Update app/main.py

app/main.py

@@ -1,646 +1,644 @@
-"""
-Smart Analytics Copilot - Complete Version
-With Export, OpenAI, Save/Load, Chart Customization, Power BI Export
-"""
-
-import streamlit as st
-import pandas as pd
-import os
-from datetime import datetime
-from dotenv import load_dotenv
-
-# Load environment variables
-load_dotenv()
-
-from data_processor import DataProcessor
-from analyzer import Analyzer
-from insight_generator import InsightGenerator
-from dashboard import DashboardGenerator
-from query_engine import QueryEngine
-from export_utils import ExportUtils
-from session_manager import SessionManager
-from chart_customizer import ChartCustomizer
-
-# Page config
-st.set_page_config(
-    page_title="Smart Analytics Copilot",
-    page_icon="🚀",
-    layout="wide",
-    initial_sidebar_state="expanded"
-)
-
-# ============ DARK THEME CSS ============
-st.markdown("""
-<style>
-    /* Main background */
-    .stApp {
-        background-color: #0a0e17 !important;
-    }
-    
-    /* All text - light color */
-    .stMarkdown, .stMarkdown p, .stMarkdown div, .stMarkdown span, 
-    .stText, p, div, span, label {
-        color: #e8e8e8 !important;
-    }
-    
-    /* Headers */
-    h1, h2, h3, h4, h5, h6 {
-        color: #00ff9d !important;
-        font-weight: 600 !important;
-    }
-    
-    /* Main header */
-    .main-header {
-        font-size: 2.8rem;
-        font-weight: bold;
-        background: linear-gradient(135deg, #00ff9d 0%, #00d4ff 100%);
-        -webkit-background-clip: text;
-        -webkit-text-fill-color: transparent;
-        margin-bottom: 1rem;
-        text-align: center;
-    }
-    
-    /* Sidebar */
-    .css-1d391kg, .stSidebar, .sidebar-content {
-        background-color: #111827 !important;
-    }
-    
-    /* Metrics */
-    div[data-testid="stMetricValue"] {
-        color: #00ff9d !important;
-        font-size: 2rem !important;
-        font-weight: bold !important;
-    }
-    
-    div[data-testid="stMetricLabel"] {
-        color: #a0aec0 !important;
-        font-size: 0.9rem !important;
-    }
-    
-    /* Tabs */
-    .stTabs [data-baseweb="tab-list"] {
-        gap: 4px;
-        background-color: #111827;
-        border-radius: 10px;
-        padding: 6px;
-    }
-    
-    .stTabs [data-baseweb="tab"] {
-        background-color: #1f2937;
-        border-radius: 8px;
-        padding: 8px 24px;
-        color: #e8e8e8 !important;
-    }
-    
-    .stTabs [aria-selected="true"] {
-        background: linear-gradient(135deg, #00ff9d 0%, #00d4ff 100%) !important;
-        color: #0a0e17 !important;
-        font-weight: bold;
-    }
-    
-    /* Buttons */
-    .stButton button {
-        background: linear-gradient(135deg, #00ff9d 0%, #00d4ff 100%) !important;
-        color: #0a0e17 !important;
-        font-weight: bold !important;
-        border: none !important;
-        border-radius: 8px !important;
-    }
-    
-    /* File uploader */
-    .stFileUploader {
-        background-color: #1f2937 !important;
-        border: 2px dashed #374151 !important;
-        border-radius: 12px !important;
-    }
-    
-    /* Expander */
-    .streamlit-expanderHeader {
-        background-color: #1f2937 !important;
-        color: #00ff9d !important;
-        border-radius: 8px;
-    }
-    
-    /* Success/Info/Warning boxes */
-    .stAlert {
-        background-color: #1f2937 !important;
-        border: 1px solid #374151 !important;
-        border-radius: 10px !important;
-    }
-    
-    .stAlert p, .stAlert div {
-        color: #e8e8e8 !important;
-    }
-    
-    /* Dataframe */
-    .stDataFrame {
-        background-color: #111827 !important;
-    }
-    
-    .stDataFrame thead th {
-        background-color: #1f2937 !important;
-        color: #00ff9d !important;
-    }
-    
-    /* Text input */
-    .stTextInput input {
-        background-color: #1f2937 !important;
-        color: #e8e8e8 !important;
-        border: 1px solid #374151 !important;
-        border-radius: 8px !important;
-    }
-    
-    /* Select box */
-    .stSelectbox div[data-baseweb="select"] {
-        background-color: #1f2937 !important;
-        border-color: #374151 !important;
-    }
-    
-    /* Download button */
-    .stDownloadButton button {
-        background: linear-gradient(135deg, #00ff9d 0%, #00d4ff 100%) !important;
-        color: #0a0e17 !important;
-    }
-</style>
-""", unsafe_allow_html=True)
-
-# Initialize session state
-if 'data_loaded' not in st.session_state:
-    st.session_state.data_loaded = False
-if 'df' not in st.session_state:
-    st.session_state.df = None
-if 'schema' not in st.session_state:
-    st.session_state.schema = None
-if 'analysis' not in st.session_state:
-    st.session_state.analysis = None
-if 'insights' not in st.session_state:
-    st.session_state.insights = None
-if 'charts' not in st.session_state:
-    st.session_state.charts = None
-if 'use_openai' not in st.session_state:
-    st.session_state.use_openai = False
-
-# Initialize managers
-session_mgr = SessionManager()
-
-
-def main():
-    st.markdown('<div class="main-header">🚀 Smart Analytics Copilot</div>', unsafe_allow_html=True)
-    st.caption("✨ Upload any CSV/JSON - AI analyzes, visualizes, and answers questions")
-    st.markdown("---")
-    
-    # Sidebar
-    with st.sidebar:
-        st.markdown("### 📁 Data Source")
-        
-        # Data source selection
-        source = st.radio("Choose data source:", ["📤 Upload File", "💾 Load Saved Session"])
-        
-        if source == "📤 Upload File":
-            uploaded_file = st.file_uploader("Choose CSV or JSON", type=['csv', 'json'])
-            if uploaded_file and not st.session_state.data_loaded:
-                with st.spinner("🔄 Processing your data..."):
-                    process_data(uploaded_file)
-        else:
-            # Load saved sessions
-            sessions = session_mgr.list_sessions()
-            if sessions:
-                session_names = [s['name'] for s in sessions]
-                selected_session = st.selectbox("Select saved session:", session_names)
-                if st.button("📂 Load Session"):
-                    with st.spinner("Loading..."):
-                        load_session(selected_session)
-            else:
-                st.info("No saved sessions found")
-        
-        st.markdown("---")
-        
-        # Settings
-        with st.expander("⚙️ Settings"):
-            st.session_state.use_openai = st.checkbox("Use OpenAI (better insights)", 
-                                                      value=st.session_state.use_openai)
-            if st.session_state.use_openai:
-                api_key = st.text_input("OpenAI API Key:", type="password")
-                if api_key:
-                    os.environ['OPENAI_API_KEY'] = api_key
-                    st.success("API Key set!")
