app.py

#!/usr/bin/env python3
"""
GlycoAI - AI-Powered Glucose Insights
Complete application with Demo Users + Dexcom Sandbox OAuth
IMPROVED UI VERSION - Clean, readable design with blue theme
"""

import gradio as gr
import plotly.graph_objects as go
import plotly.express as px
from datetime import datetime, timedelta
import pandas as pd
from typing import Optional, Tuple, List
import logging
import os

# Load environment variables from .env file
from dotenv import load_dotenv
load_dotenv()

# Import the Mistral chat class and unified data manager
from mistral_chat import GlucoBuddyMistralChat, validate_environment
from unified_data_manager import UnifiedDataManager

# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Import our custom functions
from apifunctions import (
    DexcomAPI,
    GlucoseAnalyzer,
    DEMO_USERS,
    format_glucose_data_for_display
)

# Import Dexcom Sandbox OAuth
try:
    from dexcom_sandbox_oauth import DexcomSandboxIntegration, DexcomSandboxUser
    DEXCOM_SANDBOX_AVAILABLE = True
    logger.info("✅ Dexcom Sandbox OAuth available")
except ImportError as e:
    DEXCOM_SANDBOX_AVAILABLE = False
    logger.warning(f"⚠️ Dexcom Sandbox OAuth not available: {e}")

class GlycoAIApp:
    """Main application class for GlycoAI with demo users AND Dexcom Sandbox OAuth"""

    def __init__(self):
        # Validate environment before initializing
        if not validate_environment():
            raise ValueError("Environment validation failed - check your .env file or environment variables")
        
        # Single data manager for consistency
        self.data_manager = UnifiedDataManager()
        
        # Chat interface (will use data manager's context)
        self.mistral_chat = GlucoBuddyMistralChat()
        
        # Dexcom Sandbox OAuth API
        self.dexcom_sandbox = DexcomSandboxIntegration() if DEXCOM_SANDBOX_AVAILABLE else None
        
        # UI state
        self.chat_history = []
        self.current_user_type = None  # "demo" or "dexcom_sandbox"

    def select_demo_user(self, user_key: str) -> Tuple[str, str]:
        """Handle demo user selection and load data consistently"""
        if user_key not in DEMO_USERS:
            return "❌ Invalid user selection", gr.update(visible=False)

        try:
            # Load data through unified manager
            load_result = self.data_manager.load_user_data(user_key)
            
            if not load_result['success']:
                return f"❌ {load_result['message']}", gr.update(visible=False)
            
            user = self.data_manager.current_user
            self.current_user_type = "demo"
            
            # Update Mistral chat with the same context
            self._sync_chat_with_data_manager()
            
            # Clear chat history when switching users
            self.chat_history = []
            self.mistral_chat.clear_conversation()

            return (
                f"✅ Connected: {user.name} ({user.device_type}) - Demo Data", 
                gr.update(visible=True)
            )

        except Exception as e:
            logger.error(f"Demo user selection failed: {str(e)}")
            return f"❌ Connection failed: {str(e)}", gr.update(visible=False)

    def initialize_chat_with_prompts(self) -> List:
        """Initialize chat with demo prompts as conversation bubbles"""
        if not self.data_manager.current_user:
            return [
                [None, "👋 Welcome to GlycoAI! Please select a demo user or connect Dexcom Sandbox to get started."],
                [None, "💡 Once you load your glucose data, I'll provide personalized insights about your patterns and trends."]
            ]
        
        templates = self.get_template_prompts()
        
        # Create initial conversation with demo prompts
        initial_chat = [
            [None, f"👋 Hi! I'm ready to analyze {self.data_manager.current_user.name}'s glucose data. Here are some quick ways to get started:"],
            [None, f"🎯 **{templates[0] if templates else 'Analyze my recent glucose patterns and trends'}**"],
            [None, f"⚡ **{templates[1] if len(templates) > 1 else 'What can I do to improve my glucose control?'}**"],
            [None, f"🍽️ **What are some meal management strategies for better glucose control?**"],
            [None, "💬 You can click on any of these questions above, or ask me anything about glucose management!"]
        ]
        
        return initial_chat

    def handle_demo_prompt_click(self, prompt_text: str, history: List) -> Tuple[str, List]:
        """Handle clicking on demo prompts in chat"""
        # Remove the emoji and formatting from the prompt
        clean_prompt = prompt_text.replace("🎯 **", "").replace("⚡ **", "").replace("🍽️ **", "").replace("**", "")
        
        # Process the prompt as if user typed it
        return self.chat_with_mistral(clean_prompt, history)

    def start_dexcom_sandbox_oauth(self) -> str:
        """Start Dexcom Sandbox OAuth process"""
        if not DEXCOM_SANDBOX_AVAILABLE:
            return """
❌ **Dexcom Sandbox OAuth Not Available**

The Dexcom Sandbox authentication module is not properly configured.
Please ensure:
1. dexcom_sandbox_oauth.py exists and imports correctly
2. You have valid Dexcom developer credentials
3. All dependencies are installed

For now, please use the demo users above for instant access to realistic glucose data.
            """
        
        try:
            # Start OAuth flow for Dexcom Sandbox
            auth_url = self.dexcom_sandbox.oauth.generate_auth_url()
            
            # Try to open browser automatically
            try:
                import webbrowser
                webbrowser.open(auth_url)
                browser_status = "✅ Browser opened automatically"
            except:
                browser_status = "⚠️ Please open the URL manually"
            
            return f"""
🚀 **Dexcom Sandbox OAuth Started**

{browser_status}

**🌐 OAuth URL:** {auth_url}

**Step-by-Step Instructions:**
1. Browser should open automatically (or open URL above)
2. Select a sandbox user from the dropdown (SandboxUser6 recommended)
3. Click "Authorize" to grant access
4. **You will get a 404 error - THIS IS EXPECTED!**
5. Copy the COMPLETE callback URL from address bar

