Update app.py

app.py

@@ -1,61 +1,1548 @@
-# debug_app.py - Replace your app.py temporarily with this
 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 io
+import base64
+from datetime import datetime
+import json
 import os
 import sys
+from pathlib import Path
+import time
 
-st.title("🔍 AIDA Debug Mode")
 
-# Show current directory and files
-st.write("**Current working directory:**", os.getcwd())
-st.write("**Files in current directory:**")
-files = os.listdir('.')
-for file in files:
-    st.write(f"- {file}")
+# Add the current directory to path to import our agent
+sys.path.append(str(Path(__file__).parent))
 
-# Show Python path
-st.write("**Python sys.path:**")
-for path in sys.path:
-    st.write(f"- {path}")
+try:
+    from data_analysis_agent import DataAnalysisAgent, DataAnalysisConfig
+except ImportError:
+    st.error("❌ Please ensure data_analysis_agent.py is in the same directory")
+    st.info("Download both files and place them in the same folder")
+    st.stop()
 
-# Test if data_analysis_agent.py exists
-agent_exists = os.path.exists('data_analysis_agent.py')
-st.write(f"**data_analysis_agent.py exists:** {agent_exists}")
+# Page configuration
+st.set_page_config(
+    page_title="AI Data Analysis Agent",
+    page_icon="🤖",
+    layout="wide",
+    initial_sidebar_state="expanded",
+    menu_items={
+        'Get Help': 'https://github.com/yourusername/ai-data-analysis-agent',
+        'Report a bug': "https://github.com/yourusername/ai-data-analysis-agent/issues",
+        'About': "# AI Data Analysis Agent\nPowered by Llama 3 & LangGraph"
+    }
+)
 
-if agent_exists:
-    st.write("**File size:**", os.path.getsize('data_analysis_agent.py'), "bytes")
+# Custom CSS for beautiful styling
+st.markdown("""
+<style>
+    /* Import Google Fonts */
+    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap');
+    
+    /* Global Styles */
+    .main .block-container {
+        padding-top: 2rem;
+        max-width: 1200px;
+    }
+    
+    /* Main Header */
+    .main-header {
+        font-family: 'Inter', sans-serif;
+        font-size: 3.5rem;
+        font-weight: 700;
+        text-align: center;
+        margin: 2rem 0;
+        background: linear-gradient(135deg, #1e40af 0%, #3b82f6 50%, #06b6d4 100%);
+        -webkit-background-clip: text;
+        -webkit-text-fill-color: transparent;
+        background-clip: text;
+        text-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+    }
+    
+    /* Subtitle */
+    .subtitle {
+        font-family: 'Inter', sans-serif;
+        font-size: 1.2rem;
+        text-align: center;
+        color: #64748b;
+        margin-bottom: 3rem;
+        font-weight: 400;
+    }
+    
+    /* Feature Cards */
+    .feature-card {
+        background: linear-gradient(145deg, #ffffff 0%, #f8fafc 100%);
+        border: 1px solid #e2e8f0;
+        border-radius: 16px;
+        padding: 2rem;
+        margin: 1rem 0;
+        box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -1px rgba(0, 0, 0, 0.06);
+        transition: all 0.3s ease;
+        height: 100%;
+    }
+    
+    .feature-card:hover {
+        transform: translateY(-4px);
+        box-shadow: 0 20px 25px -5px rgba(0, 0, 0, 0.1), 0 10px 10px -5px rgba(0, 0, 0, 0.04);
+    }
+    
+    .feature-icon {
+        font-size: 3rem;
+        margin-bottom: 1rem;
+        display: block;
+    }
+    
+    .feature-title {
+        font-family: 'Inter', sans-serif;
+        font-size: 1.5rem;
+        font-weight: 600;
+        color: #1e293b;
+        margin-bottom: 0.5rem;
+    }
+    
+    .feature-description {
+        color: #64748b;
+        font-size: 1rem;
+        line-height: 1.6;
+    }
+    
+    /* Metric Cards */
+    .metric-container {
+        display: flex;
+        gap: 1rem;
+        margin: 2rem 0;
+    }
+    
+    .metric-card {
+        background: linear-gradient(135deg, #4f46e5 0%, #7c3aed 100%);
+        color: white;
+        padding: 1.5 rem;
+        border-radius: 12px;
+        text-align: center;
+        box-shadow: 0 10px 15px -3px rgba(0, 0, 0, 0.1);
+        flex: 1;
+        transition: transform 0.2s ease;
+    }
+    
+    .metric-card:hover {
+        transform: scale(1.05);
+    }
+    
+    .metric-value {
+        font-size: 2rem;
+        font-weight: 700;
+        margin-bottom: 0.5rem;
+    }
+    
+    .metric-label {
+        font-size: 0.9rem;
+        opacity: 0.9;
+        font-weight: 500;
+    }
+    
+    /* Insight and Recommendation Boxes */
+    .insight-box {
+        background: linear-gradient(135deg, #eff6ff 0%, #dbeafe 100%);
+        border-left: 5px solid #3b82f6;
+        padding: 1.5rem;
+        margin: 1rem 0;
+        border-radius: 0 12px 12px 0;
+        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.05);
+        transition: all 0.3s ease;
+    }
+    
+    .insight-box:hover {
+        transform: translateX(4px);
+        box-shadow: 0 8px 25px rgba(0, 0, 0, 0.1);
+    }
+    
+    .recommendation-box {
+        background: linear-gradient(135deg, #f0fdf4 0%, #dcfce7 100%);
+        border-left: 5px solid #22c55e;
+        padding: 1.5rem;
+        margin: 1rem 0;
+        border-radius: 0 12px 12px 0;
+        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.05);
+        transition: all 0.3s ease;
+    }
+    
+    .recommendation-box:hover {
+        transform: translateX(4px);
+        box-shadow: 0 8px 25px rgba(0, 0, 0, 0.1);
+    }
+    
+    /* Upload Area */
+    .upload-area {
+        border: 2px dashed #cbd5e1;
+        border-radius: 12px;
+        padding: 3rem 2rem;
+        text-align: center;
+        background: linear-gradient(135deg, #f8fafc 0%, #f1f5f9 100%);
+        margin: 2rem 0;
+        transition: all 0.3s ease;
+    }
+    
+    .upload-area:hover {
+        border-color: #3b82f6;
+        background: linear-gradient(135deg, #eff6ff 0%, #dbeafe 100%);
+    }
+    
+    /* Progress Bar */
+    .stProgress > div > div > div > div {
+        background: linear-gradient(135deg, #3b82f6 0%, #8b5cf6 100%);
+        border-radius: 10px;
+    }
+    
+    /* Buttons */
+    .stButton > button {
+        background: linear-gradient(135deg, #3b82f6 0%, #8b5cf6 100%);
+        color: white;
+        border: none;
+        border-radius: 12px;
+        padding: 0.75rem 2rem;
+        font-weight: 600;
+        font-size: 1rem;
+        transition: all 0.3s ease;
+        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+    }
+    
+    .stButton > button:hover {
+        transform: translateY(-2px);
+        box-shadow: 0 8px 15px rgba(0, 0, 0, 0.2);
+    }
+    
+    /* Sidebar Styling */
+    .css-1d391kg {
+        background: linear-gradient(180deg, #1e293b 0%, #334155 100%);
+    }
+    
+    .css-1d391kg .sidebar-content {
+        color: white;
+    }
+    
+    /* Tab Styling */
+    .stTabs [data-baseweb="tab-list"] {
+        gap: 8px;
+    }
+    
+    .stTabs [data-baseweb="tab"] {
+        height: 50px;
+        background: linear-gradient(135deg, #f1f5f9 0%, #e2e8f0 100%);
+        border-radius: 12px;
+        border: 1px solid #cbd5e1;
+        color: #475569;
+        font-weight: 500;
+        transition: all 0.3s ease;
+    }
+    
+    .stTabs [aria-selected="true"] {
+        background: linear-gradient(135deg, #3b82f6 0%, #8b5cf6 100%);
+        color: white;
+        border: 1px solid #3b82f6;
+    }
+    
+    /* Success/Warning/Error Messages */
+    .stSuccess {
+        background: linear-gradient(135deg, #dcfce7 0%, #bbf7d0 100%);
+        border: 1px solid #22c55e;
+        border-radius: 12px;
+    }
+    
+    .stWarning {
+        background: linear-gradient(135deg, #fef3c7 0%, #fde68a 100%);
+        border: 1px solid #f59e0b;
+        border-radius: 12px;
+    }
+    
+    .stError {
+        background: linear-gradient(135deg, #fee2e2 0%, #fecaca 100%);
+        border: 1px solid #ef4444;
+        border-radius: 12px;
+    }
+    
+    /* Animation */
+    @keyframes fadeInUp {
+        from {
+            opacity: 0;
+            transform: translateY(30px);
+        }
