app.py

import gradio as gr
import pandas as pd
import asyncio
import logging
import sys
import os
import io
from contextlib import asynccontextmanager

# Change from absolute imports to relative imports
from database import initialize_database, add_participant, get_participants_dataframe
from matching_agent import create_matching_agent, run_matching
from tinyagent.hooks.logging_manager import LoggingManager

from tinyagent.hooks.logging_manager import LoggingManager
import logging


# --- Logging Setup ---
log_manager = LoggingManager(default_level=logging.INFO)
log_manager.set_levels({
    'tinyagent.hooks.gradio_callback': logging.DEBUG,
    'tinyagent.tiny_agent': logging.DEBUG,
    'tinyagent.mcp_client': logging.DEBUG,
    'tinyagent.code_agent': logging.DEBUG,
})

console_handler = logging.StreamHandler(sys.stdout)
log_manager.configure_handler(
    console_handler,
    format_string='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    level=logging.DEBUG
)

# Gradio log handler
log_stream = io.StringIO()
gradio_handler = logging.StreamHandler(log_stream)
log_manager.configure_handler(
    gradio_handler,
    format_string='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    level=logging.DEBUG  # Capture detailed logs for the UI
)

logger = log_manager.get_logger('app')

# --- Initial Setup ---
logger.info("Initializing database...")
initialize_database()

# Check for API key
if not os.environ.get("OPENAI_API_KEY"):
    raise ValueError("The OPENAI_API_KEY environment variable is not set. Please set it before running the app.")

# --- Session Management ---
@asynccontextmanager
async def agent_session():
    """Create and manage a session-specific agent."""
    session_agent = create_matching_agent(log_manager=log_manager)
    try:
        yield session_agent
    finally:
        await session_agent.close()
        logger.info("Session agent resources cleaned up")

# --- Gradio UI Functions ---

def register_participant(name, email, linkedin, background, goals):
    """Callback function to register a new participant."""
    if not all([name, email]):
        return "Please provide at least a name and email.", get_participants_dataframe()
    
    participant_data = {
        "name": name,
        "email": email,
        "linkedin_profile": linkedin,
        "background": background,
        "goals": goals
    }
    try:
        add_participant(participant_data)
        feedback = f"✅ Success! Participant '{name}' registered."
        logger.info(f"Registered new participant: {email}")
    except Exception as e:
        feedback = f"❌ Error! Could not register participant. Reason: {e}"
        logger.error(f"Failed to register participant {email}: {e}")
        
    return feedback, get_participants_dataframe()

def refresh_participants_list():
    """Callback to reload the participant data from the database."""
    return get_participants_dataframe()

async def run_matching_process(organizer_criteria, progress=gr.Progress(track_tqdm=True)):
    """Async callback to run the team matching process, with live log streaming."""
    # 1. Clear previous logs and show panel
    
    log_stream.seek(0)
    log_stream.truncate(0)
    progress(0, desc="Initializing...")
    yield [
        gr.update(visible=True, open=True),  # log_panel
        "Starting matching process...\n",     # log_output
        "🚀 Setting up the matching process..."  # matching_results_out
    ]

    # 2. Get participants and perform initial checks
    logger.info("Fetching participants...")
    progress(0, desc="Fetching participants...")
    participants_df = get_participants_dataframe()
    if len(participants_df) < 2:
        warning_msg = "Matching process aborted: not enough participants."
        logger.warning(warning_msg)
        yield [
            gr.update(),  # log_panel
            log_stream.getvalue(),  # log_output
            "Cannot run matching with fewer than 2 participants."  # matching_results_out
        ]
        return

    logger.info(f"Running matching for {len(participants_df)} participants.")
    progress(0.2, desc="🧠 Agent is thinking...")
    