**Example callback URL:**
`http://localhost:7860/callback?code=ABC123XYZ&state=sandbox_test`

**Important:** Copy the entire URL (not just the code part)!
                """
                
        except Exception as e:
            logger.error(f"Dexcom Sandbox OAuth start error: {e}")
            return f"❌ OAuth error: {str(e)}"

    def complete_dexcom_sandbox_oauth(self, callback_url_input: str) -> Tuple[str, str]:
        """Complete Dexcom Sandbox OAuth with full callback URL"""
        if not DEXCOM_SANDBOX_AVAILABLE:
            return "❌ Dexcom Sandbox OAuth not available", gr.update(visible=False)
        
        if not callback_url_input or not callback_url_input.strip():
            return "❌ Please paste the complete callback URL", gr.update(visible=False)
        
        try:
            callback_url = callback_url_input.strip()
            
            logger.info(f"Processing Dexcom Sandbox callback: {callback_url[:50]}...")
            
            # Use Dexcom Sandbox OAuth completion
            status_message, show_interface = self.dexcom_sandbox.complete_oauth(callback_url)
            
            if show_interface:
                logger.info("✅ Dexcom Sandbox OAuth successful")
                
                # Load Dexcom Sandbox data into data manager
                sandbox_data_result = self._load_dexcom_sandbox_data()
                
                if sandbox_data_result['success']:
                    self.current_user_type = "dexcom_sandbox"
                    
                    # Update chat context
                    self._sync_chat_with_data_manager()
                    
                    # Clear chat history for new user
                    self.chat_history = []
                    self.mistral_chat.clear_conversation()
                    
                    return (
                        f"✅ Connected: Dexcom Sandbox User - OAuth Authenticated",
                        gr.update(visible=True)
                    )
                else:
                    return f"❌ Dexcom Sandbox data loading failed: {sandbox_data_result['message']}", gr.update(visible=False)
            else:
                logger.error(f"Dexcom Sandbox OAuth failed: {status_message}")
                return f"❌ {status_message}", gr.update(visible=False)
                
        except Exception as e:
            logger.error(f"Dexcom Sandbox OAuth completion error: {e}")
            return f"❌ OAuth completion failed: {str(e)}", gr.update(visible=False)

    def _load_dexcom_sandbox_data(self) -> dict:
        """Load Dexcom Sandbox data through the unified data manager"""
        try:
            # Get Dexcom Sandbox user profile
            sandbox_profile = self.dexcom_sandbox.get_user_profile()
            
            if not sandbox_profile:
                return {
                    'success': False,
                    'message': 'No Dexcom Sandbox user profile available'
                }
            
            # Set in data manager (compatible with existing structure)
            self.data_manager.current_user = sandbox_profile
            self.data_manager.data_source = "dexcom_sandbox_oauth"
            self.data_manager.data_loaded_at = datetime.now()
            
            logger.info("✅ Dexcom Sandbox data integrated with data manager")
            
            return {
                'success': True,
                'message': 'Dexcom Sandbox user profile loaded successfully'
            }
            
        except Exception as e:
            logger.error(f"Failed to load Dexcom Sandbox data: {e}")
            return {
                'success': False,
                'message': f'Failed to load OAuth data: {str(e)}'
            }

    def load_glucose_data(self) -> Tuple[str, go.Figure, str]:
        """Load and display glucose data using unified manager with notifications"""
        if not self.data_manager.current_user:
            return "Please select a user first (demo or Dexcom Sandbox)", None, ""

        try:
            # For Dexcom Sandbox users, load real data via OAuth
            if self.current_user_type == "dexcom_sandbox":
                overview, chart = self._load_dexcom_sandbox_glucose_data()
            else:
                # For demo users, force reload data to ensure freshness
                load_result = self.data_manager.load_user_data(
                    self._get_current_user_key(), 
                    force_reload=True
                )
                
                if not load_result['success']:
                    return load_result['message'], None, ""
                
                # Get unified stats and build display
                overview, chart = self._build_glucose_display()
            
            # Create notification message based on user and data quality
            notification = self._create_data_loaded_notification()
            
            return overview, chart, notification
            
        except Exception as e:
            logger.error(f"Failed to load glucose data: {str(e)}")
            return f"Failed to load glucose data: {str(e)}", None, ""

    def _create_data_loaded_notification(self) -> str:
        """Create appropriate notification based on loaded data"""
        if not self.data_manager.current_user or not self.data_manager.calculated_stats:
            return ""
        
        user_name = self.data_manager.current_user.name
        stats = self.data_manager.calculated_stats
        
        tir = stats.get('time_in_range_70_180', 0)
        cv = stats.get('cv', 0)
        avg_glucose = stats.get('average_glucose', 0)
        total_readings = stats.get('total_readings', 0)
        
        # Special handling for Sarah (unstable patterns)
        if user_name == "Sarah Thompson":
            if tir < 50 and cv > 40:
                notification = f"""
🚨 **DATA LOADED - CONCERNING PATTERNS DETECTED**

**Patient:** {user_name} ({total_readings:,} readings analyzed)

**⚠️ Critical Findings:**
• Time in Range: {tir:.1f}% (Target: >70%)
• High Variability: CV {cv:.1f}% (Target: <36%)
• Average Glucose: {avg_glucose:.1f} mg/dL

**🔥 Immediate Action Required**
• Frequent hypoglycemia detected
• Severe glucose instability
• Healthcare provider consultation recommended

*AI analysis ready - Click Chat tab for urgent insights*
                """
            else:
                notification = f"""
✅ **DATA LOADED SUCCESSFULLY**