+        to {
+            opacity: 1;
+            transform: translateY(0);
+        }
+    }
+    
+    .animate-fade-in {
+        animation: fadeInUp 0.6s ease-out;
+    }
+    
+    /* Data Table Styling */
+    .stDataFrame {
+        border-radius: 12px;
+        overflow: hidden;
+        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.05);
+    }
+    
+    /* Expander Styling */
+    .streamlit-expanderHeader {
+        background: linear-gradient(135deg, #f8fafc 0%, #f1f5f9 100%);
+        border-radius: 12px;
+        border: 1px solid #e2e8f0;
+    }
+    
+    /* Footer */
+    .footer {
+        text-align: center;
+        padding: 3rem 0;
+        color: #64748b;
+        font-size: 0.9rem;
+        border-top: 1px solid #e2e8f0;
+        margin-top: 4rem;
+    }
+    
+    .footer a {
+        color: #3b82f6;
+        text-decoration: none;
+        font-weight: 500;
+    }
+    
+    .footer a:hover {
+        text-decoration: underline;
+    }
+    
+    /* Loading Animation */
+    .loading-container {
+        display: flex;
+        justify-content: center;
+        align-items: center;
+        padding: 2rem;
+    }
+    
+    .loading-spinner {
+        border: 4px solid #f3f4f6;
+        border-top: 4px solid #3b82f6;
+        border-radius: 50%;
+        width: 40px;
+        height: 40px;
+        animation: spin 1s linear infinite;
+    }
+    
+    @keyframes spin {
+        0% { transform: rotate(0deg); }
+        100% { transform: rotate(360deg); }
+    }
+</style>
+""", unsafe_allow_html=True)
 
-# Try to import
-st.write("**Attempting import...**")
-try:
-    import data_analysis_agent
-    st.success("✅ Import successful!")
-    st.write("**Available classes/functions:**")
-    st.write(dir(data_analysis_agent))
-except ImportError as e:
-    st.error(f"❌ ImportError: {e}")
-except Exception as e:
-    st.error(f"❌ Other error: {e}")
-
-# Try alternative import methods
-st.write("**Trying alternative import methods...**")
-
-# Method 1: Add current directory to path
-try:
-    sys.path.insert(0, os.getcwd())
-    import data_analysis_agent
-    st.success("✅ Alternative import method 1 worked!")
-except Exception as e:
-    st.error(f"❌ Method 1 failed: {e}")
+def initialize_session_state():
+    """Initialize session state variables"""
+    if 'analysis_results' not in st.session_state:
+        st.session_state.analysis_results = None
+    if 'dataset' not in st.session_state:
+        st.session_state.dataset = None
+    if 'agent' not in st.session_state:
+        st.session_state.agent = None
+    if 'groq_api_key' not in st.session_state:
+        st.session_state.groq_api_key = ""
+    if 'model_name' not in st.session_state:
+        st.session_state.model_name = "llama3-70b-8192"
+    if 'analysis_complete' not in st.session_state:
+        st.session_state.analysis_complete = False
 
-# Method 2: Direct module loading
-try:
-    import importlib.util
-    spec = importlib.util.spec_from_file_location("data_analysis_agent", "./data_analysis_agent.py")
-    module = importlib.util.module_from_spec(spec)
-    spec.loader.exec_module(module)
-    st.success("✅ Alternative import method 2 worked!")
-except Exception as e:
-    st.error(f"❌ Method 2 failed: {e}")
-
-st.write("---")
-st.write("**This debug info will help us fix the import issue!**")
+def create_agent():
+    """Create and configure the data analysis agent"""
+    try:
+        # Check environment variable first, then session state
+        groq_api_key = os.environ.get('GROQ_API_KEY') or st.session_state.get('groq_api_key', '')
+        if not groq_api_key:
+            return None
+            
+        agent = DataAnalysisAgent(
+            groq_api_key=groq_api_key,
+            model_name=st.session_state.get('model_name', 'llama3-70b-8192')
+        )
+        return agent
+    except Exception as e:
+        st.error(f"Failed to create agent: {str(e)}")
+        return None
+
+def sidebar_config():
+    """Configure the beautiful sidebar"""
+    with st.sidebar:
+        st.markdown("""
+        <div style='text-align: center; padding: 1rem 0;'>
+            <div style='font-size: 4.5rem; margin-bottom: 0 rem;'>🤖</div>
+            <h1 style='
+                background: linear-gradient(135deg, #1e40af 0%, #3b82f6 50%, #06b6d4 100%);
+                -webkit-background-clip: text;
+                -webkit-text-fill-color: transparent;
+                background-clip: text;
+                margin: 0; 
+                font-size: 1.6rem;
+                font-weight: 700;
+            '>AI Agents on action</h1>
+            <p style='color: #94a3b8; margin: 0.5rem 0 0 0; font-size: 0.9rem;'>Powered by Llama 3</p>
+        </div>
+        """, unsafe_allow_html=True)
+        
+        st.markdown("---")
+        
+        # Check for environment variable first
+        env_api_key = os.environ.get('GROQ_API_KEY')
+        
+        if env_api_key:
+            st.success("✅ API Key Configured")
+            st.session_state.groq_api_key = env_api_key
+            api_key_configured = True
+        else:
+            st.subheader("🔑 API Setup")
+            st.info("💡 Set GROQ_API_KEY environment variable")
+            
+            groq_api_key = st.text_input(
+                "Groq API Key",
+                type="password",
+                value=st.session_state.groq_api_key,
+                help="Get your API key from console.groq.com"
+            )
+            
+            if groq_api_key:
+                st.session_state.groq_api_key = groq_api_key
+                api_key_configured = True
+            else:
+                api_key_configured = False
+        
+        st.markdown("---")
+        
+        # Model Selection
+        st.subheader("🧠 AI Model")
+        model_options = {
+            "llama3-70b-8192": "Llama 3 70B (Recommended)",
+            "llama3-8b-8192": "Llama 3 8B (Faster)",
+            "mixtral-8x7b-32768": "Mixtral 8x7B"
+        }
+        
+        selected_model = st.selectbox(
+            "Choose Model",
+            options=list(model_options.keys()),
+            format_func=lambda x: model_options[x],
+            index=0
+        )
+        st.session_state.model_name = selected_model
+        
+        st.markdown("---")
+        
+        # Analysis Options
+        st.subheader("⚙️ Analysis Settings")
+        
+        industry_type = st.selectbox(
+            "Industry Focus",
+            ["General", "Retail", "Healthcare", "Finance", "Manufacturing", "Technology"],
+            help="Customize insights for your industry"
+        )
+        st.session_state.industry_type = industry_type
+        
+        enable_advanced = st.toggle(
+            "Advanced Analysis",
+            value=True,
+            help="Include correlation analysis and advanced insights"
+        )
+        st.session_state.enable_advanced = enable_advanced
+        
+        auto_insights = st.toggle(
+            "Auto-Generate Insights",
+            value=True,
+            help="Automatically generate business insights"
+        )
+        st.session_state.auto_insights = auto_insights
+        
+        st.markdown("---")
+        
+        # Quick Stats with dynamic insights count
+        if st.session_state.dataset is not None:
+            st.subheader("📊 Dataset Info")
+            df = st.session_state.dataset
+            
+            col1, col2 = st.columns(2)
+            with col1:
+                st.metric("Rows", f"{df.shape[0]:,}")
+                st.metric("Columns", df.shape[1])
+            with col2:
+                st.metric("Missing", f"{df.isnull().sum().sum():,}")
+                st.metric("Size", f"{df.memory_usage(deep=True).sum() / 1024**2:.1f} MB")
+        
+        # Show insights count if analysis is complete (now shows top 5)
+        if st.session_state.analysis_results:
+            insights = st.session_state.analysis_results.get('insights', [])
+            recommendations = st.session_state.analysis_results.get('recommendations', [])
+            
