross_margin_2024', 0)), unsafe_allow_html=True)
-    
-    with col4:
-        st.markdown("""
-        <div class="metric-card">
-            <h3>🎯 Net Margin</h3>
-            <h2>{:.1f}%</h2>
-            <p>Strong 📈</p>
-        </div>
-        """.format(metrics.get('net_margin_2024', 0)), unsafe_allow_html=True)
-    
-    # Charts section
-    col1, col2 = st.columns(2)
-    
-    with col1:
-        st.plotly_chart(analyzer.create_revenue_trend_chart(), use_container_width=True)
-    
-    with col2:
-        st.plotly_chart(analyzer.create_financial_health_radar(), use_container_width=True)
-    
-    # Quick insights
-    st.markdown("""
-    <div class="insight-box">
-        <h4>🎯 Quick Insights</h4>
-        <ul>
-            <li>✅ Revenue growth of {:.0f}% indicates explosive business development</li>
-            <li>💡 Net profit margin of {:.1f}% shows strong profitability</li>
-            <li>📈 Gross margin of {:.1f}% demonstrates healthy pricing power</li>
-        </ul>
-    </div>
-    """.format(
-        metrics.get('revenue_growth', 0),
-        metrics.get('net_margin_2024', 0),
-        metrics.get('gross_margin_2024', 0)
-    ), unsafe_allow_html=True)
-
-def income_analysis_page(analyzer):
-    """Income statement analysis page"""
-    st.header("💰 Income Analysis")
-    
-    if analyzer.data is None:
-        st.warning("Please upload data to begin analysis.")
-        return
-    
-    # Revenue analysis
-    st.subheader("📈 Revenue Trend Analysis")
-    st.plotly_chart(analyzer.create_revenue_trend_chart(), use_container_width=True)
-    
-    # Cost structure
-    st.subheader("🥧 Cost Structure Analysis")
-    if analyzer.processed_data:
-        data = analyzer.processed_data
-        costs = ['Cost of Goods Sold', 'Operating Expenses', 'Financial Expenses']
-        values = [data['cogs_2024'], 2000000, 234567]  # Sample values
-        
-        fig = px.pie(values=values, names=costs, title="Cost Breakdown 2024")
-        st.plotly_chart(fig, use_container_width=True)
-    
-    # Profitability metrics
-    st.subheader("📊 Profitability Indicators")
-    metrics = analyzer.calculate_metrics()
-    
-    col1, col2, col3 = st.columns(3)
-    with col1:
-        st.metric("Gross Margin", f"{metrics.get('gross_margin_2024', 0):.1f}%")
-    with col2:
-        st.metric("Net Margin", f"{metrics.get('net_margin_2024', 0):.1f}%")
-    with col3:
-        st.metric("Revenue Growth", f"{metrics.get('revenue_growth', 0):.1f}%")
-
-def balance_sheet_page(analyzer):
-    """Balance sheet analysis page"""
-    st.header("🏛️ Balance Sheet Analysis")
-    
-    if analyzer.data is None:
-        st.warning("Please upload balance sheet data to begin analysis.")
-        return
-    
-    st.info("Balance sheet analysis requires additional data. Please upload complete financial statements.")
-    
-    # Sample asset structure chart
-    assets = ['Current Assets', 'Fixed Assets', 'Intangible Assets']
-    values = [45, 35, 20]
-    
-    fig = px.pie(values=values, names=assets, title="Asset Structure")
-    st.plotly_chart(fig, use_container_width=True)
-
-def cash_flow_page(analyzer):
-    """Cash flow analysis page"""
-    st.header("💸 Cash Flow Analysis")
-    
-    if analyzer.data is None:
-        st.warning("Please upload cash flow data to begin analysis.")
-        return
-    
-    st.info("Cash flow analysis requires additional data. Please upload complete cash flow statements.")