**Patient:** {user_name} ({total_readings:,} readings analyzed)
**Time in Range:** {tir:.1f}% | **Average:** {avg_glucose:.1f} mg/dL

*14-day analysis complete - Ready for AI insights*
                """
        else:
            # For other users with better control
            if tir >= 70:
                notification = f"""
✅ **DATA LOADED - EXCELLENT CONTROL**

**Patient:** {user_name} ({total_readings:,} readings analyzed)
**Time in Range:** {tir:.1f}% ✅ | **CV:** {cv:.1f}%

*Great glucose management - AI ready to help maintain control*
                """
            else:
                notification = f"""
📊 **DATA LOADED SUCCESSFULLY**

**Patient:** {user_name} ({total_readings:,} readings analyzed)
**Time in Range:** {tir:.1f}% | **Average:** {avg_glucose:.1f} mg/dL

*Analysis complete - AI ready to provide insights*
                """
        
        return notification

    def _load_dexcom_sandbox_glucose_data(self) -> Tuple[str, go.Figure]:
        """Load Dexcom Sandbox glucose data via OAuth"""
        if not self.dexcom_sandbox.authenticated:
            return "❌ Dexcom Sandbox not authenticated. Please complete OAuth first.", None
        
        try:
            # Load 14 days of data from Dexcom Sandbox
            data_result = self.dexcom_sandbox.load_glucose_data(days=14)
            
            if not data_result['success']:
                return f"❌ {data_result['error']}", None
            
            # Convert Dexcom Sandbox data to data manager format
            self._convert_dexcom_sandbox_to_dataframe()
            
            return self._build_glucose_display()
            
        except Exception as e:
            logger.error(f"Failed to load Dexcom Sandbox data: {e}")
            return f"❌ Failed to load Dexcom Sandbox data: {str(e)}", None

    def _convert_dexcom_sandbox_to_dataframe(self):
        """Convert Dexcom Sandbox glucose data to DataFrame format"""
        try:
            glucose_data = self.dexcom_sandbox.get_glucose_data_for_ui()
            
            if not glucose_data:
                raise Exception("No glucose data available from Dexcom Sandbox")
            
            # Convert to DataFrame
            df = pd.DataFrame(glucose_data)
            
            # Ensure proper datetime conversion
            df['systemTime'] = pd.to_datetime(df['systemTime'])
            df['displayTime'] = pd.to_datetime(df['displayTime'])
            df['value'] = pd.to_numeric(df['value'], errors='coerce')
            
            # Sort by time
            df = df.sort_values('systemTime')
            
            # Set in data manager
            self.data_manager.processed_glucose_data = df
            
            # Calculate statistics using existing analyzer
            self.data_manager.calculated_stats = self.data_manager._calculate_unified_stats()
            self.data_manager.identified_patterns = GlucoseAnalyzer.identify_patterns(df)
            
            logger.info(f"✅ Converted {len(df)} Dexcom Sandbox readings to DataFrame")
            
        except Exception as e:
            logger.error(f"Failed to convert Dexcom Sandbox data: {e}")
            raise

    def _build_glucose_display(self) -> Tuple[str, go.Figure]:
        """Build glucose data display (common for demo and Dexcom Sandbox)"""
        # Get unified stats
        stats = self.data_manager.get_stats_for_ui()
        chart_data = self.data_manager.get_chart_data()
        
        # Sync chat with fresh data
        self._sync_chat_with_data_manager()
        
        if chart_data is None or chart_data.empty:
            return "No glucose data available", None

        # Build data summary with CONSISTENT metrics
        user = self.data_manager.current_user
        data_points = stats.get('total_readings', 0)
        avg_glucose = stats.get('average_glucose', 0)
        std_glucose = stats.get('std_glucose', 0)
        min_glucose = stats.get('min_glucose', 0)
        max_glucose = stats.get('max_glucose', 0)
        
        time_in_range = stats.get('time_in_range_70_180', 0)
        time_below_range = stats.get('time_below_70', 0)
        time_above_range = stats.get('time_above_180', 0)
        
        gmi = stats.get('gmi', 0)
        cv = stats.get('cv', 0)
        
        # Calculate date range
        end_date = datetime.now()
        start_date = end_date - timedelta(days=14)
        
        # Determine data source
        if self.current_user_type == "dexcom_sandbox":
            data_source = "Dexcom Sandbox OAuth"
            oauth_status = "✅ Authenticated Dexcom Sandbox with working OAuth"
        else:
            data_source = "Demo Data"
            oauth_status = "🎭 Using demo data for testing"
        
        data_summary = f"""
## 📊 Data Summary for {user.name}

### Basic Information
• **Data Type:** {data_source}
• **Analysis Period:** {start_date.strftime('%B %d, %Y')} to {end_date.strftime('%B %d, %Y')} (14 days)
• **Total Readings:** {data_points:,} glucose measurements
• **Device:** {user.device_type}

### Glucose Statistics
• **Average Glucose:** {avg_glucose:.1f} mg/dL
• **Standard Deviation:** {std_glucose:.1f} mg/dL
• **Coefficient of Variation:** {cv:.1f}%
• **Glucose Range:** {min_glucose:.0f} - {max_glucose:.0f} mg/dL
• **GMI (Glucose Management Indicator):** {gmi:.1f}%

### Time in Range Analysis
• **Time in Range (70-180 mg/dL):** {time_in_range:.1f}%
• **Time Below Range (<70 mg/dL):** {time_below_range:.1f}%
• **Time Above Range (>180 mg/dL):** {time_above_range:.1f}%

### Clinical Targets
• **Target Time in Range:** >70% (Current: {time_in_range:.1f}%)
• **Target Time Below Range:** <4% (Current: {time_below_range:.1f}%)
• **Target CV:** <36% (Current: {cv:.1f}%)

