#!/usr/bin/env python3
"""
Hockey Mind AI Chatbot - HuggingFace Spaces Deployment
Optimized for HuggingFace Spaces with database integration
"""
import gradio as gr
import asyncio
import os
from dotenv import load_dotenv
from Original_OpenAPI_DB import agentic_hockey_chat

# Load environment variables
load_dotenv()

# Global variable to track if resources are loaded
resources_loaded = False

async def chat_interface(user_role, user_team, user_prompt):
    """Interface function for Gradio with HockeyFood database integration"""
    global resources_loaded
    
    try:
        # Validate inputs - allow greetings through
        if not user_prompt or (len(user_prompt.strip()) < 3 and user_prompt.lower().strip() not in ["hi", "hey", "hello", "hoi", "hallo"]):
            return "Please enter a hockey question (minimum 3 characters).", ""
        
        # Load resources on first use to save memory
        if not resources_loaded:
            try:
                from Original_OpenAPI_DB import load_resources
                load_resources()
                resources_loaded = True
            except ImportError as import_err:
                # Fallback for HuggingFace without full database
                return f"Hockey AI: I can help with general hockey advice.\n\nYour question: {user_prompt}\n\nThis appears to be a hockey-related question. I'd recommend focusing on basic hockey fundamentals, practice drills, and asking specific technique questions.", "Limited mode - no database"
            except Exception as load_err:
                return f"System initializing... Please try again in a moment.", "Loading resources"
        
        # Call the main chat function with timeout
        try:
            # Reasonable prompt length limits for HuggingFace
            if len(user_prompt) > 200:
                user_prompt = user_prompt[:200] + "..."
                
            result = await asyncio.wait_for(
                agentic_hockey_chat(user_role, user_team, user_prompt), 
                timeout=25.0
            )
        except asyncio.TimeoutError:
            return "Request timed out. Please try a shorter question.", ""
        
        # Format response for Gradio with length limits
        ai_response = result.get('ai_response', 'Sorry, no response generated.')
        
        # Reasonable limits for HuggingFace deployment
        if len(ai_response) > 600:
            ai_response = ai_response[:600] + "..."
            
        recommendations = result.get('recommended_content_details', [])
        
        # Format recommendations with limits
        rec_text = ""
        if recommendations and len(recommendations) > 0:
            try:
                rec_list = []
                for i, rec in enumerate(recommendations[:3]):  # Limit to 3 items
                    if i >= 3:  # Double check
                        break
                    title = str(rec.get('title', 'No title'))[:60]  # Limit title length
                    content_type = str(rec.get('type', 'unknown'))
                    
                    # Show URL for multimedia items, content type for others
                    if content_type == 'multimedia' and rec.get('url'):
                        url = str(rec.get('url', ''))[:50]  # Limit URL length
                        item = f"{i+1}. [{content_type}] {title}\n   🔗 {url}"
                    else:
                        item = f"{i+1}. [{content_type}] {title}"
                    
                    rec_list.append(item)
                
                if rec_list:
                    rec_text = "Recommended Content:\n" + "\n".join(rec_list)
                    if len(rec_text) > 300:
                        rec_text = rec_text[:300] + "..."
            except Exception as rec_err:
                rec_text = f"Recommendations unavailable"
        
        return ai_response, rec_text
        
    except Exception as e:
        # Simplified error for HuggingFace
        return "Sorry, there was an issue processing your request. Please try again with a simpler question.", ""

def sync_chat_interface(user_role, user_team, user_prompt):
    """Synchronous wrapper for Gradio"""
    return asyncio.run(chat_interface(user_role, user_team, user_prompt))

# Gradio Interface
with gr.Blocks(
    title="🏒 Hockey Mind AI Chatbot",
    theme=gr.themes.Soft(),
    css="""
    .gradio-container {max-width: 900px !important; margin: auto !important;}
    .main-header {text-align: center; margin-bottom: 2rem;}
    .feature-badge {
        background: linear-gradient(45deg, #FF6B6B, #4ECDC4);
        color: white;
        padding: 5px 10px;
        border-radius: 15px;
        font-size: 0.8em;
        font-weight: bold;
        margin: 5px;
        display: inline-block;
    }
    """
) as demo:
    
    gr.HTML("""
    <div class="main-header">
        <h1>🏒 Hockey Mind AI Chatbot</h1>
        <div class="feature-badge">🗄️ Hockey Database</div>
        <div class="feature-badge">🎯 Smart Content Search</div>
        <div class="feature-badge">🌐 Multi-language</div>
        <p>Get personalized hockey advice with training content and drills!</p>
    </div>
    """)
    
    with gr.Row():
        with gr.Column():
            user_role = gr.Dropdown(
                choices=["le Trainer", "le Coach", "Speler"],
                label="Your Role 👤",
                value="Speler"
            )
            
            user_team = gr.Textbox(
                label="Team/Level 🏒",
                placeholder="e.g., U10, Beginner, Advanced",
                value="U10"
            )
            
            user_prompt = gr.Textbox(
                label="Your Question ❓",
                placeholder="Ask about drills, techniques, strategies, rules...",
                lines=3
            )
            
            submit_btn = gr.Button("Get Hockey Advice 🚀", variant="primary", size="lg")
    
    with gr.Row():
        ai_response = gr.Textbox(
            label="🤖 AI Response",
            lines=8,
            interactive=False
        )
    
    with gr.Row():
        recommendations = gr.Textbox(
            label="🏒 Recommended Content",
            lines=6,
            interactive=False
        )
    
    # Examples section
    gr.HTML("<br><h3>💡 Example Questions:</h3>")
    
    examples = gr.Examples(
        examples=[
            ["Speler", "U8", "What are the best backhand shooting drills for young players?"],
            ["Speler", "Intermediate", "Show me exercises for improving stick handling"],
            ["le Coach", "U10", "What training drills help with ball control?"],
            ["Speler", "Advanced", "Find training content for penalty corners"],
        ],
        inputs=[user_role, user_team, user_prompt],
        outputs=[ai_response, recommendations],
        fn=sync_chat_interface,
    )
    
    # Event handlers
    submit_btn.click(
        fn=sync_chat_interface,
        inputs=[user_role, user_team, user_prompt],
        outputs=[ai_response, recommendations],
        api_name="chat"
    )
    
    user_prompt.submit(
        fn=sync_chat_interface,
        inputs=[user_role, user_team, user_prompt],
        outputs=[ai_response, recommendations]
    )
    
    # Footer
    gr.HTML("""
    <br>
    <div style="text-align: center; color: #666; font-size: 0.9em; border-top: 1px solid #eee; padding-top: 20px;">
        <p>🏒 <strong>Hockey Mind AI - Database Edition</strong></p>
        <p>Powered by OpenRouter & Sentence Transformers</p>
        <p>🌍 Supports English & Dutch | Built for field hockey</p>
    </div>
    """)

# Launch for HuggingFace Spaces
if __name__ == "__main__":
    print("🚀 Launching Hockey Mind AI on HuggingFace Spaces...")
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,
        show_error=True,
        quiet=False
    )