-    
-    # Sample cash flow chart
-    categories = ['Operating CF', 'Investing CF', 'Financing CF']
-    values = [5000000, -2000000, -1000000]
-    
-    fig = go.Figure(go.Waterfall(
-        name="Cash Flow", orientation="v",
-        measure=["relative", "relative", "relative"],
-        x=categories, y=values,
-        text=[f"{v/1000000:.1f}M" for v in values]
-    ))
-    fig.update_layout(title="Cash Flow Waterfall")
-    st.plotly_chart(fig, use_container_width=True)
-
-def ratios_page(analyzer):
-    """Financial ratios analysis page"""
-    st.header("📊 Financial Ratios Hub")
-    
-    if analyzer.data is None:
-        st.warning("Please upload data to calculate ratios.")
-        return
-    
-    # Ratio categories
-    col1, col2, col3, col4 = st.columns(4)
-    
-    with col1:
-        if st.button("Profitability"):
-            st.session_state.ratio_category = "profitability"
-    with col2:
-        if st.button("Liquidity"):
-            st.session_state.ratio_category = "liquidity"
-    with col3:
-        if st.button("Efficiency"):
-            st.session_state.ratio_category = "efficiency"
-    with col4:
-        if st.button("Growth"):
-            st.session_state.ratio_category = "growth"
-    
-    # Display ratios based on selection
-    metrics = analyzer.calculate_metrics()
-    
-    st.subheader("Key Financial Ratios")
-    
-    col1, col2, col3 = st.columns(3)
-    with col1:
-        st.metric("Gross Profit Margin", f"{metrics.get('gross_margin_2024', 0):.1f}%", "A+")
-    with col2:
-        st.metric("Net Profit Margin", f"{metrics.get('net_margin_2024', 0):.1f}%", "A")
-    with col3:
-        st.metric("Revenue Growth", f"{metrics.get('revenue_growth', 0):.1f}%", "A+")
-
-def ai_assistant_page(analyzer):
-    """AI finance assistant page"""
-    st.header("🤖 AI Finance Assistant")
-    
-    if analyzer.data is None:
-        st.warning("Please upload data to enable AI analysis.")
-        return
-    
-    # Chat interface
-    st.subheader("💬 Ask Your Financial Questions")
-    
-    # Predefined questions
-    col1, col2 = st.columns(2)
-    with col1:
-        if st.button("Analyze my financial health"):
-            st.session_state.ai_query = "financial_health"
-        if st.button("Find the biggest risks"):
-            st.session_state.ai_query = "risks"
-    
-    with col2:
-        if st.button("Give investment advice"):
-            st.session_state.ai_query = "investment"
-        if st.button("Create improvement plan"):
-            st.session_state.ai_query = "improvement"
-    
-    # Text input for custom questions
-    user_question = st.text_input("Or ask your own question:")
-    
-    if user_question or 'ai_query' in st.session_state:
-        metrics = analyzer.calculate_metrics()
-        
-        # Simple AI-like responses based on data
-        if user_question or st.session_state.get('ai_query') == 'financial_health':
-            st.markdown("""
-            <div class="insight-box">
-                <h4>🎯 Financial Health Analysis</h4>
-                <p>Based on your financial data:</p>
-                <ul>
-                    <li>✅ <strong>Revenue Growth:</strong> {:.0f}% growth shows strong market performance</li>
-                    <li>✅ <strong>Profitability:</strong> {:.1f}% net margin indicates healthy operations</li>
-                    <li>📊 <strong>Overall Rating:</strong> A- (Strong financial position)</li>
-                </ul>
-            </div>
-            """.format(
-                metrics.get('revenue_growth', 0),
-                metrics.get('net_margin_2024', 0)
-            ), unsafe_allow_html=True)
-        
-        # Clear the session state
-        if 'ai_query' in st.session_state:
-            del st.session_state.ai_query
-
-if __name__ == "__main__":
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import numpy as np
+from datetime import datetime
+import io
+
+# Initialize session state
+if 'data_loaded' not in st.session_state:
+    st.session_state.data_loaded = False
+if 'analyzer' not in st.session_state:
+    st.session_state.analyzer = None
+
+# Page configuration
+st.set_page_config(
+    page_title="📊 FinanceGPT Analyzer",
+    page_icon="📊",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Custom CSS for better styling
+st.markdown("""
+<style>
+    .metric-card {
+        background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
+        padding: 1rem;
+        border-radius: 10px;
+        color: white;
+        text-align: center;
+        margin: 0.5rem 0;
+    }
+    .insight-box {
+        background: #f8f9fa;
+        padding: 1rem;
+        border-left: 4px solid #007bff;
+        border-radius: 5px;
+        margin: 1rem 0;
+    }
+    .warning-box {
+        background: #fff3cd;
+        padding: 1rem;
+        border-left: 4px solid #ffc107;
+        border-radius: 5px;
+        margin: 1rem 0;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+class FinanceAnalyzer:
+    def __init__(self):
+        self.data = None
+        self.processed_data = {}
+        
+    def load_sample_data(self):
+        """Load sample financial data"""
+        sample_data = {
+            'Year': [2024, 2024, 2024, 2024, 2024, 2024, 2024, 2024, 2024, 2024,
+                    2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023],
+            'Statement_Type': ['Income Statement'] * 10 + ['Income Statement'] * 10,
+            'Account_Name_Norwegian': ['Salgsinntekt', 'Varekostnad', 'Bruttoresultat', 'Lønnskostnad', 
+                                     'Andre driftskostnader', 'Driftsresultat', 'Finansinntekter', 
+                                     'Finanskostnader', 'Ordinært resultat før skatt', 'Årsresultat'] * 2,
+            'Account_Name_English': ['Sales Revenue', 'Cost of Goods Sold', 'Gross Profit', 'Salary Costs',
+                                   'Other Operating Expenses', 'Operating Result', 'Financial Income',
+                                   'Financial Expenses', 'Profit Before Tax', 'Net Profit'] * 2,
+            '2024_Amount_NOK': [25107008, -15064205, 10042803, -3521456, -1987234, 4534113, 123456, 
+                               -234567, 4422002, 3537602, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            '2023_Amount_NOK': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4891891, -2934535, 1957356, -1234567,
+                               -654321, 68468, 45678, -123456, -9310, -7448]
+        }
+        return pd.DataFrame(sample_data)
+    
+    def process_financial_data(self, df):
+        """Process uploaded financial data"""
+        self.data = df
+        
+        # Create pivot tables for easier analysis
+        income_2024 = df[df['Year'] == 2024]['2024_Amount_NOK'].values
+        income_2023 = df[df['Year'] == 2023]['2023_Amount_NOK'].values
+        accounts = df[df['Year'] == 2024]['Account_Name_English'].values
+        
+        self.processed_data = {
+            'revenue_2024': income_2024[0] if len(income_2024) > 0 else 0,
+            'revenue_2023': income_2023[0] if len(income_2023) > 0 else 0,
+            'net_profit_2024': income_2024[9] if len(income_2024) > 9 else 0,
+            'net_profit_2023': income_2023[9] if len(income_2023) > 9 else 0,
+            'cogs_2024': abs(income_2024[1]) if len(income_2024) > 1 else 0,
+            'cogs_2023': abs(income_2023[1]) if len(income_2023) > 1 else 0,
+            'operating_profit_2024': income_2024[5] if len(income_2024) > 5 else 0,
+            'operating_profit_2023': income_2023[5] if len(income_2023) > 5 else 0,
+        }
+        
+    def calculate_metrics(self):
+        """Calculate key financial metrics"""
+        if not self.processed_data:
+            return {}
+            
+        data = self.processed_data
+        
+        # Growth rates
+        revenue_growth = ((data['revenue_2024'] - data['revenue_2023']) / 
+                         abs(data['revenue_2023']) * 100) if data['revenue_2023'] != 0 else 0
+        
+        # Profitability ratios
+        gross_margin_2024 = ((data['revenue_2024'] - data['cogs_2024']) / 
+                            data['revenue_2024'] * 100) if data['revenue_2024'] != 0 else 0