+            # Process to get clean counts (max 5 each)
+            processed_insights_count = min(len([i for i in insights if isinstance(i, str) and len(i.strip()) > 20]), 5)
+            processed_recommendations_count = min(len([r for r in recommendations if isinstance(r, str) and len(r.strip()) > 20]), 5)
+            
+            st.markdown("---")
+            st.subheader("🧠 Analysis Results")
+            
+            col1, col2 = st.columns(2)
+            with col1:
+                st.metric("💡 Top Insights", processed_insights_count)
+            with col2:
+                st.metric("🎯 Top Recommendations", processed_recommendations_count)
+        
+        st.markdown("---")
+        
+        # Help Section
+        with st.expander("💡 Quick Help"):
+            st.markdown("""
+            **Supported Formats:**
+            - CSV files (.csv)
+            - Excel files (.xlsx, .xls)  
+            - JSON files (.json)
+            
+            **Best Practices:**
+            - Clean column names
+            - Handle missing values
+            - Include date columns
+            - Mix numeric & categorical data
+            
+            **Need Help?**
+            - [Documentation](https://github.com/yourusername/ai-data-analysis-agent)
+            - [Examples](https://github.com/yourusername/ai-data-analysis-agent/examples)
+            """)
+    
+    return api_key_configured
+
+def display_hero_section():
+    """Display the beautiful hero section"""
+    st.markdown('<div class="main-header animate-fade-in">AIDA-AI Data Analyzer </div>', unsafe_allow_html=True)
+    
+    st.markdown("""
+    <div class="subtitle animate-fade-in">
+        Transform your raw data into actionable business insights with the power of AI.<br>
+        Upload, analyze, and discover patterns automatically using intelligent agents.
+    </div>
+    """, unsafe_allow_html=True)
+
+def display_features():
+    """Display feature cards"""
+    st.markdown("### ✨ What This AI Agent Can Do")
+    
+    col1, col2, col3 = st.columns(3)
+    
+    with col1:
+        st.markdown("""
+        <div class="feature-card">
+            <div class="feature-icon">🧠</div>
+            <div class="feature-title">Intelligent Analysis</div>
+            <div class="feature-description">
+                Our AI automatically understands your data structure, identifies patterns, 
+                and generates meaningful insights without any manual configuration.
+            </div>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col2:
+        st.markdown("""
+        <div class="feature-card">
+            <div class="feature-icon">📊</div>
+            <div class="feature-title">Smart Visualizations</div>
+            <div class="feature-description">
+                Automatically creates the most appropriate charts and graphs for your data, 
+                with interactive visualizations.
+            </div>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col3:
+        st.markdown("""
+        <div class="feature-card">
+            <div class="feature-icon">🎯</div>
+            <div class="feature-title">Actionable Recommendations</div>
+            <div class="feature-description">
+                Get specific, measurable recommendations for improving your business 
+                based on data-driven insights.
+            </div>
+        </div>
+        """, unsafe_allow_html=True)
+
+def upload_dataset():
+    """Beautiful dataset upload section"""
+    st.markdown("### 📊 Upload Your Dataset")
+    
+    uploaded_file = st.file_uploader(
+        "",
+        type=['csv', 'xlsx', 'xls', 'json'],
+        help="Drag and drop your file here or click to browse",
+        label_visibility="collapsed"
+    )
+    
+    if uploaded_file is not None:
+        try:
+            # Show loading spinner
+            with st.spinner("🔍 Processing your dataset..."):
+                time.sleep(1)  # Small delay for UX
+                
+                # Read the file based on extension
+                if uploaded_file.name.endswith('.csv'):
+                    df = pd.read_csv(uploaded_file)
+                elif uploaded_file.name.endswith(('.xlsx', '.xls')):
+                    df = pd.read_excel(uploaded_file)
+                elif uploaded_file.name.endswith('.json'):
+                    df = pd.read_json(uploaded_file)
+                else:
+                    st.error("Unsupported file format")
+                    return False
+            
+            st.session_state.dataset = df
+            st.session_state.uploaded_filename = uploaded_file.name
+            
+            # Success message
+            st.success(f"✅ Successfully loaded **{uploaded_file.name}**")
+            
+            # Beautiful metrics display
+            col1, col2, col3, col4 = st.columns(4)
+            
+            with col1:
+                st.markdown(f"""
+                <div class="metric-card">
+                    <div class="metric-value">{df.shape[0]:,}</div>
+                    <div class="metric-label">Rows</div>
+                </div>
+                """, unsafe_allow_html=True)
+            
+            with col2:
+                st.markdown(f"""
+                <div class="metric-card">
+                    <div class="metric-value">{df.shape[1]}</div>
+                    <div class="metric-label">Columns</div>
+                </div>
+                """, unsafe_allow_html=True)
+            
+            with col3:
+                missing = df.isnull().sum().sum()
+                st.markdown(f"""
+                <div class="metric-card">
+                    <div class="metric-value">{missing:,}</div>
+                    <div class="metric-label">Missing Values</div>
+                </div>
+                """, unsafe_allow_html=True)
+            
+            with col4:
+                size_mb = df.memory_usage(deep=True).sum() / 1024**2
+                st.markdown(f"""
+                <div class="metric-card">
+                    <div class="metric-value">{size_mb:.1f} MB</div>
+                    <div class="metric-label">File Size</div>
+                </div>
+                """, unsafe_allow_html=True)
+            
+            st.markdown("<br>", unsafe_allow_html=True)
+            
+            # Data preview with beautiful styling
+            st.markdown("#### 📋 Data Preview")
+            st.dataframe(
+                df.head(10), 
+                use_container_width=True,
+                height=300
+            )
+            
+            # Column information in expandable section
+            with st.expander("📊 Detailed Column Information", expanded=False):
+                col_info = pd.DataFrame({
+                    'Column': df.columns,
+                    'Type': df.dtypes.astype(str),
+                    'Non-Null': df.count(),
+                    'Null Count': df.isnull().sum(),
+                    'Unique Values': df.nunique(),
+                    'Sample Data': [str(df[col].iloc[0]) if len(df) > 0 else '' for col in df.columns]
+                })
+                st.dataframe(col_info, use_container_width=True)
+                
+            return True
+            
+        except Exception as e:
+            st.error(f"❌ Error reading file: {str(e)}")
+            return False
+    else:
+        # Show upload placeholder
+        st.markdown("""
+        <div class="upload-area">
+            <div style="font-size: 3rem; margin-bottom: 1rem;">📁</div>
+            <div style="font-size: 1.2rem; font-weight: 600; margin-bottom: 0.5rem;">
+                Drop your dataset here
+            </div>
+            <div style="color: #64748b;">
+                Supports CSV, Excel, and JSON files • Max 200MB
+            </div>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    return False
+
+def run_analysis():
+    """Run the AI analysis with beautiful progress indicators"""
+    if st.session_state.dataset is None:
+        st.warning("Please upload a dataset first.")