-        
-        st.markdown("---")
-        
-        # Export section (only if data loaded)
-        if st.session_state.data_loaded:
-            st.markdown("### 💾 Export Options")
-            export_utils = ExportUtils(st.session_state.df)
-            
-            export_format = st.selectbox("Export format:", 
-                                         ["CSV", "Excel", "JSON", "Power BI CSV", "Power BI ZIP (Complete)"])
-            
-            if st.button("📥 Download"):
-                if export_format == "CSV":
-                    data = export_utils.to_csv()
-                    mime = "text/csv"
-                    ext = "csv"
-                elif export_format == "Excel":
-                    data = export_utils.to_excel()
-                    mime = "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
-                    ext = "xlsx"
-                elif export_format == "JSON":
-                    data = export_utils.to_json()
-                    mime = "application/json"
-                    ext = "json"
-                elif export_format == "Power BI CSV":
-                    data = export_utils.to_powerbi_ready()
-                    mime = "text/csv"
-                    ext = "csv"
-                else:  # Power BI ZIP (Complete)
-                    data = export_utils.to_powerbi_zip()
-                    mime = "application/zip"
-                    ext = "zip"
-                
-                st.download_button(
-                    label="✅ Click to Download",
-                    data=data,
-                    file_name=f"export_{datetime.now().strftime('%Y%m%d_%H%M%S')}.{ext}",
-                    mime=mime
-                )
-            
-            # Save session button
-            st.markdown("---")
-            if st.button("💾 Save Current Session"):
-                name, path = session_mgr.save_session(st.session_state.df, st.session_state.schema)
-                st.success(f"✅ Session saved as: {name}")
-    
-    # Main content
-    if st.session_state.data_loaded:
-        tab1, tab2, tab3, tab4, tab5 = st.tabs([
-            "📊 Dashboard", "💡 AI Insights", "🎨 Custom Charts", "🔍 Query", "📋 Data"
-        ])
-        
-        with tab1:
-            show_dashboard()
-        
-        with tab2:
-            show_insights()
-        
-        with tab3:
-            show_chart_customizer()
-        
-        with tab4:
-            show_query_interface()
-        
-        with tab5:
-            show_data_preview()
-    
-    else:
-        show_welcome()
-
-
-def process_data(uploaded_file):
-    """Process uploaded data"""
-    try:
-        processor = DataProcessor()
-        st.session_state.df = processor.load_from_upload(uploaded_file)
-        st.session_state.df = processor.preprocess()
-        st.session_state.schema = processor.detect_schema()
-        
-        analyzer = Analyzer(st.session_state.df, st.session_state.schema)
-        st.session_state.analysis = analyzer.run_full_analysis()
-        
-        # Use OpenAI if enabled
-        api_key = os.environ.get('OPENAI_API_KEY')
-        insight_gen = InsightGenerator(use_openai=st.session_state.use_openai, api_key=api_key)
-        st.session_state.insights = insight_gen.generate_insights(
-            st.session_state.df, 
-            st.session_state.schema, 
-            st.session_state.analysis
-        )
-        
-        dashboard_gen = DashboardGenerator(st.session_state.df, st.session_state.schema)
-        st.session_state.charts = dashboard_gen.generate_all_charts()
-        
-        st.session_state.data_loaded = True
-        st.success(f"✅ Successfully loaded {len(st.session_state.df):,} rows with {len(st.session_state.df.columns)} columns")
-        st.balloons()
-        st.rerun()
-    except Exception as e:
-        st.error(f"Error: {e}")
-
-
-def load_session(session_name):
-    """Load saved session and regenerate insights"""
-    session = session_mgr.load_session(session_name)
-    if session:
-        st.session_state.df = session['df']
-        st.session_state.schema = session['schema']
-        
-        # Regenerate analysis and insights for loaded session
-        with st.spinner("🔄 Regenerating analysis..."):
-            analyzer = Analyzer(st.session_state.df, st.session_state.schema)
-            st.session_state.analysis = analyzer.run_full_analysis()
-            
-            # Regenerate insights
-            api_key = os.environ.get('OPENAI_API_KEY')
-            insight_gen = InsightGenerator(use_openai=st.session_state.use_openai, api_key=api_key)
-            st.session_state.insights = insight_gen.generate_insights(
-                st.session_state.df, 
-                st.session_state.schema, 
-                st.session_state.analysis
-            )
-            
-            # Regenerate charts
-            dashboard_gen = DashboardGenerator(st.session_state.df, st.session_state.schema)
-            st.session_state.charts = dashboard_gen.generate_all_charts()
-        
-        st.session_state.data_loaded = True
-        st.success(f"✅ Loaded session: {session_name}")
-        st.rerun()
-    else:
-        st.error("Failed to load session")
-
-
-def show_dashboard():
-    """Display dashboard"""
-    st.markdown("### 📈 Key Metrics")
-    st.markdown("---")
-    
-    # Check if data exists
-    if st.session_state.df is None:
-        st.warning("No data loaded. Please upload a file first.")
