import os
import gradio as gr
import json
from datetime import datetime
from backend.conversation_engine import ConversationEngine
from backend.storage import Storage


# Global conversation engine instance
engine = ConversationEngine()
storage = Storage("data")


def chat_interface(message, history):
    """
    Main chat interface handler compatible with Gradio 6.x.
    
    Args:
        message: User's input message
        history: Chat history list of message dicts
    
    Returns:
        Updated chat history with new bot response
    """
    if not message.strip():
        return history
    
    # Process message through engine
    response, metadata = engine.process_message(message)
    
    # Return in Gradio 6.x format: list of {"role": "user"/"assistant", "content": "..."}
    return history + [{"role": "user", "content": message}, {"role": "assistant", "content": response}]


def new_conversation():
    """Reset conversation for new session."""
    engine.reset()
    return [], "✅ Conversation reset. Start fresh!"


def get_admin_leads_view():
    """Get HTML view of all leads."""
    leads = storage.get_all_leads()
    
    if not leads:
        return "<p>No leads yet.</p>"
    
    html = "<table style='width:100%; border-collapse: collapse;'>"
    html += "<tr style='background: #f0f0f0;'><th style='border: 1px solid #ddd; padding: 8px;'>ID</th><th style='border: 1px solid #ddd; padding: 8px;'>Name</th><th style='border: 1px solid #ddd; padding: 8px;'>Business</th><th style='border: 1px solid #ddd; padding: 8px;'>Budget</th><th style='border: 1px solid #ddd; padding: 8px;'>Email</th><th style='border: 1px solid #ddd; padding: 8px;'>Score</th><th style='border: 1px solid #ddd; padding: 8px;'>Created</th></tr>"
    
    for lead in reversed(leads[-20:]):  # Show last 20
        name = lead.get("user_name", "N/A")
        business = lead.get("business_type", "N/A")[:30]
        budget = lead.get("budget", "N/A")[:20]
        email = lead.get("email", "N/A")
        score_info = lead.get("scoring", {})
        score = f"{score_info.get('score', 0)} ({score_info.get('classification', 'Unknown')})"
        created = lead.get("created_at", "N/A").split("T")[0]
        
        html += f"<tr><td style='border: 1px solid #ddd; padding: 8px;'>{lead['id'][:10]}...</td><td style='border: 1px solid #ddd; padding: 8px;'>{name}</td><td style='border: 1px solid #ddd; padding: 8px;'>{business}</td><td style='border: 1px solid #ddd; padding: 8px;'>{budget}</td><td style='border: 1px solid #ddd; padding: 8px;'>{email}</td><td style='border: 1px solid #ddd; padding: 8px;'>{score}</td><td style='border: 1px solid #ddd; padding: 8px;'>{created}</td></tr>"
    
    html += "</table>"
    return html


def get_admin_tickets_view():
    """Get HTML view of all support tickets."""
    tickets = storage.get_all_tickets()
    
    if not tickets:
        return "<p>No support tickets yet.</p>"
    
    html = "<table style='width:100%; border-collapse: collapse;'>"
    html += "<tr style='background: #f0f0f0;'><th style='border: 1px solid #ddd; padding: 8px;'>ID</th><th style='border: 1px solid #ddd; padding: 8px;'>Name</th><th style='border: 1px solid #ddd; padding: 8px;'>Issue</th><th style='border: 1px solid #ddd; padding: 8px;'>Reference</th><th style='border: 1px solid #ddd; padding: 8px;'>Status</th><th style='border: 1px solid #ddd; padding: 8px;'>Created</th></tr>"
    
    for ticket in reversed(tickets[-20:]):  # Show last 20
        name = ticket.get("user_name", "N/A")
        issue = ticket.get("issue_description", "N/A")[:50]
        ref_id = ticket.get("reference_id", "N/A")[:20]
        status = ticket.get("status", "N/A")
        created = ticket.get("created_at", "N/A").split("T")[0]
        