+        return
+    
+    # Check for API key from environment or session state
+    api_key = os.environ.get('GROQ_API_KEY') or st.session_state.get('groq_api_key')
+    if not api_key:
+        st.warning("Please set GROQ_API_KEY environment variable or enter it in the sidebar.")
+        return
+    
+    # Create agent
+    with st.spinner("🤖 Initializing AI agent..."):
+        agent = create_agent()
+        if agent is None:
+            st.error("Failed to initialize AI agent. Check your API key.")
+            return
+    
+    st.session_state.agent = agent
+    
+    # Save dataset temporarily
+    temp_file = "temp_dataset.csv"
+    st.session_state.dataset.to_csv(temp_file, index=False)
+    
+    # Beautiful progress tracking
+    progress_container = st.container()
+    
+    with progress_container:
+        st.markdown("### 🚀 AI Analysis in Progress")
+        
+        # Progress bar
+        progress_bar = st.progress(0)
+        status_text = st.empty()
+        
+        # Step indicators
+        steps = [
+            ("🔍", "Analyzing dataset structure"),
+            ("📊", "Examining columns and data quality"),
+            ("🧠", "Generating AI insights"),
+            ("📈", "Planning visualizations"),
+            ("🎨", "Creating charts"),
+            ("🎯", "Formulating recommendations")
+        ]
+        
+        step_cols = st.columns(len(steps))
+        step_indicators = []
+        
+        for i, (icon, desc) in enumerate(steps):
+            with step_cols[i]:
+                step_indicators.append(st.empty())
+                step_indicators[i].markdown(f"""
+                <div style="text-align: center; padding: 1rem; opacity: 0.3;">
+                    <div style="font-size: 2rem;">{icon}</div>
+                    <div style="font-size: 0.8rem; margin-top: 0.5rem;">{desc}</div>
+                </div>
+                """, unsafe_allow_html=True)
+    
+    try:
+        # Step 1
+        step_indicators[0].markdown(f"""
+        <div style="text-align: center; padding: 1rem; opacity: 1; background: linear-gradient(135deg, #eff6ff 0%, #dbeafe 100%); border-radius: 12px;">
+            <div style="font-size: 2rem;">🔍</div>
+            <div style="font-size: 0.8rem; margin-top: 0.5rem; font-weight: 600;">Analyzing Structure</div>
+        </div>
+        """, unsafe_allow_html=True)
+        status_text.markdown("**🔍 AI agent analyzing dataset structure...**")
+        progress_bar.progress(15)
+        time.sleep(1)
+        
+        # Step 2
+        step_indicators[1].markdown(f"""
+        <div style="text-align: center; padding: 1rem; opacity: 1; background: linear-gradient(135deg, #eff6ff 0%, #dbeafe 100%); border-radius: 12px;">
+            <div style="font-size: 2rem;">📊</div>
+            <div style="font-size: 0.8rem; margin-top: 0.5rem; font-weight: 600;">Examining Data</div>
+        </div>
+        """, unsafe_allow_html=True)
+        status_text.markdown("**📊 Analyzing columns and data quality...**")
+        progress_bar.progress(30)
+        time.sleep(1)
+        
+        # Step 3
+        step_indicators[2].markdown(f"""
+        <div style="text-align: center; padding: 1rem; opacity: 1; background: linear-gradient(135deg, #eff6ff 0%, #dbeafe 100%); border-radius: 12px;">
+            <div style="font-size: 2rem;">🧠</div>
+            <div style="font-size: 0.8rem; margin-top: 0.5rem; font-weight: 600;">AI Thinking</div>
+        </div>
+        """, unsafe_allow_html=True)
+        status_text.markdown("**🧠 Generating insights with AI...**")
+        progress_bar.progress(50)
+        time.sleep(1)
+        
+        # Step 4
+        step_indicators[3].markdown(f"""
+        <div style="text-align: center; padding: 1rem; opacity: 1; background: linear-gradient(135deg, #eff6ff 0%, #dbeafe 100%); border-radius: 12px;">
+            <div style="font-size: 2rem;">📈</div>
+            <div style="font-size: 0.8rem; margin-top: 0.5rem; font-weight: 600;">Planning Charts</div>
+        </div>
+        """, unsafe_allow_html=True)
+        status_text.markdown("**📈 Planning optimal visualizations...**")
+        progress_bar.progress(70)
+        time.sleep(1)
+        
+        # Step 5
+        step_indicators[4].markdown(f"""
+        <div style="text-align: center; padding: 1rem; opacity: 1; background: linear-gradient(135deg, #eff6ff 0%, #dbeafe 100%); border-radius: 12px;">
+            <div style="font-size: 2rem;">🎨</div>
+            <div style="font-size: 0.8rem; margin-top: 0.5rem; font-weight: 600;">Creating Charts</div>
+        </div>
+        """, unsafe_allow_html=True)
+        status_text.markdown("**🎨 Creating beautiful visualizations...**")
+        progress_bar.progress(85)
+        
+        # Run the actual analysis
+        results = agent.analyze_dataset(temp_file)
+        
+        # Step 6
+        step_indicators[5].markdown(f"""
+        <div style="text-align: center; padding: 1rem; opacity: 1; background: linear-gradient(135deg, #eff6ff 0%, #dbeafe 100%); border-radius: 12px;">
+            <div style="font-size: 2rem;">🎯</div>
+            <div style="font-size: 0.8rem; margin-top: 0.5rem; font-weight: 600;">Final Recommendations</div>
+        </div>
+        """, unsafe_allow_html=True)
+        status_text.markdown("**🎯 Formulating actionable recommendations...**")
+        progress_bar.progress(100)
+        
+        # Clean up temp file
+        if os.path.exists(temp_file):