-        return
-    
-    # Display metrics
-    if st.session_state.schema['numeric']:
-        cols = st.columns(min(4, len(st.session_state.schema['numeric'])))
-        for idx, col in enumerate(st.session_state.schema['numeric'][:4]):
-            with cols[idx]:
-                total = st.session_state.df[col].sum()
-                avg = st.session_state.df[col].mean()
-                st.metric(
-                    label=f"💰 {col.upper()}",
-                    value=f"{total:,.0f}",
-                    delta=f"Avg: {avg:,.0f}"
-                )
-    
-    st.markdown("---")
-    st.markdown("### 📊 Visualizations")
-    
-    if st.session_state.charts:
-        for chart in st.session_state.charts[:4]:
-            st.plotly_chart(chart['figure'], use_container_width=True)
-    else:
-        st.info("No charts available. Try uploading data first.")
-    
-    st.markdown("---")
-    st.markdown("### 📋 Summary Statistics")
-    if st.session_state.schema['numeric']:
-        summary = st.session_state.df[st.session_state.schema['numeric']].describe()
-        st.dataframe(summary, use_container_width=True)
-
-
-def show_insights():
-    """Display AI insights"""
-    st.markdown("### 🧠 AI-Powered Insights")
-    st.markdown("Here's what we discovered in your data:")
-    st.markdown("---")
-    
-    # Check if insights exist
-    if st.session_state.insights is None:
-        st.info("💡 Insights will appear after data is analyzed.")
-        return
-    
-    for insight in st.session_state.insights:
-        if "Dataset" in insight:
-            st.info(f"📊 {insight}")
-        elif "correlation" in insight.lower():
-            st.success(f"✅ {insight}")
-        elif "skewed" in insight.lower():
-            st.warning(f"📈 {insight}")
-        elif "Recommendation" in insight:
-            st.info(f"💡 {insight}")
-        else:
-            st.markdown(f"• {insight}")
-    
-    # Power BI template section
-    st.markdown("---")
-    with st.expander("📊 Power BI Resources"):
-        export_utils = ExportUtils(st.session_state.df)
-        
-        col1, col2 = st.columns(2)
-        
-        with col1:
-            # Show DAX template
-            template = export_utils.get_powerbi_template()
-            st.code(template, language="dax")
-            
-            st.download_button(
-                label="📥 Download DAX Template",
-                data=template,
-                file_name="powerbi_measures.dax",
-                mime="text/plain"
-            )
-        
-        with col2:
-            # Show instructions
-            instructions = """
-**Power BI Import Steps:**
-
-1. **Export Data**: Use sidebar to export as "Power BI CSV"
-2. **Open Power BI Desktop**
-3. **Get Data** → **Text/CSV**
-4. **Select your exported CSV**
-5. **Click Load**
-6. **Copy DAX measures** from above
-7. **Create visuals** using the measures
-            """
-            st.info(instructions)
-
-
-def show_chart_customizer():
-    """Show chart customization interface"""
-    st.markdown("### 🎨 Custom Chart Builder")
-    st.markdown("Create your own custom visualizations")
-    st.markdown("---")
-    
-    customizer = ChartCustomizer(st.session_state.df)
-    available_charts = customizer.get_available_charts()
-    
-    col1, col2, col3 = st.columns([1, 1, 1])
-    
-    with col1:
-        chart_type = st.selectbox("Chart Type:", available_charts)
-    
-    with col2:
-        # Get appropriate columns
-        if 'Histogram' in chart_type or 'Box' in chart_type:
-            columns = st.session_state.schema['numeric']
-            if not columns:
-                columns = list(st.session_state.df.select_dtypes(include=['number']).columns)
-        elif 'Pie' in chart_type or 'Bar' in chart_type:
-            columns = st.session_state.schema['categorical']
-            if not columns:
-                columns = list(st.session_state.df.select_dtypes(include=['object']).columns)
-        else:
-            columns = list(st.session_state.df.columns)
-        
-        if columns:
-            x_col = st.selectbox("X-Axis / Category:", columns)
-        else:
-            x_col = None
-            st.warning("No suitable columns found")
-    
-    with col3:
-        # For charts that need Y-axis
-        if any(t in chart_type for t in ['Line', 'Scatter', 'Bar']) and 'Histogram' not in chart_type:
-            y_cols = ['None'] + st.session_state.schema['numeric']
-            y_col = st.selectbox("Y-Axis / Value:", y_cols)
-            y_col = None if y_col == 'None' else y_col
-        else:
-            y_col = None
-    
-    # Color column (optional)
-    color_cols = ['None'] + st.session_state.schema['categorical']
-    color_col = st.selectbox("Color By (optional):", color_cols)
-    color_col = None if color_col == 'None' else color_col
-    
-    # Title
-    title = st.text_input("Chart Title:", value=f"{chart_type} of {x_col if x_col else 'data'}")
-    
-    if st.button("🎨 Generate Chart", use_container_width=True):
-        if x_col:
-            with st.spinner("Creating chart..."):
-                fig = customizer.create_chart(chart_type, x_col, y_col, color_col, title)
-                if fig:
-                    st.plotly_chart(fig, use_container_width=True)
-                    
-                    # Download chart button
-                    try:
-                        st.download_button(
-                            label="📸 Download as PNG",
-                            data=fig.to_image(format="png"),
-                            file_name="custom_chart.png",
-                            mime="image/png"
-                        )
-                    except:
-                        st.info("💡 Install kaleido for PNG export: `pip install kaleido`")
-                else:
-                    st.error("Could not create chart. Try different settings.")