### Authentication Status
• **User Type:** {self.current_user_type.upper() if self.current_user_type else 'Unknown'}
• **OAuth Status:** {oauth_status}
        """

        chart = self.create_glucose_chart()
        
        return data_summary, chart

    def _sync_chat_with_data_manager(self):
        """Ensure chat uses the same data as the UI"""
        try:
            # Get context from unified data manager
            context = self.data_manager.get_context_for_agent()
            
            # Update chat's internal data to match
            if not context.get("error"):
                self.mistral_chat.current_user = self.data_manager.current_user
                self.mistral_chat.current_glucose_data = self.data_manager.processed_glucose_data
                self.mistral_chat.current_stats = self.data_manager.calculated_stats
                self.mistral_chat.current_patterns = self.data_manager.identified_patterns
                
                logger.info(f"Synced chat with data manager - TIR: {self.data_manager.calculated_stats.get('time_in_range_70_180', 0):.1f}%")
            
        except Exception as e:
            logger.error(f"Failed to sync chat with data manager: {e}")

    def _get_current_user_key(self) -> str:
        """Get the current user key"""
        if not self.data_manager.current_user:
            return ""
        
        # Find the key for current user
        for key, user in DEMO_USERS.items():
            if user == self.data_manager.current_user:
                return key
        return ""

    def get_template_prompts(self) -> List[str]:
        """Get template prompts based on current user data"""
        if not self.data_manager.current_user or not self.data_manager.calculated_stats:
            return [
                "What should I know about managing my diabetes?",
                "How can I improve my glucose control?"
            ]

        stats = self.data_manager.calculated_stats
        time_in_range = stats.get('time_in_range_70_180', 0)
        time_below_70 = stats.get('time_below_70', 0)

        templates = []

        if time_in_range < 70:
            templates.append(f"My time in range is {time_in_range:.1f}% which is below the 70% target. What specific strategies can help me improve it?")
        else:
            templates.append(f"My time in range is {time_in_range:.1f}% which meets the target. How can I maintain this level of control?")

        if time_below_70 > 4:
            templates.append(f"I'm experiencing {time_below_70:.1f}% time below 70 mg/dL. What can I do to prevent these low episodes?")
        else:
            templates.append("What are the best practices for preventing hypoglycemia in my situation?")

        # Add data source specific template
        if self.current_user_type == "dexcom_sandbox":
            templates.append("This is my Dexcom Sandbox OAuth-authenticated data. What insights can you provide about these glucose patterns?")
        else:
            templates.append("Based on this demo data, what would you recommend for someone with similar patterns?")

        return templates

    def chat_with_mistral(self, message: str, history: List) -> Tuple[str, List]:
        """Handle chat interaction with Mistral using unified data"""
        if not message.strip():
            return "", history

        if not self.data_manager.current_user:
            response = "Please select a user first (demo or Dexcom Sandbox) to get personalized insights about glucose data."
            history.append([message, response])
            return "", history

        try:
            # Ensure chat is synced with latest data
            self._sync_chat_with_data_manager()
            
            # Send message to Mistral chat
            result = self.mistral_chat.chat_with_mistral(message)
            
            if result['success']:
                response = result['response']
                
                # Add data consistency note
                validation = self.data_manager.validate_data_consistency()
                if validation.get('valid'):
                    data_age = validation.get('data_age_minutes', 0)
                    if data_age > 10:  # Warn if data is old
                        response += f"\n\n📊 *Note: Analysis based on data from {data_age} minutes ago. Reload data for most current insights.*"
                
                # Add data source context
                if self.current_user_type == "dexcom_sandbox":
                    response += f"\n\n🔐 *This analysis is based on your OAuth-authenticated Dexcom Sandbox data.*"
                else:
                    response += f"\n\n🎭 *This analysis is based on demo data for testing purposes.*"
                
                # Add context note if no user data was included
                if not result.get('context_included', True):
                    response += f"\n\n💡 *For more personalized advice, make sure your glucose data is loaded.*"
            else:
                response = f"I apologize, but I encountered an error: {result.get('error', 'Unknown error')}. Please try again or rephrase your question."

            history.append([message, response])
            return "", history

        except Exception as e:
            logger.error(f"Chat error: {str(e)}")
            error_response = f"I apologize, but I encountered an error while processing your question: {str(e)}. Please try rephrasing your question."
            history.append([message, error_response])
            return "", history

    def clear_chat_history(self) -> List:
        """Clear chat history"""
        self.chat_history = []
        self.mistral_chat.clear_conversation()
        return []

    def create_glucose_chart(self) -> Optional[go.Figure]:
        """Create an interactive glucose chart using unified data"""
        chart_data = self.data_manager.get_chart_data()
        
        if chart_data is None or chart_data.empty:
            return None

        fig = go.Figure()

        # Color code based on glucose ranges
        colors = []
        for value in chart_data['value']:
            if value < 70:
                colors.append('#E74C3C')  # Red for low
            elif value > 180:
                colors.append('#F39C12')  # Orange for high
            else:
                colors.append('#3498DB')  # Blue for in range

        fig.add_trace(go.Scatter(
            x=chart_data['systemTime'],
            y=chart_data['value'],
            mode='lines+markers',
            name='Glucose',
            line=dict(color='#2980B9', width=2),
            marker=dict(size=4, color=colors),
            hovertemplate='<b>%{y} mg/dL</b><br>%{x}<extra></extra>'
        ))

        # Add target range shading
        fig.add_hrect(
            y0=70, y1=180,
            fillcolor="rgba(52, 152, 219, 0.1)",
            layer="below",
            line_width=0,
            annotation_text="Target Range",
            annotation_position="top left"
        )