+            os.remove(temp_file)
+        
+        if "error" in results:
+            st.error(f"❌ Analysis failed: {results['error']}")
+            return
+        
+        st.session_state.analysis_results = results
+        st.session_state.analysis_complete = True
+        
+        # Success animation
+        status_text.markdown("**✅ Analysis completed successfully!**")
+        
+        # Show completion message
+        st.balloons()
+        time.sleep(1)
+        
+        # Clear progress and show results
+        progress_container.empty()
+        st.rerun()
+        
+    except Exception as e:
+        st.error(f"❌ Analysis failed: {str(e)}")
+        if os.path.exists(temp_file):
+            os.remove(temp_file)
+
+def display_results():
+    """Display beautiful analysis results"""
+    results = st.session_state.analysis_results
+    if results is None:
+        return
+    
+    # Results header
+    st.markdown("""
+    <div style="text-align: center; margin: 3rem 0;">
+        <h1 style="font-size: 2.5rem; color: #1e293b; margin-bottom: 0.5rem;">📊 Analysis Complete!</h1>
+        <p style="font-size: 1.1rem; color: #64748b;">Here are your AI-generated insights and recommendations</p>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    # Dataset Overview with beautiful cards
+    st.markdown("### 📋 Dataset Overview")
+    info = results.get('dataset_info', {})
+    
+    col1, col2, col3, col4, col5 = st.columns(5)
+    
+    metrics = [
+        ("📊", "Total Rows", f"{info.get('shape', [0])[0]:,}", "#3b82f6"),
+        ("📋", "Columns", str(info.get('shape', [0, 0])[1]), "#8b5cf6"),
+        ("🔢", "Numeric", str(len(info.get('numeric_columns', []))), "#06b6d4"),
+        ("📝", "Categorical", str(len(info.get('categorical_columns', []))), "#10b981"),
+        ("✨", "Quality Score", f"{max(0, 100 - (sum(info.get('null_counts', {}).values()) / max(info.get('shape', [1, 1])[0] * info.get('shape', [1, 1])[1], 1) * 100)):.0f}%", "#f59e0b")
+    ]
+    
+    for i, (icon, label, value, color) in enumerate(metrics):
+        with [col1, col2, col3, col4, col5][i]:
+            st.markdown(f"""
+            <div style="
+                background: linear-gradient(135deg, {color}15 0%, {color}25 100%);
+                border: 2px solid {color}30;
+                border-radius: 16px;
+                padding: 1.5rem;
+                text-align: center;
+                margin: 0.5rem 0;
+                transition: transform 0.2s ease;
+            ">
+                <div style="font-size: 2rem; margin-bottom: 0.5rem;">{icon}</div>
+                <div style="font-size: 1.8rem; font-weight: 700; color: {color}; margin-bottom: 0.25rem;">{value}</div>
+                <div style="font-size: 0.9rem; color: #64748b; font-weight: 500;">{label}</div>
+            </div>
+            """, unsafe_allow_html=True)
+    
+    st.markdown("<br>", unsafe_allow_html=True)
+    
+    # Key Insights Section - Extract complete insights with headers and content combined
+    st.markdown("### 💡 Key Insights")
+    insights = results.get('insights', [])
+    
+    if insights:
+        # Combine all insight text and parse properly
+        full_text = ' '.join(str(item) for item in insights)
+        
+        # Extract complete insights (header + content) using regex
+        import re
+        
+        # Pattern to match **Insight X:** followed by content until next insight or end
+        insight_pattern = r'\*\*Insight (\d+):(.*?)(?=\*\*Insight \d+:|$)'
+        matches = re.findall(insight_pattern, full_text, re.DOTALL)
+        
+        processed_insights = []
+        for match in matches:
+            insight_num, content = match
+            clean_content = content.strip().rstrip('*')
+            if len(clean_content) > 20:
+                processed_insights.append(clean_content)
+        
+        # Take top 5 insights
+        top_insights = processed_insights[:5]
+        
+        if top_insights:
+            st.markdown(f"**Top {len(top_insights)} key insights from your data:**")
+            st.markdown("<br>", unsafe_allow_html=True)
+            
+            for i, insight in enumerate(top_insights):
+                st.markdown(f"""
+                <div class="insight-box animate-fade-in">
+                    <div style="display: flex; align-items: flex-start; gap: 1rem;">
+                        <div style="
+                            background: #3b82f6; 
+                            color: white; 
+                            border-radius: 50%; 
+                            width: 32px; 
+                            height: 32px; 
+                            display: flex; 
+                            align-items: center; 
+                            justify-content: center; 
+                            font-weight: bold; 
+                            font-size: 0.9rem;
+                            flex-shrink: 0;
+                        ">{i+1}</div>
+                        <div style="flex: 1;">
+                            <strong style="color: #1e293b;">💡 Key Insight {i+1}:</strong><br>
+                            <span style="color: #475569; line-height: 1.6;">{insight}</span>
+                        </div>
+                    </div>
+                </div>
+                """, unsafe_allow_html=True)
+        else:
+            st.info("🔍 No insights could be extracted from the analysis.")
+    else:
+        st.info("🔍 No insights were generated.")