-        else:
-            st.error("Please select a column for X-Axis")
-
-
-def show_query_interface():
-    """Natural language query interface"""
-    st.markdown("### 💬 Natural Language Query")
-    st.markdown("Ask any question about your data in plain English:")
-    st.markdown("---")
-    
-    query_engine = QueryEngine(st.session_state.df, st.session_state.schema)
-    
-    # Example questions
-    with st.expander("🔍 View Example Questions"):
-        if st.session_state.schema['numeric']:
-            example_col = st.session_state.schema['numeric'][0]
-            st.markdown(f"• 'Statistics {example_col}'")
-            st.markdown(f"• 'Total {example_col}'")
-            st.markdown(f"• 'Average {example_col}'")
-        
-        if st.session_state.schema['categorical'] and st.session_state.schema['numeric']:
-            st.markdown(f"• 'Top 5 {st.session_state.schema['categorical'][0]} by {st.session_state.schema['numeric'][0]}'")
-        
-        st.markdown("• 'Summary statistics'")
-        st.markdown("• 'Show me the data'")
-    
-    st.markdown("---")
-    
-    question = st.text_input("Ask a question:", placeholder="e.g., What is the average of time_in_hospital?")
-    
-    if question:
-        with st.spinner("🤔 Analyzing your question..."):
-            answer = query_engine.answer_question(question)
-            st.markdown("### ✅ Answer")
-            st.success(answer)
-
-
-def show_data_preview():
-    """Show data preview and info with better formatting"""
-    st.markdown("### 📋 Data Preview")
-    st.markdown("---")
-    
-    col1, col2, col3 = st.columns(3)
-    with col1:
-        st.metric("📊 Total Rows", f"{len(st.session_state.df):,}")
-    with col2:
-        st.metric("📋 Total Columns", len(st.session_state.df.columns))
-    with col3:
-        memory = st.session_state.df.memory_usage(deep=True).sum() / 1024**2
-        st.metric("💾 Memory Usage", f"{memory:.2f} MB")
-    
-    st.markdown("---")
-    st.markdown("### 📄 Data Sample (First 100 rows)")
-    
-    # Create a copy for display
-    display_df = st.session_state.df.head(100).copy()
-    
-    # Clean datetime columns for better display
-    for col in display_df.columns:
-        if 'datetime' in col.lower() or 'date' in col.lower() or 'time' in col.lower():
-            try:
-                display_df[col] = pd.to_datetime(display_df[col]).dt.strftime('%Y-%m-%d %H:%M:%S')
-            except:
-                pass
-    
-    st.dataframe(display_df, use_container_width=True)
-    
-    st.markdown("---")
-    st.markdown("### 📊 Column Information")
-    
-    col_info = pd.DataFrame({
-        'Column': st.session_state.df.columns,
-        'Type': st.session_state.df.dtypes.astype(str),
-        'Non-Null': st.session_state.df.count().values,
-        'Nulls': st.session_state.df.isnull().sum().values,
-        'Unique': st.session_state.df.nunique().values
-    })
-    st.dataframe(col_info, use_container_width=True)
-
-
-def show_welcome():
-    """Welcome screen"""
-    st.markdown("""
-    <div style="text-align: center; padding: 2rem; background: linear-gradient(135deg, #111827 0%, #0a0e17 100%); border-radius: 20px; margin: 2rem 0;">
-        <h2 style="color: #00ff9d;">🚀 Welcome to Smart Analytics Copilot</h2>
-        <p style="font-size: 1.1rem;">Upload any CSV or JSON file and let AI analyze it instantly</p>
-        <hr>
-        <p>👈 <strong>Get Started</strong>: Upload a file or load a saved session from the sidebar</p>
-    </div>
-    """, unsafe_allow_html=True)
-    
-    col1, col2, col3 = st.columns(3)
-    
-    with col1:
-        st.markdown("""
-        <div style="background: linear-gradient(135deg, #1f2937 0%, #111827 100%); padding: 1.5rem; border-radius: 15px; text-align: center;">
-            <h3 style="color: #00ff9d;">📊 Auto Dashboard</h3>
-            <p>Smart charts based on your data</p>
-        </div>
-        """, unsafe_allow_html=True)
-    
-    with col2:
-        st.markdown("""
-        <div style="background: linear-gradient(135deg, #1f2937 0%, #111827 100%); padding: 1.5rem; border-radius: 15px; text-align: center;">
-            <h3 style="color: #00ff9d;">💡 AI Insights</h3>
-            <p>Natural language explanations</p>
-        </div>
-        """, unsafe_allow_html=True)
-    
-    with col3:
-        st.markdown("""
-        <div style="background: linear-gradient(135deg, #1f2937 0%, #111827 100%); padding: 1.5rem; border-radius: 15px; text-align: center;">
-            <h3 style="color: #00ff9d;">🎨 Custom Charts</h3>
-            <p>Build your own visualizations</p>
-        </div>
-        """, unsafe_allow_html=True)
-
-
-if __name__ == "__main__":
+"""Smart Analytics Copilot - Complete Version
+With Export, OpenAI, Save/Load, Chart Customization, Power BI Export"""
+
+import streamlit as st
+import pandas as pd
+import os
+from datetime import datetime
+from dotenv import load_dotenv
+
+#Load environment variables
+load_dotenv()
+
+from data_processor import DataProcessor
+from analyzer import Analyzer
+from insight_generator import InsightGenerator
+from dashboard import DashboardGenerator
+from query_engine import QueryEngine
+from export_utils import ExportUtils
+from session_manager import SessionManager
+from chart_customizer import ChartCustomizer
+
+#Page config
+st.set_page_config(
+    page_title="Smart Analytics Copilot",
+    page_icon="🚀",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# ============ DARK THEME CSS ============
+st.markdown("""
+<style>
+    /* Main background */
+    .stApp {
+        background-color: #0a0e17 !important;
+    }
+    
+    /* All text - light color */
+    .stMarkdown, .stMarkdown p, .stMarkdown div, .stMarkdown span, 
+    .stText, p, div, span, label {