+        net_margin_2024 = (data['net_profit_2024'] / data['revenue_2024'] * 100) if data['revenue_2024'] != 0 else 0
+        
+        return {
+            'revenue_growth': revenue_growth,
+            'gross_margin_2024': gross_margin_2024,
+            'net_margin_2024': net_margin_2024,
+            'revenue_2024_m': data['revenue_2024'] / 1000000,
+            'net_profit_2024_m': data['net_profit_2024'] / 1000000,
+        }
+
+    def create_revenue_trend_chart(self):
+        """Create revenue trend visualization"""
+        if not self.processed_data:
+            return go.Figure().add_annotation(text="No data available", 
+                                            xref="paper", yref="paper",
+                                            x=0.5, y=0.5, showarrow=False)
+            
+        fig = go.Figure()
+        
+        years = [2023, 2024]
+        revenues = [self.processed_data['revenue_2023']/1000000, 
+                   self.processed_data['revenue_2024']/1000000]
+        net_profits = [self.processed_data['net_profit_2023']/1000000,
+                      self.processed_data['net_profit_2024']/1000000]
+        
+        fig.add_trace(go.Scatter(x=years, y=revenues, mode='lines+markers',
+                                name='Revenue (M NOK)', line=dict(color='#1f77b4', width=3)))
+        fig.add_trace(go.Scatter(x=years, y=net_profits, mode='lines+markers',
+                                name='Net Profit (M NOK)', line=dict(color='#ff7f0e', width=3)))
+        
+        fig.update_layout(title='Revenue vs Profit Trend', xaxis_title='Year', 
+                         yaxis_title='Amount (M NOK)', height=400)
+        return fig
+
+    def create_financial_health_radar(self):
+        """Create financial health radar chart"""
+        metrics = self.calculate_metrics()
+        
+        categories = ['Revenue Growth', 'Gross Margin', 'Net Margin', 'Profitability', 'Efficiency']
+        values = [
+            min(metrics.get('revenue_growth', 0) / 5, 100),  # Scale revenue growth
+            metrics.get('gross_margin_2024', 0),
+            max(metrics.get('net_margin_2024', 0), 0),
+            70,  # Sample value
+            65   # Sample value
+        ]
+        
+        fig = go.Figure()
+        fig.add_trace(go.Scatterpolar(
+            r=values,
+            theta=categories,
+            fill='toself',
+            name='Financial Health'
+        ))
+        
+        fig.update_layout(
+            polar=dict(
+                radialaxis=dict(visible=True, range=[0, 100])
+            ),
+            title="Financial Health Score",
+            height=400
+        )
+        return fig
+
+def main():
+    st.title("📊 FinanceGPT Analyzer")
+    st.markdown("### Professional Financial Analysis Dashboard")
+    
+    # Initialize analyzer
+    if st.session_state.analyzer is None:
+        st.session_state.analyzer = FinanceAnalyzer()
+    
+    analyzer = st.session_state.analyzer
+    
+    # Sidebar navigation
+    with st.sidebar:
+        st.header("Navigation")
+        page = st.selectbox("Choose Analysis Page", [
+            "🏠 Dashboard",
+            "💰 Income Analysis", 
+            "🏛️ Balance Sheet Analysis",
+            "💸 Cash Flow Analysis",
+            "📊 Financial Ratios Hub",
+            "🤖 AI Finance Assistant"
+        ])
+        
+        st.header("Data Upload")
+        uploaded_file = st.file_uploader("Upload CSV file", type=['csv'])
+        
+        if st.button("Use Sample Data"):
+            analyzer.data = analyzer.load_sample_data()
+            analyzer.process_financial_data(analyzer.data)
+            st.session_state.data_loaded = True
+            st.success("Sample data loaded!")
+            st.rerun()
+        
+        if uploaded_file is not None:
+            try:
+                df = pd.read_csv(uploaded_file)
+                analyzer.data = df
+                analyzer.process_financial_data(df)
+                st.session_state.data_loaded = True
+                st.success("Data uploaded successfully!")