+    
+    # Interactive Visualizations Section
+    st.markdown("### 📈 Interactive Data Exploration")
+    
+    if st.session_state.dataset is not None:
+        df = st.session_state.dataset
+        
+        # Beautiful tabs
+        tab1, tab2, tab3, tab4 = st.tabs([
+            "📊 Distributions", 
+            "🔗 Correlations", 
+            "📈 Trends & Patterns", 
+            "🎯 Custom Analysis"
+        ])
+        
+        with tab1:
+            st.markdown("#### 📊 Distribution Analysis")
+            numeric_cols = df.select_dtypes(include=['number']).columns.tolist()
+            
+            if len(numeric_cols) > 0:
+                # Column selector at the top
+                selected_col = st.selectbox(
+                    "Select column to analyze",
+                    numeric_cols,
+                    key="dist_col"
+                )
+                
+                st.markdown("<br>", unsafe_allow_html=True)
+                
+                # Show all three plots side by side
+                col1, col2, col3 = st.columns(3)
+                
+                with col1:
+                    st.markdown("**Histogram**")
+                    fig_hist = px.histogram(
+                        df, 
+                        x=selected_col, 
+                        title=f"Histogram",
+                        nbins=30,
+                        color_discrete_sequence=['#3b82f6']
+                    )
+                    fig_hist.update_layout(
+                        height=380,
+                        plot_bgcolor='rgba(0,0,0,0)',
+                        paper_bgcolor='rgba(0,0,0,0)',
+                        title_font_size=14,
+                        margin=dict(t=40, b=40, l=40, r=40)
+                    )
+                    st.plotly_chart(fig_hist, use_container_width=True)
+                
+                with col2:
+                    st.markdown("**Box Plot**")
+                    fig_box = px.box(
+                        df, 
+                        y=selected_col, 
+                        title=f"Box Plot",
+                        color_discrete_sequence=['#8b5cf6']
+                    )
+                    fig_box.update_layout(
+                        height=380,
+                        plot_bgcolor='rgba(0,0,0,0)',
+                        paper_bgcolor='rgba(0,0,0,0)',
+                        title_font_size=14,
+                        margin=dict(t=40, b=40, l=40, r=40)
+                    )
+                    st.plotly_chart(fig_box, use_container_width=True)
+                
+                with col3:
+                    st.markdown("**Violin Plot**")
+                    fig_violin = px.violin(
+                        df, 
+                        y=selected_col, 
+                        title=f"Violin Plot",
+                        color_discrete_sequence=['#06b6d4']
+                    )
+                    fig_violin.update_layout(
+                        height=380,
+                        plot_bgcolor='rgba(0,0,0,0)',
+                        paper_bgcolor='rgba(0,0,0,0)',
+                        title_font_size=14,
+                        margin=dict(t=40, b=40, l=40, r=40)
+                    )
+                    st.plotly_chart(fig_violin, use_container_width=True)
+                
+                # Statistics cards below the plots
+                st.markdown("#### 📊 Statistical Summary")
+                stats_col1, stats_col2, stats_col3, stats_col4, stats_col5 = st.columns(5)
+                
+                stats = [
+                    ("Mean", f"{df[selected_col].mean():.2f}", "#3b82f6"),
+                    ("Median", f"{df[selected_col].median():.2f}", "#8b5cf6"),
+                    ("Std Dev", f"{df[selected_col].std():.2f}", "#06b6d4"),
+                    ("Min", f"{df[selected_col].min():.2f}", "#10b981"),
+                    ("Max", f"{df[selected_col].max():.2f}", "#f59e0b")
+                ]
+                
+                for i, (label, value, color) in enumerate(stats):
+                    with [stats_col1, stats_col2, stats_col3, stats_col4, stats_col5][i]:
+                        st.markdown(f"""
+                        <div style="
+                            background: {color}15;
+                            border: 1px solid {color}30;
+                            border-radius: 12px;
+                            padding: 1rem;
+                            text-align: center;
+                        ">
+                            <div style="font-size: 1.4rem; font-weight: 700; color: {color};">{value}</div>
+                            <div style="font-size: 0.85rem; color: #64748b; margin-top: 0.25rem;">{label}</div>
+                        </div>
+                        """, unsafe_allow_html=True)
+            else:
+                st.info("📊 No numeric columns found for distribution analysis.")
+        
+        with tab2:
+            st.markdown("#### 🔗 Correlation Analysis")
+            
+            if len(numeric_cols) > 1:
+                # Correlation matrix heatmap
+                corr_matrix = df[numeric_cols].corr()
+                
+                fig = px.imshow(
+                    corr_matrix, 
+                    text_auto=True, 
+                    aspect="auto",
+                    title="Correlation Matrix",
+                    color_continuous_scale="RdBu_r",
+                    zmin=-1,
+                    zmax=1
+                )
+                fig.update_layout(
+                    height=500,
+                    plot_bgcolor='rgba(0,0,0,0)',
+                    paper_bgcolor='rgba(0,0,0,0)'
+                )
+                st.plotly_chart(fig, use_container_width=True)
+                
+                # Top correlations
+                st.markdown("#### 🔗 Strongest Correlations")
+                correlations = []
+                for i in range(len(corr_matrix.columns)):
+                    for j in range(i+1, len(corr_matrix.columns)):
+                        corr_val = corr_matrix.iloc[i, j]
+                        if not pd.isna(corr_val):
+                            correlations.append({
+                                'Variable 1': corr_matrix.columns[i],
+                                'Variable 2': corr_matrix.columns[j],
+                                'Correlation': corr_val,
+                                'Strength': abs(corr_val)
+                            })
+                
+                if correlations:
+                    corr_df = pd.DataFrame(correlations)
+                    corr_df = corr_df.sort_values('Strength', ascending=False).head(10)
+                    
+                    # Display as beautiful cards
+                    for _, row in corr_df.head(5).iterrows():
+                        strength = "Strong" if row['Strength'] > 0.7 else "Moderate" if row['Strength'] > 0.5 else "Weak"
+                        color = "#ef4444" if row['Strength'] > 0.7 else "#f59e0b" if row['Strength'] > 0.5 else "#10b981"
+                        
+                        st.markdown(f"""
+                        <div style="
+                            background: {color}15;
+                            border-left: 4px solid {color};
+                            border-radius: 8px;
+                            padding: 1rem;
+                            margin: 0.5rem 0;
+                        ">
+                            <div style="font-weight: 600; color: #1e293b; margin-bottom: 0.5rem;">
+                                {row['Variable 1']} ↔ {row['Variable 2']}
+                            </div>
+                            <div style="color: #64748b;">
+                                Correlation: <strong style="color: {color};">{row['Correlation']:.3f}</strong> 
+                                ({strength} relationship)
+                            </div>
+                        </div>
+                        """, unsafe_allow_html=True)
+            else:
+                st.info("🔗 Need at least 2 numeric columns for correlation analysis.")
+        
+        with tab3:
+            st.markdown("#### 📈 Trends & Patterns")
+            
+            date_cols = df.select_dtypes(include=['datetime64']).columns.tolist()
+            cat_cols = df.select_dtypes(include=['object', 'category']).columns.tolist()
+            
+            if len(date_cols) > 0 and len(numeric_cols) > 0:
+                col1, col2 = st.columns(2)
+                with col1:
+                    date_col = st.selectbox("Date column", date_cols, key="trend_date")
+                with col2:
+                    value_col = st.selectbox("Value column", numeric_cols, key="trend_value")
+                
+                df_sorted = df.sort_values(date_col)
+                fig = px.line(
+                    df_sorted, 
+                    x=date_col, 
+                    y=value_col, 
+                    title=f"{value_col} Over Time",
+                    color_discrete_sequence=['#3b82f6']
+                )
+                fig.update_layout(height=400)
+                st.plotly_chart(fig, use_container_width=True)
+                
+            elif cat_cols and numeric_cols:
+                st.markdown("#### 📊 Category-based Analysis")
+                
+                col1, col2, col3 = st.columns(3)
+                with col1:
+                    cat_col = st.selectbox("Category", cat_cols, key="cat_trend")
+                with col2:
+                    num_col = st.selectbox("Numeric value", numeric_cols, key="num_trend")
+                with col3:
+                    agg_func = st.selectbox("Aggregation", ["mean", "sum", "count", "median"])
+                
+                if agg_func == "count":
+                    grouped = df.groupby(cat_col).size().reset_index(name='count')
+                    y_col = 'count'
+                else:
+                    grouped = df.groupby(cat_col)[num_col].agg(agg_func).reset_index()
+                    y_col = num_col
+                
+                fig = px.bar(
+                    grouped, 
+                    x=cat_col, 
+                    y=y_col,
+                    title=f"{agg_func.title()} of {num_col if agg_func != 'count' else 'Count'} by {cat_col}",
+                    color_discrete_sequence=['#8b5cf6']
+                )
+                fig.update_layout(height=400)
+                st.plotly_chart(fig, use_container_width=True)
+            else:
+                st.info("📈 Upload data with date columns or categorical data to see trends.")