+        color: #e8e8e8 !important;
+    }
+    
+    /* Headers */
+    h1, h2, h3, h4, h5, h6 {
+        color: #00ff9d !important;
+        font-weight: 600 !important;
+    }
+    
+    /* Main header */
+    .main-header {
+        font-size: 2.8rem;
+        font-weight: bold;
+        background: linear-gradient(135deg, #00ff9d 0%, #00d4ff 100%);
+        -webkit-background-clip: text;
+        -webkit-text-fill-color: transparent;
+        margin-bottom: 1rem;
+        text-align: center;
+    }
+    
+    /* Sidebar */
+    .css-1d391kg, .stSidebar, .sidebar-content {
+        background-color: #111827 !important;
+    }
+    
+    /* Metrics */
+    div[data-testid="stMetricValue"] {
+        color: #00ff9d !important;
+        font-size: 2rem !important;
+        font-weight: bold !important;
+    }
+    
+    div[data-testid="stMetricLabel"] {
+        color: #a0aec0 !important;
+        font-size: 0.9rem !important;
+    }
+    
+    /* Tabs */
+    .stTabs [data-baseweb="tab-list"] {
+        gap: 4px;
+        background-color: #111827;
+        border-radius: 10px;
+        padding: 6px;
+    }
+    
+    .stTabs [data-baseweb="tab"] {
+        background-color: #1f2937;
+        border-radius: 8px;
+        padding: 8px 24px;
+        color: #e8e8e8 !important;
+    }
+    
+    .stTabs [aria-selected="true"] {
+        background: linear-gradient(135deg, #00ff9d 0%, #00d4ff 100%) !important;
+        color: #0a0e17 !important;
+        font-weight: bold;
+    }
+    
+    /* Buttons */
+    .stButton button {
+        background: linear-gradient(135deg, #00ff9d 0%, #00d4ff 100%) !important;
+        color: #0a0e17 !important;
+        font-weight: bold !important;
+        border: none !important;
+        border-radius: 8px !important;
+    }
+    
+    /* File uploader */
+    .stFileUploader {
+        background-color: #1f2937 !important;
+        border: 2px dashed #374151 !important;
+        border-radius: 12px !important;
+    }
+    
+    /* Expander */
+    .streamlit-expanderHeader {
+        background-color: #1f2937 !important;
+        color: #00ff9d !important;
+        border-radius: 8px;
+    }
+    
+    /* Success/Info/Warning boxes */
+    .stAlert {
+        background-color: #1f2937 !important;
+        border: 1px solid #374151 !important;
+        border-radius: 10px !important;
+    }
+    
+    .stAlert p, .stAlert div {
+        color: #e8e8e8 !important;
+    }
+    
+    /* Dataframe */
+    .stDataFrame {
+        background-color: #111827 !important;
+    }
+    
+    .stDataFrame thead th {
+        background-color: #1f2937 !important;
+        color: #00ff9d !important;
+    }
+    
+    /* Text input */
+    .stTextInput input {
+        background-color: #1f2937 !important;
+        color: #e8e8e8 !important;
+        border: 1px solid #374151 !important;
+        border-radius: 8px !important;
+    }
+    
+    /* Select box */
+    .stSelectbox div[data-baseweb="select"] {
+        background-color: #1f2937 !important;
+        border-color: #374151 !important;
+    }
+    
+    /* Download button */
+    .stDownloadButton button {
+        background: linear-gradient(135deg, #00ff9d 0%, #00d4ff 100%) !important;
+        color: #0a0e17 !important;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Initialize session state
+if 'data_loaded' not in st.session_state:
+    st.session_state.data_loaded = False
+if 'df' not in st.session_state:
+    st.session_state.df = None
+if 'schema' not in st.session_state:
+    st.session_state.schema = None
+if 'analysis' not in st.session_state:
+    st.session_state.analysis = None
+if 'insights' not in st.session_state:
+    st.session_state.insights = None
+if 'charts' not in st.session_state:
+    st.session_state.charts = None
+if 'use_openai' not in st.session_state:
+    st.session_state.use_openai = False
+
+# Initialize managers
+session_mgr = SessionManager()
+
+
+def main():
+    st.markdown('<div class="main-header">🚀 Smart Analytics Copilot</div>', unsafe_allow_html=True)
+    st.caption("✨ Upload any CSV/JSON - AI analyzes, visualizes, and answers questions")
+    st.markdown("---")
+    
+    # Sidebar
+    with st.sidebar:
+        st.markdown("### 📁 Data Source")
+        
+        # Data source selection
+        source = st.radio("Choose data source:", ["📤 Upload File", "💾 Load Saved Session"])
+        
+        if source == "📤 Upload File":
+            uploaded_file = st.file_uploader("Choose CSV or JSON", type=['csv', 'json'])
+            if uploaded_file and not st.session_state.data_loaded:
+                with st.spinner("🔄 Processing your data..."):
+                    process_data(uploaded_file)
+        else:
+            # Load saved sessions
+            sessions = session_mgr.list_sessions()
+            if sessions:
+                session_names = [s['name'] for s in sessions]
+                selected_session = st.selectbox("Select saved session:", session_names)
+                if st.button("📂 Load Session"):
+                    with st.spinner("Loading..."):
+                        load_session(selected_session)
+            else:
+                st.info("No saved sessions found")
+        
+        st.markdown("---")
+        
+        # Settings
+        with st.expander("⚙️ Settings"):
+            st.session_state.use_openai = st.checkbox("Use OpenAI (better insights)", 
+                                                      value=st.session_state.use_openai)
+            if st.session_state.use_openai:
+                api_key = st.text_input("OpenAI API Key:", type="password")
+                if api_key:
+                    os.environ['OPENAI_API_KEY'] = api_key
+                    st.success("API Key set!")