+                st.rerun()
+            except Exception as e:
+                st.error(f"Error loading file: {e}")
+    
+    # Main content based on selected page
+    if page == "🏠 Dashboard":
+        dashboard_page(analyzer)
+    elif page == "💰 Income Analysis":
+        income_analysis_page(analyzer)
+    elif page == "🏛️ Balance Sheet Analysis":
+        balance_sheet_page(analyzer)
+    elif page == "💸 Cash Flow Analysis":
+        cash_flow_page(analyzer)
+    elif page == "📊 Financial Ratios Hub":
+        ratios_page(analyzer)
+    elif page == "🤖 AI Finance Assistant":
+        ai_assistant_page(analyzer)
+
+def dashboard_page(analyzer):
+    """Main dashboard page"""
+    st.header("📊 Financial Dashboard")
+    
+    if analyzer.data is None or not st.session_state.data_loaded:
+        st.warning("Please upload data or use sample data to begin analysis.")
+        st.info("👈 Use the sidebar to upload your CSV file or click 'Use Sample Data'")
+        return
+    
+    metrics = analyzer.calculate_metrics()
+    
+    # Key metrics cards
+    col1, col2, col3, col4 = st.columns(4)
+    
+    with col1:
+        st.markdown("""
+        <div class="metric-card">
+            <h3>💰 Revenue</h3>
+            <h2>{:.1f}M NOK</h2>
+            <p>+{:.0f}% 🔥</p>
+        </div>
+        """.format(metrics.get('revenue_2024_m', 0), metrics.get('revenue_growth', 0)), 
+        unsafe_allow_html=True)
+    
+    with col2:
+        st.markdown("""
+        <div class="metric-card">
+            <h3>📈 Net Profit</h3>
+            <h2>{:.1f}M NOK</h2>
+            <p>Profitable ✅</p>
+        </div>
+        """.format(metrics.get('net_profit_2024_m', 0)), unsafe_allow_html=True)
+    
+    with col3:
+        st.markdown("""
+        <div class="metric-card">
+            <h3>📊 Gross Margin</h3>
+            <h2>{:.1f}%</h2>
+            <p>Healthy 💪</p>
+        </div>
+        """.format(metrics.get('gross_margin_2024', 0)), unsafe_allow_html=True)
+    
+    with col4:
+        st.markdown("""
+        <div class="metric-card">
+            <h3>🎯 Net Margin</h3>
+            <h2>{:.1f}%</h2>
+            <p>Strong 📈</p>
+        </div>
+        """.format(metrics.get('net_margin_2024', 0)), unsafe_allow_html=True)
+    
+    # Charts section
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.plotly_chart(analyzer.create_revenue_trend_chart(), use_container_width=True)
+    
+    with col2:
+        st.plotly_chart(analyzer.create_financial_health_radar(), use_container_width=True)
+    
+    # Quick insights
+    st.markdown("""
+    <div class="insight-box">
+        <h4>🎯 Quick Insights</h4>
+        <ul>
+            <li>✅ Revenue growth of {:.0f}% indicates explosive business development</li>
+            <li>💡 Net profit margin of {:.1f}% shows strong profitability</li>
+            <li>📈 Gross margin of {:.1f}% demonstrates healthy pricing power</li>
+        </ul>
+    </div>
+    """.format(
+        metrics.get('revenue_growth', 0),
+        metrics.get('net_margin_2024', 0),
+        metrics.get('gross_margin_2024', 0)
+    ), unsafe_allow_html=True)
+
+def income_analysis_page(analyzer):
+    """Income statement analysis page"""
+    st.header("💰 Income Analysis")
+    
+    if analyzer.data is None or not st.session_state.data_loaded:
+        st.warning("Please upload data to begin analysis.")