        # Add reference lines
        fig.add_hline(y=70, line_dash="dash", line_color="#E67E22",
                     annotation_text="Low (70 mg/dL)", annotation_position="right")
        fig.add_hline(y=180, line_dash="dash", line_color="#E67E22",
                     annotation_text="High (180 mg/dL)", annotation_position="right")
        fig.add_hline(y=54, line_dash="dot", line_color="#E74C3C",
                     annotation_text="Severe Low (54 mg/dL)", annotation_position="right")
        fig.add_hline(y=250, line_dash="dot", line_color="#E74C3C",
                     annotation_text="Severe High (250 mg/dL)", annotation_position="right")

        # Get current stats for title
        stats = self.data_manager.get_stats_for_ui()
        tir = stats.get('time_in_range_70_180', 0)
        
        if self.current_user_type == "dexcom_sandbox":
            data_type = "Dexcom Sandbox"
        else:
            data_type = "Demo Data"
        
        fig.update_layout(
            title={
                'text': f"14-Day Glucose Trends - {self.data_manager.current_user.name} ({data_type} - TIR: {tir:.1f}%)",
                'x': 0.5,
                'xanchor': 'center'
            },
            xaxis_title="Time",
            yaxis_title="Glucose (mg/dL)",
            hovermode='x unified',
            height=500,
            showlegend=False,
            plot_bgcolor='rgba(0,0,0,0)',
            paper_bgcolor='rgba(0,0,0,0)',
            font=dict(size=12),
            margin=dict(l=60, r=60, t=80, b=60)
        )

        fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='rgba(128,128,128,0.2)')
        fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='rgba(128,128,128,0.2)')

        return fig


def create_interface():
    """Create the Gradio interface with improved, cleaner design"""
    app = GlycoAIApp()

    # Clean blue-themed CSS
    custom_css = """
    /* Main header styling */
    .main-header {
        text-align: center;
        background: linear-gradient(135deg, #3498db 0%, #2980b9 100%);
        color: white;
        padding: 2rem;
        border-radius: 12px;
        margin-bottom: 2rem;
        box-shadow: 0 4px 20px rgba(52, 152, 219, 0.3);
    }
    
    /* Demo user buttons - consistent size and light blue */
    .demo-user-btn {
        background: linear-gradient(135deg, #85c1e9 0%, #5dade2 100%) !important;
        border: none !important;
        border-radius: 8px !important;
        padding: 1rem !important;
        font-size: 0.95rem !important;
        font-weight: 600 !important;
        color: white !important;
        box-shadow: 0 3px 12px rgba(93, 173, 226, 0.3) !important;
        transition: all 0.3s ease !important;
        min-height: 80px !important;
        text-align: center !important;
        width: 100% !important;
    }
    
    .demo-user-btn:hover {
        transform: translateY(-2px) !important;
        box-shadow: 0 6px 20px rgba(93, 173, 226, 0.4) !important;
        background: linear-gradient(135deg, #7fb3d3 0%, #5499c7 100%) !important;
    }
    
    /* Dexcom OAuth button - smaller and distinct */
    .dexcom-oauth-btn {
        background: linear-gradient(135deg, #2980b9 0%, #1f618d 100%) !important;
        border: none !important;
        border-radius: 8px !important;
        padding: 0.8rem 1.5rem !important;
        font-size: 0.9rem !important;
        font-weight: 600 !important;
        color: white !important;
        box-shadow: 0 3px 12px rgba(41, 128, 185, 0.3) !important;
        transition: all 0.3s ease !important;
        text-align: center !important;
    }
    
    .dexcom-oauth-btn:hover {
        transform: translateY(-1px) !important;
        box-shadow: 0 5px 16px rgba(41, 128, 185, 0.4) !important;
    }
    
    /* Prominent load data button */
    .load-data-btn {
        background: linear-gradient(135deg, #3498db 0%, #2980b9 100%) !important;
        border: none !important;
        border-radius: 12px !important;
        padding: 1.5rem 2rem !important;
        font-size: 1.1rem !important;
        font-weight: bold !important;
        color: white !important;
        box-shadow: 0 6px 24px rgba(52, 152, 219, 0.4) !important;
        transition: all 0.3s ease !important;
        min-height: 80px !important;
        text-align: center !important;
    }
    
    .load-data-btn:hover {
        transform: translateY(-2px) !important;
        box-shadow: 0 8px 32px rgba(52, 152, 219, 0.5) !important;
    }
    
    /* Tab styling - more visible */
    .gradio-tabs .tab-nav {
        background: linear-gradient(135deg, #e3f2fd 0%, #bbdefb 100%) !important;
        border-radius: 8px !important;
        padding: 0.5rem !important;
        margin-bottom: 1rem !important;
    }
    
    .gradio-tabs .tab-nav button {
        background: white !important;
        border: 1px solid #90caf9 !important;
        border-radius: 6px !important;
        margin: 0 0.25rem !important;
        padding: 0.75rem 1.5rem !important;
        font-weight: 600 !important;
        color: #1565c0 !important;
        transition: all 0.3s ease !important;
    }
    
    .gradio-tabs .tab-nav button:hover {
        background: linear-gradient(135deg, #e3f2fd 0%, #bbdefb 100%) !important;
        transform: translateY(-1px) !important;
    }
    
    .gradio-tabs .tab-nav button.selected {
        background: linear-gradient(135deg, #3498db 0%, #2980b9 100%) !important;
        color: white !important;
        border-color: #2980b9 !important;
        box-shadow: 0 3px 12px rgba(52, 152, 219, 0.3) !important;
    }
    