+        
+        st.markdown("---")
+        
+        # Export section (only if data loaded)
+        if st.session_state.data_loaded:
+            st.markdown("### 💾 Export Options")
+            export_utils = ExportUtils(st.session_state.df)
+            
+            export_format = st.selectbox("Export format:", 
+                                         ["CSV", "Excel", "JSON", "Power BI CSV", "Power BI ZIP (Complete)"])
+            
+            if st.button("📥 Download"):
+                if export_format == "CSV":
+                    data = export_utils.to_csv()
+                    mime = "text/csv"
+                    ext = "csv"
+                elif export_format == "Excel":
+                    data = export_utils.to_excel()
+                    mime = "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+                    ext = "xlsx"
+                elif export_format == "JSON":
+                    data = export_utils.to_json()
+                    mime = "application/json"
+                    ext = "json"
+                elif export_format == "Power BI CSV":
+                    data = export_utils.to_powerbi_ready()
+                    mime = "text/csv"
+                    ext = "csv"
+                else:  # Power BI ZIP (Complete)
+                    data = export_utils.to_powerbi_zip()
+                    mime = "application/zip"
+                    ext = "zip"
+                
+                st.download_button(
+                    label="✅ Click to Download",
+                    data=data,
+                    file_name=f"export_{datetime.now().strftime('%Y%m%d_%H%M%S')}.{ext}",
+                    mime=mime
+                )
+            
+            # Save session button
+            st.markdown("---")
+            if st.button("💾 Save Current Session"):
+                name, path = session_mgr.save_session(st.session_state.df, st.session_state.schema)
+                st.success(f"✅ Session saved as: {name}")
+    
+    # Main content
+    if st.session_state.data_loaded:
+        tab1, tab2, tab3, tab4, tab5 = st.tabs([
+            "📊 Dashboard", "💡 AI Insights", "🎨 Custom Charts", "🔍 Query", "📋 Data"
+        ])
+        
+        with tab1:
+            show_dashboard()
+        
+        with tab2:
+            show_insights()
+        
+        with tab3:
+            show_chart_customizer()
+        
+        with tab4:
+            show_query_interface()
+        
+        with tab5:
+            show_data_preview()
+    
+    else:
+        show_welcome()
+
+
+def process_data(uploaded_file):
+    """Process uploaded data"""
+    try:
+        processor = DataProcessor()
+        st.session_state.df = processor.load_from_upload(uploaded_file)
+        st.session_state.df = processor.preprocess()
+        st.session_state.schema = processor.detect_schema()
+        
+        analyzer = Analyzer(st.session_state.df, st.session_state.schema)
+        st.session_state.analysis = analyzer.run_full_analysis()
+        
+        # Use OpenAI if enabled
+        api_key = os.environ.get('OPENAI_API_KEY')
+        insight_gen = InsightGenerator(use_openai=st.session_state.use_openai, api_key=api_key)
+        st.session_state.insights = insight_gen.generate_insights(
+            st.session_state.df, 
+            st.session_state.schema, 
+            st.session_state.analysis
+        )
+        
+        dashboard_gen = DashboardGenerator(st.session_state.df, st.session_state.schema)
+        st.session_state.charts = dashboard_gen.generate_all_charts()
+        
+        st.session_state.data_loaded = True
+        st.success(f"✅ Successfully loaded {len(st.session_state.df):,} rows with {len(st.session_state.df.columns)} columns")
+        st.balloons()
+        st.rerun()
+    except Exception as e:
+        st.error(f"Error: {e}")
+
+
+def load_session(session_name):
+    """Load saved session and regenerate insights"""
+    session = session_mgr.load_session(session_name)
+    if session:
+        st.session_state.df = session['df']
+        st.session_state.schema = session['schema']
+        
+        # Regenerate analysis and insights for loaded session
+        with st.spinner("🔄 Regenerating analysis..."):
+            analyzer = Analyzer(st.session_state.df, st.session_state.schema)
+            st.session_state.analysis = analyzer.run_full_analysis()
+            
+            # Regenerate insights
+            api_key = os.environ.get('OPENAI_API_KEY')
+            insight_gen = InsightGenerator(use_openai=st.session_state.use_openai, api_key=api_key)
+            st.session_state.insights = insight_gen.generate_insights(
+                st.session_state.df, 
+                st.session_state.schema, 
+                st.session_state.analysis
+            )
+            
+            # Regenerate charts
+            dashboard_gen = DashboardGenerator(st.session_state.df, st.session_state.schema)
+            st.session_state.charts = dashboard_gen.generate_all_charts()
+        
+        st.session_state.data_loaded = True
+        st.success(f"✅ Loaded session: {session_name}")
+        st.rerun()
+    else:
+        st.error("Failed to load session")
+
+
+def show_dashboard():
+    """Display dashboard"""
+    st.markdown("### 📈 Key Metrics")
+    st.markdown("---")
+    
+    # Check if data exists
+    if st.session_state.df is None:
+        st.warning("No data loaded. Please upload a file first.")