    # 3. Create a session-specific agent and run matching
    async with agent_session() as session_agent:
        logger.info("Created session-specific agent")
        
        # Create a background task for the core matching logic
        match_task = asyncio.create_task(
            run_matching(session_agent, participants_df, organizer_criteria)
        )

        # 4. Stream logs while the agent works
        while not match_task.done():
            await asyncio.sleep(0.5)  # Poll for new logs every 0.5s
            yield [
                gr.update(),  # log_panel
                log_stream.getvalue(),  # log_output
                gr.update()  # matching_results_out - No change to final output yet
            ]

        # 5. Process the final result from the agent
        try:
            final_report = await match_task
            logger.info("Matching process completed successfully.")
            progress(1.0, desc="✅ Done!")
            yield [
                gr.update(),  # log_panel
                log_stream.getvalue(),  # log_output
                final_report  # matching_results_out
            ]
        except Exception as e:
            logger.error(f"An error occurred during the matching process: {e}", exc_info=True)
            yield [
                gr.update(),  # log_panel
                log_stream.getvalue(),  # log_output
                f"An error occurred: {e}"  # matching_results_out
            ]

# --- Gradio App Definition ---

with gr.Blocks(theme=gr.themes.Default(
    font=[gr.themes.GoogleFont("Inter"), "Arial", "sans-serif"]
), title="HackBuddyAI") as app:
    gr.Markdown("# 🤖 HackBuddyAI")
    
    with gr.Tabs():
        with gr.TabItem("👤 Participant Registration"):
            gr.Markdown("## Welcome, Participant!")
            gr.Markdown("Fill out the form below to register for the hackathon.")
            with gr.Row():
                with gr.Column():
                    name_in = gr.Textbox(label="Full Name")
                    email_in = gr.Textbox(label="Email Address")
                    linkedin_in = gr.Textbox(label="LinkedIn Profile URL", placeholder="Optional")
                with gr.Column():
                    background_in = gr.Textbox(label="Your Background & Skills", lines=5, placeholder="e.g., Python developer with 3 years of experience, specializing in Django and REST APIs...")
                    goals_in = gr.Textbox(label="Your Goals for this Hackathon", lines=5, placeholder="e.g., I want to learn about machine learning and work on a cool data visualization project...")
            
            submit_button = gr.Button("Register", variant="primary")
            registration_feedback = gr.Markdown()

        with gr.TabItem("👑 Organizer Dashboard"):
            gr.Markdown("## Welcome, Organizer!")
            gr.Markdown("Here you can view registered participants and run the AI-powered team matching process.")
            
            with gr.Accordion("View Registered Participants", open=False):
                refresh_button = gr.Button("🔄 Refresh List")
                participants_df_out = gr.DataFrame(value=get_participants_dataframe, interactive=False)

            gr.Markdown("### Run Matching")
            organizer_criteria_in = gr.Textbox(
                label="Matching Criteria", 
                lines=4,
                value="Create teams of 3. Try to balance skills in each team (e.g., frontend, backend, data).",
                placeholder="Describe your ideal team composition..."
            )
            run_button = gr.Button("🚀 Run AI Matching", variant="primary")
            
            gr.Markdown("### 🤝 Matched Teams")
            matching_results_out = gr.Markdown("Matching has not been run yet.")

            with gr.Accordion("Agent Logs", open=False, visible=False) as log_panel:
                log_output = gr.Code(
                    label="Live Logs",
                    lines=15,
                    interactive=False,
                )

    # Footer
    gr.Markdown(
        "<div style='text-align: center; margin-top: 20px;'>"
        "Built with ❤️ by <a href='https://github.com/askbudi/tinyagent' target='_blank'>TinyAgent</a>"
        "<br>Start building your own AI agents with TinyAgent"
        "</div>"
    )

    # --- Event Handlers ---
    submit_button.click(
        fn=register_participant,
        inputs=[name_in, email_in, linkedin_in, background_in, goals_in],
        outputs=[registration_feedback, participants_df_out]
    )
    
    refresh_button.click(
        fn=refresh_participants_list,
        inputs=[],
        outputs=[participants_df_out]
    )

    run_button.click(
        fn=run_matching_process,
        inputs=[organizer_criteria_in],
        outputs=[log_panel, log_output, matching_results_out]
    )

# --- Launching the App ---
if __name__ == "__main__":
    try:
        logger.info("Launching Gradio app...")
        # queue() is important for handling multiple users and async calls
        app.queue().launch(share=False)
    except KeyboardInterrupt:
        logger.info("Gradio app shutting down.")
    finally:
        logger.info("Shutdown complete.")