+        
+        with tab4:
+            st.markdown("#### 🎯 Custom Analysis Builder")
+            
+            col1, col2 = st.columns([1, 2])
+            
+            with col1:
+                viz_type = st.selectbox(
+                    "Chart Type",
+                    ["Scatter Plot", "Bar Chart", "Pie Chart", "Sunburst", "Treemap"]
+                )
+                
+                if viz_type == "Scatter Plot" and len(numeric_cols) >= 2:
+                    x_col = st.selectbox("X-axis", numeric_cols, key="custom_x")
+                    y_col = st.selectbox("Y-axis", numeric_cols, key="custom_y")
+                    color_col = st.selectbox("Color by", ["None"] + list(df.columns), key="custom_color")
+                    size_col = st.selectbox("Size by", ["None"] + numeric_cols, key="custom_size")
+                
+                elif viz_type in ["Bar Chart", "Pie Chart"] and cat_cols:
+                    cat_col = st.selectbox("Category", cat_cols, key="custom_cat")
+                    if numeric_cols:
+                        val_col = st.selectbox("Value (optional)", ["Count"] + numeric_cols, key="custom_val")
+                    else:
+                        val_col = "Count"
+            
+            with col2:
+                try:
+                    if viz_type == "Scatter Plot" and len(numeric_cols) >= 2:
+                        fig = px.scatter(
+                            df,
+                            x=x_col,
+                            y=y_col,
+                            color=None if color_col == "None" else color_col,
+                            size=None if size_col == "None" else size_col,
+                            title=f"{y_col} vs {x_col}",
+                            color_discrete_sequence=['#3b82f6'],
+                            hover_data=df.columns[:5].tolist()
+                        )
+                        fig.update_layout(height=500)
+                        st.plotly_chart(fig, use_container_width=True)
+                    
+                    elif viz_type == "Pie Chart" and cat_cols:
+                        if val_col == "Count":
+                            value_counts = df[cat_col].value_counts().head(8)
+                            fig = px.pie(
+                                values=value_counts.values,
+                                names=value_counts.index,
+                                title=f"Distribution of {cat_col}"
+                            )
+                        else:
+                            grouped = df.groupby(cat_col)[val_col].sum().head(8)
+                            fig = px.pie(
+                                values=grouped.values,
+                                names=grouped.index,
+                                title=f"{val_col} by {cat_col}"
+                            )
+                        fig.update_layout(height=500)
+                        st.plotly_chart(fig, use_container_width=True)
+                        
+                except Exception as e:
+                    st.error(f"Error creating visualization: {str(e)}")
+    
+    # Recommendations Section - Extract complete recommendations with headers and content combined
+    st.markdown("### 🎯 AI-Generated Recommendations")
+    recommendations = results.get('recommendations', [])
+    
+    if recommendations:
+        # Combine all recommendation text and parse properly
+        full_text = ' '.join(str(item) for item in recommendations)
+        
+        # Extract complete recommendations using regex
+        import re
+        
+        # Pattern to match recommendations (various formats)
+        rec_patterns = [
+            r'\*\*.*?(\d+):(.*?)(?=\*\*.*?\d+:|$)',  # **Something 1:** format
+            r'(\d+)\.\s+(.*?)(?=\d+\.|$)',           # 1. format
+        ]
+        
+        processed_recommendations = []
+        for pattern in rec_patterns:
+            matches = re.findall(pattern, full_text, re.DOTALL)
+            if matches:
+                for match in matches:
+                    if len(match) == 2:
+                        rec_num, content = match
+                        clean_content = content.strip().rstrip('*')
+                        if len(clean_content) > 20:
+                            processed_recommendations.append(clean_content)
+                break
+        
+        # Take top 5 recommendations
+        top_recommendations = processed_recommendations[:5]
+        
+        if top_recommendations:
+            st.markdown(f"**Top {len(top_recommendations)} actionable recommendations:**")
+            st.markdown("<br>", unsafe_allow_html=True)
+            
+            for i, rec in enumerate(top_recommendations):
+                st.markdown(f"""
+                <div class="recommendation-box animate-fade-in">
+                    <div style="display: flex; align-items: flex-start; gap: 1rem;">
+                        <div style="
+                            background: #22c55e; 
+                            color: white; 
+                            border-radius: 50%; 
+                            width: 32px; 
+                            height: 32px; 
+                            display: flex; 
+                            align-items: center; 
+                            justify-content: center; 
+                            font-weight: bold; 
+                            font-size: 0.9rem;
+                            flex-shrink: 0;
+                        ">{i+1}</div>
+                        <div style="flex: 1;">
+                            <strong style="color: #1e293b;">🎯 Recommendation {i+1}:</strong><br>
+                            <span style="color: #475569; line-height: 1.6;">{rec}</span>
+                        </div>
+                    </div>
+                </div>
+                """, unsafe_allow_html=True)
+        else:
+            st.info("🎯 No recommendations could be extracted from the analysis.")
+    else:
+        st.info("🎯 No recommendations were generated.")
+    
+    # Download Results Section
+    st.markdown("### 💾 Download Your Results")
+    
+    col1, col2, col3 = st.columns(3)
+    
+    download_items = [
+        ("📄", "Analysis Report (JSON)", "Download complete analysis", "json"),
+        ("📊", "Enhanced Dataset (CSV)", "Download processed data", "csv"),
+        ("📋", "Executive Summary (MD)", "Download business report", "md")
+    ]
+    
+    for i, (icon, title, desc, file_type) in enumerate(download_items):
+        with [col1, col2, col3][i]:
+            st.markdown(f"""
+            <div style="
+                background: linear-gradient(135deg, #f8fafc 0%, #f1f5f9 100%);
+                border: 2px solid #e2e8f0;
+                border-radius: 16px;
+                padding: 1.5rem;
+                text-align: center;
+                margin: 0.5rem 0;
+                transition: all 0.3s ease;
+            ">
+                <div style="font-size: 2.5rem; margin-bottom: 1rem;">{icon}</div>
+                <div style="font-size: 1.1rem; font-weight: 600; margin-bottom: 0.5rem; color: #1e293b;">{title}</div>
+                <div style="font-size: 0.9rem; color: #64748b; margin-bottom: 1rem;">{desc}</div>
+            """, unsafe_allow_html=True)
+            
+            if file_type == "json":
+                data = json.dumps(results, indent=2, default=str)
+                filename = f"analysis_results_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
+                mime = "application/json"
+            elif file_type == "csv":
+                data = st.session_state.dataset.to_csv(index=False)
+                filename = f"enhanced_dataset_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv"
+                mime = "text/csv"
+            else:  # md
+                data = generate_report(results)
+                filename = f"executive_summary_{datetime.now().strftime('%Y%m%d_%H%M%S')}.md"
+                mime = "text/markdown"
+            
+            st.download_button(
+                label=f"Download {file_type.upper()}",
+                data=data,
+                file_name=filename,
+                mime=mime,
+                use_container_width=True
+            )
+            
+            st.markdown("</div>", unsafe_allow_html=True)
+
+def generate_report(results):
+    """Generate a beautiful markdown report"""
+    filename = getattr(st.session_state, 'uploaded_filename', 'dataset')
+    
+    report = f"""# 🤖 AI Data Analysis Executive Summary
+
+**Dataset:** {filename}  
+**Generated:** {datetime.now().strftime('%B %d, %Y at %I:%M %p')}  
+**Powered by:** Llama 3 & LangGraph AI Agents
+
+---
+
+## 📊 Executive Overview
+
+This report presents key findings from an AI-powered analysis of your dataset. Our advanced language models have identified patterns, trends, and opportunities that can drive business decisions.