        html += f"<tr><td style='border: 1px solid #ddd; padding: 8px;'>{ticket['id'][:10]}...</td><td style='border: 1px solid #ddd; padding: 8px;'>{name}</td><td style='border: 1px solid #ddd; padding: 8px;'>{issue}</td><td style='border: 1px solid #ddd; padding: 8px;'>{ref_id}</td><td style='border: 1px solid #ddd; padding: 8px;'><span style='background: #fff3cd; padding: 2px 6px; border-radius: 3px;'>{status}</span></td><td style='border: 1px solid #ddd; padding: 8px;'>{created}</td></tr>"
    
    html += "</table>"
    return html


def get_admin_logs_view():
    """Get HTML view of conversation logs summary."""
    convs = storage.get_all_conversations()
    summary = storage.get_summary()
    
    html = f"""
    <div style='padding: 16px; background: #f9f9f9; border-radius: 8px;'>
        <h3>System Summary</h3>
        <p><strong>Total Conversations:</strong> {summary['total_conversations']}</p>
        <p><strong>Total Leads:</strong> {summary['total_leads']}</p>
        <p><strong>Total Support Tickets:</strong> {summary['total_tickets']}</p>
    </div>
    
    <h3>Recent Conversations</h3>
    """
    
    if not convs:
        html += "<p>No conversations logged yet.</p>"
    else:
        html += "<table style='width:100%; border-collapse: collapse;'>"
        html += "<tr style='background: #f0f0f0;'><th style='border: 1px solid #ddd; padding: 8px;'>ID</th><th style='border: 1px solid #ddd; padding: 8px;'>Intent</th><th style='border: 1px solid #ddd; padding: 8px;'>Messages</th><th style='border: 1px solid #ddd; padding: 8px;'>Timestamp</th></tr>"
        
        for conv in reversed(convs[-20:]):  # Show last 20
            intent = conv.get("intent", "unknown")
            msg_count = len(conv.get("messages", []))
            timestamp = conv.get("timestamp", "N/A").split("T")[0]
            
            html += f"<tr><td style='border: 1px solid #ddd; padding: 8px;'>{conv['id'][:10]}...</td><td style='border: 1px solid #ddd; padding: 8px;'>{intent}</td><td style='border: 1px solid #ddd; padding: 8px;'>{msg_count}</td><td style='border: 1px solid #ddd; padding: 8px;'>{timestamp}</td></tr>"
        
        html += "</table>"
    
    return html


def export_leads_json():
    """Export leads as JSON."""
    leads = storage.get_all_leads()
    return json.dumps(leads, indent=2)


def export_tickets_json():
    """Export tickets as JSON."""
    tickets = storage.get_all_tickets()
    return json.dumps(tickets, indent=2)


# Create Gradio interface (theme parameter moved to launch() in Gradio 6.x)
with gr.Blocks(title="AI Support Chatbot") as demo:
    gr.Markdown("# 🤖 AI Customer Support & Lead Qualification Chatbot")
    gr.Markdown("Ask questions, qualify for services, or report issues. The AI will route your request appropriately.")
    
    with gr.Tabs():
        # Tab 1: Chat Interface
        with gr.Tab("💬 Chat"):
            with gr.Column():
                chatbot = gr.Chatbot(label="Conversation", height=500)
                
                with gr.Row():
                    msg = gr.Textbox(
                        label="Your Message",
                        placeholder="Type your message here...",
                        lines=2
                    )
                    submit = gr.Button("Send", size="lg", variant="primary")
                
                with gr.Row():
                    new_chat_btn = gr.Button("🔄 New Conversation", size="sm")
                    clear_history = gr.Button("🗑️ Clear Chat", size="sm")
                
                # Info box
                gr.Markdown("""
                ### How to use:
                - **Lead Inquiry**: Ask about pricing, services, or your business problem
                - **Support Issue**: Describe any technical problems or issues
                - **General Questions**: Ask about our company and services
                
                The AI will detect your intent and respond accordingly!
                """)
        