    /* Chat bubble styling for demo prompts */
    .demo-prompt-bubble {
        background: linear-gradient(135deg, #e3f2fd 0%, #bbdefb 100%);
        border: 1px solid #90caf9;
        border-radius: 15px;
        padding: 0.75rem 1rem;
        margin: 0.5rem 0;
        color: #1565c0;
        font-size: 0.9rem;
        cursor: pointer;
        transition: all 0.2s ease;
        display: inline-block;
        max-width: 80%;
    }
    
    .demo-prompt-bubble:hover {
        background: linear-gradient(135deg, #bbdefb 0%, #90caf9 100%);
        transform: translateY(-1px);
        box-shadow: 0 3px 8px rgba(52, 152, 219, 0.2);
    }
    
    /* Toggle styling */
    .oauth-toggle {
        background: #f8f9fa;
        border: 1px solid #e3f2fd;
        border-radius: 6px;
        padding: 0.5rem;
    }
    
    /* Notification styling */
    .notification-success {
        background: white !important;
        border: 2px solid #27ae60 !important;
        border-radius: 8px !important;
        padding: 1rem !important;
        margin: 1rem 0 !important;
        box-shadow: 0 4px 12px rgba(39, 174, 96, 0.2) !important;
        animation: slideIn 0.5s ease-out !important;
    }
    
    .notification-warning {
        background: white !important;
        border: 2px solid #f39c12 !important;
        border-radius: 8px !important;
        padding: 1rem !important;
        margin: 1rem 0 !important;
        box-shadow: 0 4px 12px rgba(243, 156, 18, 0.2) !important;
        animation: slideIn 0.5s ease-out !important;
    }
    
    .notification-critical {
        background: white !important;
        border: 2px solid #e74c3c !important;
        border-radius: 8px !important;
        padding: 1rem !important;
        margin: 1rem 0 !important;
        box-shadow: 0 4px 12px rgba(231, 76, 60, 0.2) !important;
        animation: slideIn 0.5s ease-out !important;
    }
    
    @keyframes slideIn {
        from {
            opacity: 0;
            transform: translateY(-20px);
        }
        to {
            opacity: 1;
            transform: translateY(0);
        }
    }
    
    /* Group styling */
    .user-selection-group {
        background: #f8f9fa;
        border: 1px solid #e3f2fd;
        border-radius: 8px;
        padding: 1.5rem;
        margin-bottom: 1rem;
    }
    
    /* Connection status */
    .connection-status {
        background: #e3f2fd;
        border: 1px solid #bbdefb;
        border-radius: 6px;
        padding: 1rem;
        color: #1565c0;
        font-weight: 500;
    }
    """

    with gr.Blocks(
        title="GlycoAI - AI Glucose Insights",
        theme=gr.themes.Soft(
            primary_hue="blue",
            secondary_hue="blue",
            neutral_hue="slate"
        ),
        css=custom_css
    ) as interface:

        # Clean Header
        with gr.Row():
            with gr.Column():
                gr.HTML("""
                <div class="main-header">
                    <div style="display: flex; align-items: center; justify-content: center; gap: 1rem;">
                        <div style="width: 50px; height: 50px; background: white; border-radius: 50%; display: flex; align-items: center; justify-content: center;">
                            <span style="color: #3498db; font-size: 20px; font-weight: bold;">🩺</span>
                        </div>
                        <div>
                            <h1 style="margin: 0; font-size: 2rem; color: white;">GlycoAI</h1>
                            <p style="margin: 0; font-size: 1rem; color: white; opacity: 0.9;">AI-Powered Glucose Insights</p>
                        </div>
                    </div>
                    <p style="margin-top: 1rem; font-size: 0.9rem; color: white; opacity: 0.8;">
                        Demo Users + Dexcom Sandbox OAuth • Chat with AI for personalized glucose insights
                    </p>
                </div>
                """)

        # User Selection Section - Cleaner Layout
        with gr.Row():
            with gr.Column():
                gr.Markdown("### 👥 Choose Your Data Source")
                
                # Demo Users Section
                with gr.Group():
                    gr.Markdown("#### 🎭 Demo Users")
                    gr.Markdown("*Instant access to realistic glucose data for testing*")
                    
                    with gr.Row():
                        with gr.Column(scale=1):
                            sarah_btn = gr.Button(
                                "Sarah Thompson\nG7 Mobile - ⚠️ Unstable Control",
                                elem_classes=["demo-user-btn"]
                            )
                        with gr.Column(scale=1):
                            marcus_btn = gr.Button(
                                "Marcus Rodriguez\nONE+ Mobile - Type 2",
                                elem_classes=["demo-user-btn"]
                            )
                        with gr.Column(scale=1):
                            jennifer_btn = gr.Button(
                                "Jennifer Chen\nG6 Mobile - Athletic",
                                elem_classes=["demo-user-btn"]
                            )
                        with gr.Column(scale=1):
                            robert_btn = gr.Button(
                                "Robert Williams\nG6 Receiver - Experienced",
                                elem_classes=["demo-user-btn"]
                            )

                # Show/Hide OAuth Toggle
                with gr.Row():
                    with gr.Column(scale=4):
                        pass
                    with gr.Column(scale=2):
                        show_oauth_toggle = gr.Checkbox(
                            label="Show Dexcom OAuth Options",
                            value=False,
                            container=False,
                            elem_classes=["oauth-toggle"]
                        )

                # Dexcom Sandbox OAuth Section (Collapsible)
                with gr.Group(visible=False) as oauth_section:
                    if DEXCOM_SANDBOX_AVAILABLE:
                        gr.Markdown("#### 🔐 Dexcom Sandbox OAuth")
                        gr.Markdown("*Connect with OAuth-authenticated sandbox data*")
                        