+        return
+    
+    # Display metrics
+    if st.session_state.schema['numeric']:
+        cols = st.columns(min(4, len(st.session_state.schema['numeric'])))
+        for idx, col in enumerate(st.session_state.schema['numeric'][:4]):
+            with cols[idx]:
+                total = st.session_state.df[col].sum()
+                avg = st.session_state.df[col].mean()
+                st.metric(
+                    label=f"💰 {col.upper()}",
+                    value=f"{total:,.0f}",
+                    delta=f"Avg: {avg:,.0f}"
+                )
+    
+    st.markdown("---")
+    st.markdown("### 📊 Visualizations")
+    
+    if st.session_state.charts:
+        for chart in st.session_state.charts[:4]:
+            st.plotly_chart(chart['figure'], use_container_width=True)
+    else:
+        st.info("No charts available. Try uploading data first.")
+    
+    st.markdown("---")
+    st.markdown("### 📋 Summary Statistics")
+    if st.session_state.schema['numeric']:
+        summary = st.session_state.df[st.session_state.schema['numeric']].describe()
+        st.dataframe(summary, use_container_width=True)
+
+
+def show_insights():
+    """Display AI insights"""
+    st.markdown("### 🧠 AI-Powered Insights")
+    st.markdown("Here's what we discovered in your data:")
+    st.markdown("---")
+    
+    # Check if insights exist
+    if st.session_state.insights is None:
+        st.info("💡 Insights will appear after data is analyzed.")
+        return
+    
+    for insight in st.session_state.insights:
+        if "Dataset" in insight:
+            st.info(f"📊 {insight}")
+        elif "correlation" in insight.lower():
+            st.success(f"✅ {insight}")
+        elif "skewed" in insight.lower():
+            st.warning(f"📈 {insight}")
+        elif "Recommendation" in insight:
+            st.info(f"💡 {insight}")
+        else:
+            st.markdown(f"• {insight}")
+    
+    # Power BI template section
+    st.markdown("---")
+    with st.expander("📊 Power BI Resources"):
+        export_utils = ExportUtils(st.session_state.df)
+        
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            # Show DAX template
+            template = export_utils.get_powerbi_template()
+            st.code(template, language="dax")
+            
+            st.download_button(
+                label="📥 Download DAX Template",
+                data=template,
+                file_name="powerbi_measures.dax",
+                mime="text/plain"
+            )
+        
+        with col2:
+            # Show instructions
+            instructions = """
+**Power BI Import Steps:**
+
+1. **Export Data**: Use sidebar to export as "Power BI CSV"
+2. **Open Power BI Desktop**
+3. **Get Data** → **Text/CSV**
+4. **Select your exported CSV**
+5. **Click Load**
+6. **Copy DAX measures** from above
+7. **Create visuals** using the measures
+            """
+            st.info(instructions)
+
+
+def show_chart_customizer():
+    """Show chart customization interface"""
+    st.markdown("### 🎨 Custom Chart Builder")
+    st.markdown("Create your own custom visualizations")
+    st.markdown("---")
+    
+    customizer = ChartCustomizer(st.session_state.df)
+    available_charts = customizer.get_available_charts()
+    
+    col1, col2, col3 = st.columns([1, 1, 1])
+    
+    with col1:
+        chart_type = st.selectbox("Chart Type:", available_charts)
+    
+    with col2:
+        # Get appropriate columns
+        if 'Histogram' in chart_type or 'Box' in chart_type:
+            columns = st.session_state.schema['numeric']
+            if not columns:
+                columns = list(st.session_state.df.select_dtypes(include=['number']).columns)
+        elif 'Pie' in chart_type or 'Bar' in chart_type:
+            columns = st.session_state.schema['categorical']
+            if not columns:
+                columns = list(st.session_state.df.select_dtypes(include=['object']).columns)
+        else:
+            columns = list(st.session_state.df.columns)
+        
+        if columns:
+            x_col = st.selectbox("X-Axis / Category:", columns)
+        else:
+            x_col = None
+            st.warning("No suitable columns found")
+    
+    with col3:
+        # For charts that need Y-axis
+        if any(t in chart_type for t in ['Line', 'Scatter', 'Bar']) and 'Histogram' not in chart_type:
+            y_cols = ['None'] + st.session_state.schema['numeric']
+            y_col = st.selectbox("Y-Axis / Value:", y_cols)
+            y_col = None if y_col == 'None' else y_col
+        else:
+            y_col = None
+    
+    # Color column (optional)
+    color_cols = ['None'] + st.session_state.schema['categorical']
+    color_col = st.selectbox("Color By (optional):", color_cols)
+    color_col = None if color_col == 'None' else color_col
+    
+    # Title
+    title = st.text_input("Chart Title:", value=f"{chart_type} of {x_col if x_col else 'data'}")
+    
+    if st.button("🎨 Generate Chart", use_container_width=True):
+        if x_col:
+            with st.spinner("Creating chart..."):
+                fig = customizer.create_chart(chart_type, x_col, y_col, color_col, title)
+                if fig:
+                    st.plotly_chart(fig, use_container_width=True)
+                    
+                    # Download chart button
+                    try:
+                        st.download_button(
+                            label="📸 Download as PNG",
+                            data=fig.to_image(format="png"),
+                            file_name="custom_chart.png",
+                            mime="image/png"
+                        )
+                    except:
+                        st.info("💡 Install kaleido for PNG export: `pip install kaleido`")
+                else:
+                    st.error("Could not create chart. Try different settings.")