        # Tab 2: Admin Dashboard
        with gr.Tab("📊 Admin Panel"):
            gr.Markdown("## Admin Dashboard")
            
            with gr.Tabs():
                with gr.Tab("👥 Leads"):
                    leads_html = gr.HTML()
                    refresh_leads = gr.Button("🔄 Refresh Leads")
                    refresh_leads.click(get_admin_leads_view, outputs=leads_html)
                    
                    leads_html.load(get_admin_leads_view, outputs=leads_html)
                
                with gr.Tab("🎟️ Support Tickets"):
                    tickets_html = gr.HTML()
                    refresh_tickets = gr.Button("🔄 Refresh Tickets")
                    refresh_tickets.click(get_admin_tickets_view, outputs=tickets_html)
                    
                    tickets_html.load(get_admin_tickets_view, outputs=tickets_html)
                
                with gr.Tab("📋 Logs & Summary"):
                    logs_html = gr.HTML()
                    refresh_logs = gr.Button("🔄 Refresh Logs")
                    refresh_logs.click(get_admin_logs_view, outputs=logs_html)
                    
                    logs_html.load(get_admin_logs_view, outputs=logs_html)
                
                with gr.Tab("📥 Export"):
                    gr.Markdown("### Export Data")
                    
                    with gr.Row():
                        with gr.Column():
                            gr.Markdown("**Leads Export**")
                            export_leads_btn = gr.Button("Export Leads as JSON")
                            leads_json = gr.Textbox(label="Leads JSON", lines=10, interactive=False)
                            export_leads_btn.click(export_leads_json, outputs=leads_json)
                        
                        with gr.Column():
                            gr.Markdown("**Support Tickets Export**")
                            export_tickets_btn = gr.Button("Export Tickets as JSON")
                            tickets_json = gr.Textbox(label="Tickets JSON", lines=10, interactive=False)
                            export_tickets_btn.click(export_tickets_json, outputs=tickets_json)
        
        # Tab 3: About
        with gr.Tab("ℹ️ About"):
            gr.Markdown("""
            # About This Chatbot
            
            ## Features
            - **Intent Detection**: Automatically classifies user messages (lead, support, general)
            - **Multi-Flow Routing**: Routes conversations based on detected intent
            - **Lead Qualification**: Captures lead information with automatic scoring
            - **Support Ticketing**: Creates support tickets with conversation context
            - **Smart Memory**: Maintains conversation context (last 10 messages)
            - **FAQ Retrieval**: Answers common questions from knowledge base
            - **Hallucination Control**: Grounds responses and avoids making up information
            - **Lead Scoring**: Rates leads as Hot/Warm/Cold based on engagement signals
            
            ## Technology Stack
            - **Frontend**: Gradio (Python)
            - **Backend**: Python with modular architecture
            - **AI**: Google Gemini 2.5 Flash API
            - **Storage**: JSON files
            
            ## Designed for Hugging Face Spaces
            This application is built to run directly on Hugging Face Spaces without additional infrastructure.
            
            ---
            Built with ❤️ for customer support & lead qualification
            """)
    
    # Event handlers
    def handle_chat_submit(message, history):
        """Handle chat message submission and save to log."""
        new_history = chat_interface(message, history)
        
        # Save to conversation log
        if message.strip():
            try:
                storage.save_conversation({
                    "intent": engine.current_intent,
                    "user_name": engine.memory.user_name,
                    "message_count": len(new_history),
                    "timestamp": datetime.now().isoformat()
                })
            except:
                pass  # Silently fail if logging fails
        
        return new_history, ""
    
    submit.click(handle_chat_submit, [msg, chatbot], [chatbot, msg])
    msg.submit(handle_chat_submit, [msg, chatbot], [chatbot, msg])
    
    new_chat_btn.click(new_conversation, outputs=[chatbot, gr.State()])
    clear_history.click(lambda: [], outputs=chatbot)


if __name__ == "__main__":
    # Check for API key
    if not os.getenv("GEMINI_API_KEY"):
        print("⚠️  WARNING: GEMINI_API_KEY not set. The chatbot will use pattern-based fallback.")
        print("Set the GEMINI_API_KEY environment variable to enable AI-powered responses.")
    
    # Launch the app (theme parameter for Gradio 6.x)
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,
        show_error=True,
        theme=gr.themes.Soft()
    )