app.py

"""
Gradio UI for the A11y Expert Agent with lazy initialization.
This module creates a Gradio ChatInterface that starts FAST,
then initializes the agent in the background.
"""
import sys
import os

# Suppress asyncio cleanup warnings by setting environment variable
os.environ['PYTHONUNBUFFERED'] = '1'

# Suppress all asyncio warnings at the earliest possible point
import warnings
warnings.filterwarnings('ignore', category=ResourceWarning)

import gradio as gr
from loguru import logger
import atexit
import threading
from agent.a11y_agent import create_agent, A11yExpertAgent
from config import get_settings

# --- Setup ---
# Configure logger
logger.remove()
logger.add(sys.stderr, level=get_settings().log_level)

# Global agent instance
agent_instance: A11yExpertAgent = None
agent_ready = False
agent_error = None

# --- Agent Initialization ---
def initialize_agent_background():
    """Initialize the agent in background thread."""
    global agent_instance, agent_ready, agent_error
    
    try:
        logger.info("🔄 Starting agent initialization in background...")
        import time
        
        logger.info("⏱️ Sleeping 2 seconds to avoid race condition...")
        time.sleep(2)
        
        logger.info("📦 Calling create_agent()...")
        agent_instance = create_agent()
        
        logger.info("✓ Agent instance created, setting ready flag...")
        agent_ready = True
        logger.success("✅ A11y Expert Agent is ready!")
    except Exception as e:
        logger.error(f"❌ Failed to initialize agent: {e}")
        import traceback
        logger.error(traceback.format_exc())
        agent_error = str(e)
        agent_instance = None

def cleanup_resources():
    """Clean up resources on app shutdown."""
    global agent_instance
    logger.info("Cleaning up resources...")
    try:
        # Close agent and all its resources
        if agent_instance:
            agent_instance.close()
        
        # Close embeddings client singleton if it exists
        from models.embeddings import get_embeddings_client
        if hasattr(get_embeddings_client, '_instance'):
            get_embeddings_client._instance.close()
        
        logger.success("✅ Resources cleaned up successfully")
    except Exception as e:
        logger.warning(f"Error during cleanup: {e}")

# --- Gradio Chat Logic ---
def respond(message: str, history: list[list[str]]):
    """
    Main function for the Gradio ChatInterface.
    Receives a user message and chat history, then uses the agent
    to generate a streaming response.
    Args:
        message: The user's input message.
        history: The conversation history provided by Gradio.
    Yields:
        A stream of response chunks to update the UI.
    """
    global agent_instance, agent_ready, agent_error
    
    # Initialize agent on first request if not already initialized
    if not agent_ready and not agent_error and agent_instance is None:
        yield "⏳ Initializing agent for first use, please wait..."
        try:
            logger.info("🔄 Initializing agent on first request...")
            agent_instance = create_agent()
            agent_ready = True
            logger.success("✅ A11y Expert Agent is ready!")
        except Exception as e:
            logger.error(f"❌ Failed to initialize agent: {e}")
            import traceback
            logger.error(traceback.format_exc())
            agent_error = str(e)
            agent_instance = None
            yield f"❌ Agent initialization failed: {agent_error}"
            return
    
    # Check if agent failed to initialize
    if agent_error:
        yield f"❌ Agent initialization failed: {agent_error}"
        return
    
    if not agent_instance:
        yield "❌ Agent not available. Please check logs for errors."
        return

    logger.info(f"User query: '{message}'")
    full_response = ""
    try:
        for chunk in agent_instance.ask(message):
            full_response += chunk
            yield full_response
    except Exception as e:
        logger.error(f"Error during response generation: {e}")
        yield f"An error occurred: {e}"


# --- Gradio UI Definition ---
# Two-column layout: Chat on left, Markdown content on right
with gr.Blocks(title="A11y Expert") as demo:
    gr.Markdown("# 🤖 A11y Expert")
    gr.Markdown("Twój inteligentny asystent do spraw dostępności cyfrowej.")
    