+        st.info("👈 Use the sidebar to upload your CSV file or click 'Use Sample Data'")
+        return
+    
+    # Revenue analysis
+    st.subheader("📈 Revenue Trend Analysis")
+    st.plotly_chart(analyzer.create_revenue_trend_chart(), use_container_width=True)
+    
+    # Cost structure
+    st.subheader("🥧 Cost Structure Analysis")
+    if analyzer.processed_data:
+        data = analyzer.processed_data
+        costs = ['Cost of Goods Sold', 'Operating Expenses', 'Financial Expenses']
+        values = [data['cogs_2024'], 2000000, 234567]  # Sample values
+        
+        fig = px.pie(values=values, names=costs, title="Cost Breakdown 2024")
+        st.plotly_chart(fig, use_container_width=True)
+    
+    # Profitability metrics
+    st.subheader("📊 Profitability Indicators")
+    metrics = analyzer.calculate_metrics()
+    
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        st.metric("Gross Margin", f"{metrics.get('gross_margin_2024', 0):.1f}%")
+    with col2:
+        st.metric("Net Margin", f"{metrics.get('net_margin_2024', 0):.1f}%")
+    with col3:
+        st.metric("Revenue Growth", f"{metrics.get('revenue_growth', 0):.1f}%")
+
+def balance_sheet_page(analyzer):
+    """Balance sheet analysis page"""
+    st.header("🏛️ Balance Sheet Analysis")
+    
+    if analyzer.data is None or not st.session_state.data_loaded:
+        st.warning("Please upload balance sheet data to begin analysis.")
+        st.info("👈 Use the sidebar to upload your CSV file or click 'Use Sample Data'")
+        return
+    
+    st.info("Balance sheet analysis requires additional data. Please upload complete financial statements.")
+    
+    # Sample asset structure chart
+    assets = ['Current Assets', 'Fixed Assets', 'Intangible Assets']
+    values = [45, 35, 20]
+    
+    fig = px.pie(values=values, names=assets, title="Asset Structure")
+    st.plotly_chart(fig, use_container_width=True)
+
+def cash_flow_page(analyzer):
+    """Cash flow analysis page"""
+    st.header("💸 Cash Flow Analysis")
+    
+    if analyzer.data is None or not st.session_state.data_loaded:
+        st.warning("Please upload cash flow data to begin analysis.")
+        st.info("👈 Use the sidebar to upload your CSV file or click 'Use Sample Data'")
+        return
+    
+    st.info("Cash flow analysis requires additional data. Please upload complete cash flow statements.")
+    
+    # Sample cash flow chart
+    categories = ['Operating CF', 'Investing CF', 'Financing CF']
+    values = [5000000, -2000000, -1000000]
+    
+    fig = go.Figure(go.Waterfall(
+        name="Cash Flow", orientation="v",
+        measure=["relative", "relative", "relative"],
+        x=categories, y=values,
+        text=[f"{v/1000000:.1f}M" for v in values]
+    ))
+    fig.update_layout(title="Cash Flow Waterfall")
+    st.plotly_chart(fig, use_container_width=True)
+
+def ratios_page(analyzer):
+    """Financial ratios analysis page"""
+    st.header("📊 Financial Ratios Hub")
+    
+    if analyzer.data is None or not st.session_state.data_loaded:
+        st.warning("Please upload data to calculate ratios.")
+        st.info("👈 Use the sidebar to upload your CSV file or click 'Use Sample Data'")
+        return
+    
+    # Ratio categories
+    col1, col2, col3, col4 = st.columns(4)
+    
+    with col1:
+        if st.button("Profitability"):
+            st.session_state.ratio_category = "profitability"
+    with col2:
+        if st.button("Liquidity"):
+            st.session_state.ratio_category = "liquidity"
+    with col3:
+        if st.button("Efficiency"):
+            st.session_state.ratio_category = "efficiency"
+    with col4:
+        if st.button("Growth"):
+            st.session_state.ratio_category = "growth"
+    
+    # Display ratios based on selection
+    metrics = analyzer.calculate_metrics()
+    
+    st.subheader("Key Financial Ratios")
+    
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        st.metric("Gross Profit Margin", f"{metrics.get('gross_margin_2024', 0):.1f}%", "A+")
+    with col2:
+        st.metric("Net Profit Margin", f"{metrics.get('net_margin_2024', 0):.1f}%", "A")
+    with col3:
+        st.metric("Revenue Growth", f"{metrics.get('revenue_growth', 0):.1f}%", "A+")
+
+def ai_assistant_page(analyzer):
+    """AI finance assistant page"""
+    st.header("🤖 AI Finance Assistant")
+    
+    if analyzer.data is None or not st.session_state.data_loaded:
+        st.warning("Please upload data to enable AI analysis.")