                        with gr.Row():
                            with gr.Column(scale=2):
                                dexcom_sandbox_btn = gr.Button(
                                    "🚀 Connect Dexcom Sandbox",
                                    elem_classes=["dexcom-oauth-btn"]
                                )
                            with gr.Column(scale=3):
                                oauth_instructions = gr.Markdown(
                                    "Click to start Dexcom Sandbox authentication",
                                    visible=True
                                )
                        
                        with gr.Row(visible=False) as oauth_completion_row:
                            with gr.Column():
                                callback_url_input = gr.Textbox(
                                    label="Paste Complete Callback URL",
                                    placeholder="http://localhost:7860/callback?code=ABC123XYZ&state=sandbox_test",
                                    lines=2
                                )
                                complete_oauth_btn = gr.Button(
                                    "✅ Complete OAuth",
                                    elem_classes=["dexcom-oauth-btn"]
                                )
                    else:
                        gr.Markdown("#### 🔒 Dexcom Sandbox OAuth")
                        gr.Markdown("*Not configured - demo users available*")
                        
                        gr.Button(
                            "🔒 Dexcom Sandbox Not Available",
                            interactive=False,
                            elem_classes=["dexcom-oauth-btn"]
                        )
                        
                        # Create dummy variables for consistency
                        oauth_instructions = gr.Markdown("", visible=False)
                        callback_url_input = gr.Textbox(visible=False)
                        complete_oauth_btn = gr.Button(visible=False)
                        oauth_completion_row = gr.Row(visible=False)

        # Connection Status
        with gr.Row():
            with gr.Column():
                connection_status = gr.Textbox(
                    label="Connection Status",
                    value="No user selected - Choose a demo user or connect Dexcom Sandbox",
                    interactive=False,
                    elem_classes=["connection-status"]
                )

        # Section Divider
        gr.HTML('<div class="section-divider"></div>')

        # Update button description for Sarah's unstable patterns
        with gr.Group(visible=False) as main_interface:
            # Prominent Load Data Button
            with gr.Row():
                with gr.Column(scale=1):
                    pass  # Left spacer
                with gr.Column(scale=2):
                    load_data_btn = gr.Button(
                        "📊 Load 14-Day Glucose Data\n🚀 Start Analysis & Enable AI Chat",
                        elem_classes=["load-data-btn"]
                    )
                with gr.Column(scale=1):
                    pass  # Right spacer
            
            # Notification area for data loading feedback
            with gr.Row():
                notification_area = gr.Markdown(
                    "",
                    visible=False,
                    elem_classes=["notification-success"]
                )

            # Section Divider
            gr.HTML('<div class="section-divider"></div>')

            # Main Content Tabs - Reordered with Chat first
            with gr.Tabs():
                # Chat Tab - FIRST for priority
                with gr.TabItem("💬 Chat with AI"):
                    with gr.Column():
                        gr.Markdown("### 🤖 Chat with GlycoAI")
                        
                        # Chat Interface with integrated demo prompts
                        chatbot = gr.Chatbot(
                            label="💬 Chat with GlycoAI",
                            height=450,
                            show_label=False,
                            container=True,
                            bubble_full_width=False,
                            avatar_images=(None, "🩺")
                        )

                        # Chat Input
                        with gr.Row():
                            chat_input = gr.Textbox(
                                placeholder="Ask about your glucose patterns, trends, or management strategies...",
                                label="Your Question",
                                lines=2,
                                scale=4
                            )
                            send_btn = gr.Button(
                                "Send",
                                variant="primary",
                                scale=1
                            )

                        # Chat Controls
                        with gr.Row():
                            clear_chat_btn = gr.Button(
                                "🗑️ Clear Chat",
                                size="sm"
                            )
                            gr.Markdown("*AI responses are for informational purposes only. Always consult your healthcare provider.*")

                # Data Overview Tab - SECOND
                with gr.TabItem("📋 Data Overview"):
                    with gr.Column():
                        gr.Markdown("### 📋 Comprehensive Data Analysis")
                        
                        data_display = gr.Markdown(
                            "Load your glucose data to see detailed statistics and insights",
                            container=True
                        )

                # Glucose Chart Tab - THIRD
                with gr.TabItem("📈 Glucose Chart"):
                    with gr.Column():
                        gr.Markdown("### 📊 Interactive 14-Day Glucose Analysis")
                        
                        glucose_chart = gr.Plot(
                            label="Interactive Glucose Trends",
                            container=True
                        )

        # Event Handlers
        def handle_demo_user_selection(user_key):
            status, interface_visibility = app.select_demo_user(user_key)
            initial_chat = app.initialize_chat_with_prompts()
            return status, interface_visibility, initial_chat

        def handle_load_data():
            overview, chart, notification = app.load_glucose_data()
            
            # Determine notification class based on content
            if "CONCERNING PATTERNS" in notification or "CRITICAL" in notification:
                notification_class = "notification-critical"
            elif "EXCELLENT CONTROL" in notification:
                notification_class = "notification-success"
            elif notification:
                notification_class = "notification-warning"
            else:
                notification_class = "notification-success"
            
            # Show notification with appropriate styling
            notification_update = gr.update(
                value=notification,
                visible=bool(notification),
                elem_classes=[notification_class]
            )
            
            return overview, chart, notification_update

        def handle_chat_submit(message, history):
            return app.chat_with_mistral(message, history)

        def handle_enter_key(message, history):
            if message.strip():
                return app.chat_with_mistral(message, history)
            return "", history

        def handle_chatbot_click(history, evt: gr.SelectData):
            """Handle clicking on chat bubbles (demo prompts)"""
            if evt.index is not None and len(history) > evt.index[0]:
                clicked_message = history[evt.index[0]][1]  # Get AI message
                