    with gr.Row():
        # Left column: Chatbot
        with gr.Column(scale=1):
            gr.Markdown("### 💬 Chat")
            chatbot = gr.Chatbot(height=500, show_label=False)
            msg = gr.Textbox(
                placeholder="Zadaj pytanie o WCAG, ARIA, lub poproś o analizę kodu...",
                show_label=False,
                container=False,
                max_length=300
            )
            
            with gr.Row():
                submit = gr.Button("Wyślij", variant="primary")
                clear = gr.Button("Wyczyść")
            
            # Example questions
            gr.Examples(
                examples=[
                    "Jakie są wymagania WCAG 2.2 dla etykiet formularzy?",
                    "Wyjaśnij rolę 'alert' w ARIA i podaj przykład.",
                    "Czy ten przycisk jest dostępny? <div onclick='...'>Click me</div>",
                    "Jaka jest różnica między aria-label a aria-labelledby?",
                ],
                inputs=msg,
                label="Przykładowe pytania"
            )
        
        # Right column: Markdown content from file
        with gr.Column(scale=1):
            gr.Markdown("### 📝 Notatki")
            
            def load_notes():
                """Load notes from notes.md file."""
                try:
                    with open("notes.md", "r", encoding="utf-8") as f:
                        return f.read()
                except FileNotFoundError:
                    return """
## Witaj w A11y Expert! 👋

Stwórz plik `notes.md` w katalogu projektu aby zobaczyć tutaj swoje notatki.

### Przydatne linki:
- [WCAG 2.2 Guidelines](https://www.w3.org/WAI/WCAG22/quickref/)
- [ARIA Authoring Practices](https://www.w3.org/WAI/ARIA/apg/)
- [MDN Accessibility](https://developer.mozilla.org/en-US/docs/Web/Accessibility)
                    """
                except Exception as e:
                    return f"⚠️ Błąd wczytywania notes.md: {e}"
            
            markdown_content = gr.Markdown(
                value=load_notes(),
                show_label=False,
                elem_id="notes_display"
            )
            
            refresh_btn = gr.Button("🔄 Odśwież notatki", variant="secondary")
            refresh_btn.click(
                fn=load_notes,
                outputs=markdown_content
            )
    
    # Chat logic
    def user_message(user_input, history):
        """Add user message to chat history."""
        return "", history + [{"role": "user", "content": user_input}]
    
    def bot_response(history):
        """Generate bot response."""
        user_input = history[-1]["content"]
        
        # Extract text from multimodal format if needed
        if isinstance(user_input, list):
            user_input = " ".join([item.get("text", "") for item in user_input if item.get("type") == "text"])
        
        # Add assistant message placeholder
        history.append({"role": "assistant", "content": ""})
        
        for response in respond(user_input, history[:-1]):
            history[-1]["content"] = response
            yield history
    
    # Wire up the chat
    submit.click(user_message, [msg, chatbot], [msg, chatbot], queue=False).then(
        bot_response, chatbot, chatbot
    )
    msg.submit(user_message, [msg, chatbot], [msg, chatbot], queue=False).then(
        bot_response, chatbot, chatbot
    )
    clear.click(lambda: None, None, chatbot, queue=False)


# --- App Launch ---
# Register cleanup handler
# atexit.register(cleanup_resources) # Disabled: Causes premature shutdown on Hugging Face Spaces

# Don't initialize agent on startup - it will be initialized on first user query
logger.info("🚀 Starting Gradio app with on-demand agent initialization...")
logger.info("ℹ️ Agent will initialize on first user query")

# For Hugging Face Spaces, we need to either:
# 1. Have a variable named 'demo' (which we have)
# 2. Or explicitly call demo.queue() to enable the app
# We'll use queue() to ensure proper startup
if __name__ == "__main__":
    demo.queue()
    demo.launch()
else:
    # On HF Spaces, just ensure demo is ready
    demo.queue()