+        st.info("👈 Use the sidebar to upload your CSV file or click 'Use Sample Data'")
+        return
+    
+    # Chat interface
+    st.subheader("💬 Ask Your Financial Questions")
+    
+    # Predefined questions
+    col1, col2 = st.columns(2)
+    with col1:
+        if st.button("Analyze my financial health"):
+            st.session_state.ai_query = "financial_health"
+        if st.button("Find the biggest risks"):
+            st.session_state.ai_query = "risks"
+    
+    with col2:
+        if st.button("Give investment advice"):
+            st.session_state.ai_query = "investment"
+        if st.button("Create improvement plan"):
+            st.session_state.ai_query = "improvement"
+    
+    # Text input for custom questions
+    user_question = st.text_input("Or ask your own question:")
+    
+    if user_question or 'ai_query' in st.session_state:
+        metrics = analyzer.calculate_metrics()
+        
+        # Simple AI-like responses based on data
+        if user_question or st.session_state.get('ai_query') == 'financial_health':
+            st.markdown("""
+            <div class="insight-box">
+                <h4>🎯 Financial Health Analysis</h4>
+                <p>Based on your financial data:</p>
+                <ul>
+                    <li>✅ <strong>Revenue Growth:</strong> {:.0f}% growth shows strong market performance</li>
+                    <li>✅ <strong>Profitability:</strong> {:.1f}% net margin indicates healthy operations</li>
+                    <li>📊 <strong>Overall Rating:</strong> A- (Strong financial position)</li>
+                </ul>
+            </div>
+            """.format(
+                metrics.get('revenue_growth', 0),
+                metrics.get('net_margin_2024', 0)
+            ), unsafe_allow_html=True)
+        
+        # Clear the session state
+        if 'ai_query' in st.session_state:
+            del st.session_state.ai_query
+
+if __name__ == "__main__":
     main()

docker-compose.yml

@@ -1,21 +1,21 @@
-version: '3.8'
-
-services:
-  finance-analyzer:
-    build: .
-    ports:
-      - "7860:7860"
-    environment:
-      - STREAMLIT_SERVER_PORT=7860
-      - STREAMLIT_SERVER_ADDRESS=0.0.0.0
-      - STREAMLIT_BROWSER_GATHER_USAGE_STATS=false
-      - STREAMLIT_GLOBAL_DEVELOPMENT_MODE=false
-    volumes:
-      - ./data:/app/data  # Optional: for persistent data storage
-    restart: unless-stopped
-    healthcheck:
-      test: ["CMD", "curl", "-f", "http://localhost:7860/_stcore/health"]
-      interval: 30s
-      timeout: 10s
-      retries: 3
+version: '3.8'
+
+services:
+  finance-analyzer:
+    build: .
+    ports:
+      - "7860:7860"
+    environment:
+      - STREAMLIT_SERVER_PORT=7860
+      - STREAMLIT_SERVER_ADDRESS=0.0.0.0
+      - STREAMLIT_BROWSER_GATHER_USAGE_STATS=false
+      - STREAMLIT_GLOBAL_DEVELOPMENT_MODE=false
+    volumes:
+      - ./data:/app/data  # Optional: for persistent data storage
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:7860/_stcore/health"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
       start_period: 40s

requirements.txt

@@ -1,5 +1,5 @@
-streamlit>=1.28.0
-pandas>=2.0.0
-plotly>=5.15.0
-numpy>=1.24.0
+streamlit>=1.28.0
+pandas>=2.0.0
+plotly>=5.15.0
+numpy>=1.24.0
 openpyxl>=3.1.0