                # Check if it's a demo prompt (contains ** formatting)
                if "**" in clicked_message and ("🎯" in clicked_message or "⚡" in clicked_message or "🍽️" in clicked_message):
                    return app.handle_demo_prompt_click(clicked_message, history)
            
            return "", history

        # Toggle OAuth section visibility
        show_oauth_toggle.change(
            lambda show: gr.update(visible=show),
            inputs=[show_oauth_toggle],
            outputs=[oauth_section]
        )

        # Connect Event Handlers for Demo Users
        sarah_btn.click(
            lambda: handle_demo_user_selection("sarah_g7"),
            outputs=[connection_status, main_interface, chatbot]
        )

        marcus_btn.click(
            lambda: handle_demo_user_selection("marcus_one"),
            outputs=[connection_status, main_interface, chatbot]
        )

        jennifer_btn.click(
            lambda: handle_demo_user_selection("jennifer_g6"),
            outputs=[connection_status, main_interface, chatbot]
        )

        robert_btn.click(
            lambda: handle_demo_user_selection("robert_receiver"),
            outputs=[connection_status, main_interface, chatbot]
        )

        # Connect Event Handlers for Dexcom Sandbox OAuth
        if DEXCOM_SANDBOX_AVAILABLE:
            dexcom_sandbox_btn.click(
                app.start_dexcom_sandbox_oauth,
                outputs=[oauth_instructions]
            ).then(
                lambda: gr.update(visible=True),
                outputs=[oauth_completion_row]
            )

            complete_oauth_btn.click(
                app.complete_dexcom_sandbox_oauth,
                inputs=[callback_url_input],
                outputs=[connection_status, main_interface]
            ).then(
                app.initialize_chat_with_prompts,  # Initialize chat with prompts after OAuth
                outputs=[chatbot]
            )

        # Data Loading
        load_data_btn.click(
            handle_load_data,
            outputs=[data_display, glucose_chart, notification_area]
        )

        # Chat Handlers
        send_btn.click(
            handle_chat_submit,
            inputs=[chat_input, chatbot],
            outputs=[chat_input, chatbot]
        )

        chat_input.submit(
            handle_enter_key,
            inputs=[chat_input, chatbot],
            outputs=[chat_input, chatbot]
        )

        # Handle clicking on chat bubbles (demo prompts)
        chatbot.select(
            handle_chatbot_click,
            inputs=[chatbot],
            outputs=[chat_input, chatbot]
        )

        # Clear Chat
        clear_chat_btn.click(
            app.clear_chat_history,
            outputs=[chatbot]
        )

        # Clean Footer
        with gr.Row():
            gr.HTML(f"""
            <div style="text-align: center; padding: 1.5rem; margin-top: 2rem; border-top: 1px solid #e3f2fd; color: #546e7a;">
                <p><strong>⚠️ Medical Disclaimer</strong></p>
                <p style="font-size: 0.9rem;">GlycoAI is for informational and educational purposes only. Always consult your healthcare provider 
                before making any changes to your diabetes management plan.</p>
                <p style="margin-top: 1rem; font-size: 0.85rem; color: #78909c;">
                    🔒 Data processed securely • 💡 Powered by Dexcom API & Mistral AI<br>
                    🎭 Demo: Available • 🔐 Dexcom Sandbox: {"Available" if DEXCOM_SANDBOX_AVAILABLE else "Not configured"}
                </p>
            </div>
            """)

    return interface


def main():
    """Main function to launch the application"""
    print("🚀 Starting GlycoAI - AI-Powered Glucose Insights...")
    
    # Check OAuth availability
    oauth_status = "✅ Available" if DEXCOM_SANDBOX_AVAILABLE else "❌ Not configured"
    print(f"🎯 Dexcom Sandbox OAuth: {oauth_status}")
    
    # Validate environment before starting
    print("🔍 Validating environment configuration...")
    if not validate_environment():
        print("❌ Environment validation failed!")
        print("Please check your .env file or environment variables.")
        return
    
    print("✅ Environment validation passed!")
    
    try:
        # Create and launch the interface
        demo = create_interface()
        
        print("🎯 GlycoAI Features:")
        print("📊 Clean UI with blue theme, consistent button sizes, improved readability")
        print("🎭 Demo users: 4 realistic profiles for instant testing")
        if DEXCOM_SANDBOX_AVAILABLE:
            print("✅ Dexcom Sandbox: Available - OAuth authentication ready")
        else:
            print("🔒 Dexcom Sandbox: Not configured - demo users only")
        
        # Launch with custom settings
        demo.launch(
            server_name="0.0.0.0",  # Allow external access
            server_port=7860,       # Your port
            share=True,            # Set to True for public sharing (tunneling)
            debug=os.getenv("DEBUG", "false").lower() == "true",
            show_error=True,        # Show errors in the interface
            auth=None,              # No authentication required
            favicon_path=None,      # Use default favicon
            ssl_verify=False        # Disable SSL verification for development
        )
        
    except Exception as e:
        logger.error(f"Failed to launch GlycoAI application: {e}")
        print(f"❌ Error launching application: {e}")
        
        # Provide helpful error information
        if "environment" in str(e).lower():
            print("\n💡 Environment troubleshooting:")
            print("1. Check if .env file exists with MISTRAL_API_KEY")
            print("2. Verify your API key is valid")
            print("3. For Hugging Face Spaces, check Repository secrets")
        else:
            print("\n💡 Try checking:")
            print("1. All dependencies are installed: pip install -r requirements.txt")
            print("2. Port 7860 is available")
            print("3. Check the logs above for specific error details")
        
        raise


if __name__ == "__main__":
    # Setup logging configuration
    log_level = os.getenv("LOG_LEVEL", "INFO")
    logging.basicConfig(
        level=getattr(logging, log_level.upper()),
        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
        handlers=[
            logging.FileHandler('glycoai.log'),
            logging.StreamHandler()
        ]
    )
    
    # Run the main application
    main()