+        else:
+            st.error("Please select a column for X-Axis")
+
+
+def show_query_interface():
+    """Natural language query interface"""
+    st.markdown("### 💬 Natural Language Query")
+    st.markdown("Ask any question about your data in plain English:")
+    st.markdown("---")
+    
+    query_engine = QueryEngine(st.session_state.df, st.session_state.schema)
+    
+    # Example questions
+    with st.expander("🔍 View Example Questions"):
+        if st.session_state.schema['numeric']:
+            example_col = st.session_state.schema['numeric'][0]
+            st.markdown(f"• 'Statistics {example_col}'")
+            st.markdown(f"• 'Total {example_col}'")
+            st.markdown(f"• 'Average {example_col}'")
+        
+        if st.session_state.schema['categorical'] and st.session_state.schema['numeric']:
+            st.markdown(f"• 'Top 5 {st.session_state.schema['categorical'][0]} by {st.session_state.schema['numeric'][0]}'")
+        
+        st.markdown("• 'Summary statistics'")
+        st.markdown("• 'Show me the data'")
+    
+    st.markdown("---")
+    
+    question = st.text_input("Ask a question:", placeholder="e.g., What is the average of time_in_hospital?")
+    
+    if question:
+        with st.spinner("🤔 Analyzing your question..."):
+            answer = query_engine.answer_question(question)
+            st.markdown("### ✅ Answer")
+            st.success(answer)
+
+
+def show_data_preview():
+    """Show data preview and info with better formatting"""
+    st.markdown("### 📋 Data Preview")
+    st.markdown("---")
+    
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        st.metric("📊 Total Rows", f"{len(st.session_state.df):,}")
+    with col2:
+        st.metric("📋 Total Columns", len(st.session_state.df.columns))
+    with col3:
+        memory = st.session_state.df.memory_usage(deep=True).sum() / 1024**2
+        st.metric("💾 Memory Usage", f"{memory:.2f} MB")
+    
+    st.markdown("---")
+    st.markdown("### 📄 Data Sample (First 100 rows)")
+    
+    # Create a copy for display
+    display_df = st.session_state.df.head(100).copy()
+    
+    # Clean datetime columns for better display
+    for col in display_df.columns:
+        if 'datetime' in col.lower() or 'date' in col.lower() or 'time' in col.lower():
+            try:
+                display_df[col] = pd.to_datetime(display_df[col]).dt.strftime('%Y-%m-%d %H:%M:%S')
+            except:
+                pass
+    
+    st.dataframe(display_df, use_container_width=True)
+    
+    st.markdown("---")
+    st.markdown("### 📊 Column Information")
+    
+    col_info = pd.DataFrame({
+        'Column': st.session_state.df.columns,
+        'Type': st.session_state.df.dtypes.astype(str),
+        'Non-Null': st.session_state.df.count().values,
+        'Nulls': st.session_state.df.isnull().sum().values,
+        'Unique': st.session_state.df.nunique().values
+    })
+    st.dataframe(col_info, use_container_width=True)
+
+
+def show_welcome():
+    """Welcome screen"""
+    st.markdown("""
+    <div style="text-align: center; padding: 2rem; background: linear-gradient(135deg, #111827 0%, #0a0e17 100%); border-radius: 20px; margin: 2rem 0;">
+        <h2 style="color: #00ff9d;">🚀 Welcome to Smart Analytics Copilot</h2>
+        <p style="font-size: 1.1rem;">Upload any CSV or JSON file and let AI analyze it instantly</p>
+        <hr>
+        <p>👈 <strong>Get Started</strong>: Upload a file or load a saved session from the sidebar</p>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    col1, col2, col3 = st.columns(3)
+    
+    with col1:
+        st.markdown("""
+        <div style="background: linear-gradient(135deg, #1f2937 0%, #111827 100%); padding: 1.5rem; border-radius: 15px; text-align: center;">
+            <h3 style="color: #00ff9d;">📊 Auto Dashboard</h3>
+            <p>Smart charts based on your data</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col2:
+        st.markdown("""
+        <div style="background: linear-gradient(135deg, #1f2937 0%, #111827 100%); padding: 1.5rem; border-radius: 15px; text-align: center;">
+            <h3 style="color: #00ff9d;">💡 AI Insights</h3>
+            <p>Natural language explanations</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col3:
+        st.markdown("""
+        <div style="background: linear-gradient(135deg, #1f2937 0%, #111827 100%); padding: 1.5rem; border-radius: 15px; text-align: center;">
+            <h3 style="color: #00ff9d;">🎨 Custom Charts</h3>
+            <p>Build your own visualizations</p>
+        </div>
+        """, unsafe_allow_html=True)
+
+
+if __name__ == "__main__":
     main()