app.py

#!/usr/bin/env python3
"""
Hockey Mind AI Chatbot - Fixed Gradio Interface for Hugging Face Spaces
"""
import gradio as gr
import asyncio
import os
from dotenv import load_dotenv
from 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"""
    global resources_loaded
    
    try:
        # Load resources on first use to save memory
        if not resources_loaded:
            try:
                from OpenAPI_DB import load_resources
                load_resources()
                resources_loaded = True
            except ImportError as import_err:
                return f"Import Error: {str(import_err)}. Please check if all required packages are installed.", "Unable to load ML models."
        
        # Call the main chat function
        result = await agentic_hockey_chat(user_role, user_team, user_prompt)
        
        # Format response for Gradio
        ai_response = result.get('ai_response', 'Sorry, no response generated.')
        recommendations = result.get('recommended_content_details', [])
        
        # Format recommendations as HTML
        rec_html = ""
        if recommendations:
            rec_html = "<h3>🏒 Recommended Videos:</h3><ul>"
            for rec in recommendations[:5]:
                title = rec.get('title', 'No title')
                url = rec.get('url', '#')
                similarity = rec.get('similarity', 0)
                rec_html += f"<li><a href='{url}' target='_blank'>{title}</a> (Similarity: {similarity:.3f})</li>"
            rec_html += "</ul>"
        
        return ai_response, rec_html
        
    except Exception as e:
        import traceback
        error_details = traceback.format_exc()
        return f"Error: {str(e)}\n\nDetails:\n{error_details}", "No recommendations available due to error."

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: 800px !important; margin: auto !important;}
    .main-header {text-align: center; margin-bottom: 2rem;}
    """
) as demo:
    
    gr.HTML("""
    <div class="main-header">
        <h1>🏒 Hockey Mind AI Chatbot</h1>
        <p>Get personalized hockey advice and video recommendations!</p>
        <p><i>Optimized for field hockey coaching, training, and player development</i></p>
    </div>
    """)
    
    with gr.Row():
        with gr.Column():
            user_role = gr.Dropdown(
                choices=["le Trainer", "le Coach", "Speler"],
                label="Your Role 👤",
                value="Coach"
            )
            
            user_team = gr.Textbox(
                label="Team/Level 🏒",
                placeholder="e.g., U8C, Toronto Maple Leafs, Beginner",
                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.HTML()
    
    # Examples section
    gr.HTML("<br><h3>💡 Example Questions:</h3>")
    
    examples = gr.Examples(
        examples=[
            ["Coach", "U8C", "What are the best backhand shooting drills for young players?"],
            ["Player", "Intermediate", "How can I improve my penalty corner technique?"],
            ["le Coach", "U10", "Geef me oefeningen voor backhandschoten"],
            ["Parent", "Beginner", "What equipment does my child need to start playing hockey?"],
            ["Coach", "Advanced", "What are effective small-sided games for skill development?"],
        ],
        inputs=[user_role, user_team, user_prompt],
        outputs=[ai_response, recommendations],
        fn=sync_chat_interface,
    )
    
    # Event handler
    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;">
        <p>🏒 Hockey Mind AI - Powered by OpenRouter & Sentence Transformers</p>
        <p>Supports English & Dutch | Built for field hockey community</p>
    </div>
    """)

# Launch configuration for Hugging Face Spaces
if __name__ == "__main__":
    # Check if running on Hugging Face Spaces
    if os.getenv("SPACE_ID"):
        # Production mode on HF Spaces
        demo.launch(
            server_name="0.0.0.0",
            server_port=7860,
            share=False,
            show_error=True,
            quiet=False
        )
    else:
        # Local development mode
        demo.launch(
            share=True,
            show_error=True
        )