+
+### Dataset Metrics
+- **Total Records:** {results.get('dataset_info', {}).get('shape', [0])[0]:,}
+- **Data Points:** {len(results.get('dataset_info', {}).get('columns', []))}
+- **Data Quality Score:** {max(0, 100 - (sum(results.get('dataset_info', {}).get('null_counts', {}).values()) / max(results.get('dataset_info', {}).get('shape', [1, 1])[0] * results.get('dataset_info', {}).get('shape', [1, 1])[1], 1) * 100)):.0f}%
+
+---
+
+## 💡 Strategic Insights
+
+Our AI analysis has uncovered the following key insights:
+
+"""
+    
+    insights = results.get('insights', [])
+    if insights:
+        for i, insight in enumerate(insights, 1):
+            report += f"**{i}.** {insight}\n\n"
+    else:
+        report += "*No specific insights were generated for this dataset.*\n\n"
+    
+    report += """---
+
+## 🎯 Recommended Actions
+
+Based on the data analysis, we recommend the following strategic actions:
+
+"""
+    
+    recommendations = results.get('recommendations', [])
+    if recommendations:
+        for i, rec in enumerate(recommendations, 1):
+            report += f"**{i}.** {rec}\n\n"
+    else:
+        report += "*No specific recommendations were generated for this dataset.*\n\n"
+    
+    report += f"""---
+
+## 🔧 Technical Summary
+
+- **Analysis Completed:** {results.get('analysis_timestamp', 'N/A')}
+- **Visualizations Created:** {len(results.get('visualizations', []))}
+- **Processing Errors:** {len(results.get('errors', []))}
+- **AI Model Used:** Llama 3 (70B parameters)
+
+---
+
+## 📈 Next Steps
+
+1. **Review Insights:** Analyze each insight for immediate actionable opportunities
+2. **Implement Recommendations:** Prioritize recommendations based on business impact
+3. **Monitor Progress:** Track key metrics identified in this analysis
+4. **Iterate:** Regular re-analysis as new data becomes available
+
+---
+
+*This report was generated automatically by our AI Data Analysis Agent. For questions or support, please contact your data team.*
+"""
+    
+    return report
+
+def main():
+    """Main application function with beautiful design"""
+    initialize_session_state()
+    
+    # Check if analysis is complete to show results immediately
+    if st.session_state.analysis_complete and st.session_state.analysis_results:
+        display_results()
+        
+        # Add a "Start New Analysis" button
+        st.markdown("---")
+        col1, col2, col3 = st.columns([1, 1, 1])
+        with col2:
+            if st.button("🔄 Start New Analysis", use_container_width=True):
+                # Reset session state
+                st.session_state.analysis_results = None
+                st.session_state.analysis_complete = False
+                st.session_state.dataset = None
+                st.rerun()
+        return
+    
+    # Hero Section
+    display_hero_section()
+    
+    # Feature showcase
+    display_features()
+    
+    # Sidebar configuration
+    api_configured = sidebar_config()
+    
+    if not api_configured:
+        # Beautiful warning with setup instructions
+        st.markdown("""
+        <div style="
+            background: linear-gradient(135deg, #fef3c7 0%, #fde68a 100%);
+            border: 2px solid #f59e0b;
+            border-radius: 16px;
+            padding: 2rem;
+            margin: 2rem 0;
+            text-align: center;
+        ">
+            <div style="font-size: 3rem; margin-bottom: 1rem;">🔑</div>
+            <h3 style="color: #92400e; margin-bottom: 1rem;">API Key Required</h3>
+            <p style="color: #78350f; margin-bottom: 1.5rem;">
+                Please configure your Groq API key to unlock the power of AI analysis
+            </p>
+        </div>
+        """, unsafe_allow_html=True)
+        
+        # Expandable setup guide
+        with st.expander("🚀 Quick Setup Guide", expanded=True):
+            st.markdown("""
+            ### Option 1: Environment Variable (Recommended)
+            ```bash
+            export GROQ_API_KEY="your_api_key_here"
+            streamlit run web_app.py
+            ```
+            
+            ### Option 2: Manual Entry
+            1. Visit [Groq Console](https://console.groq.com/) 🔗
+            2. Create a free account and generate your API key
+            3. Enter the key in the sidebar ← 
+            4. Upload your dataset and start analyzing!
+            
+            ### Supported File Formats
+            - **CSV files** (.csv) - Most common format
+            - **Excel files** (.xlsx, .xls) - Spreadsheet data
+            - **JSON files** (.json) - Structured data
+            
+            ### Tips for Best Results
+            - Ensure clean, well-structured data
+            - Include meaningful column names
+            - Mix of numeric and categorical columns works best
+            - Date/time columns enable trend analysis
+            """)
+        return
+    
+    # Main content area with beautiful layout
+    st.markdown("---")
+    
+    # Dataset upload section
+    dataset_uploaded = upload_dataset()
+    
+    # Analysis section
+    if dataset_uploaded:
+        st.markdown("---")
+        
+        # Center the analyze button with beautiful styling
+        col1, col2, col3 = st.columns([1, 2, 1])
+        with col2:
+            if st.button(
+                "🚀 Analyze My Data with AI",
+                type="primary",
+                use_container_width=True,
+                help="Start the AI-powered analysis of your dataset"
+            ):
+                run_analysis()
+    
+    # Footer
+    st.markdown("""
+    <div class="footer">
+        <div style="max-width: 800px; margin: 0 auto;">
+            <div style="font-size: 1.5rem; margin-bottom: 1rem;">🤖✨</div>
+            <p style="margin-bottom: 1rem;">
+                <strong>AI Data Analysis Agent</strong> - Transform your data into actionable insights
+            </p>
+            <p style="font-size: 0.85rem; margin-bottom: 1rem;">
+                Powered by <strong>Llama 3</strong> • Built with <strong>LangGraph</strong> • 
+                Designed with <strong>Streamlit</strong>
+            </p>
+            <div style="display: flex; justify-content: center; gap: 2rem; font-size: 0.9rem;">
+                <a href="#" style="color: #3b82f6; text-decoration: none;">📖 Documentation</a>
+                <a href="#" style="color: #3b82f6; text-decoration: none;">🐛 Report Issues</a>
+                <a href="#" style="color: #3b82f6; text-decoration: none;">⭐ Give Feedback</a>
+                <a href="#" style="color: #3b82f6; text-decoration: none;">💡 Feature Requests</a>
+            </div>
+        </div>
+    </div>
+    """, unsafe_allow_html=True)
+
+if __name__ == "__main__":
+    main()