"""
Main module that handles processing of YouTube transcripts and connecting to the AI service.
Each user session has its own output stream and thread to handle the asynchronous AI response.
"""

import re
import threading
import asyncio
from asyncio import sleep
from datetime import datetime
import pytz
import os
import logging
import uuid

# Youtube Transcript imports
import youtube_transcript_api._errors
from youtube_transcript_api import YouTubeTranscriptApi
from youtube_transcript_api.formatters import TextFormatter

# OpenAI API imports
from openai import AssistantEventHandler
from openai import OpenAI

from dotenv import load_dotenv
load_dotenv()

# Global dict for per-user session streams.
user_streams = {}
# Lock to ensure thread-safe operations on shared memory.
stream_lock = threading.Lock()

# For running async code in non-async functions.
awaiter = asyncio.run

# Configure logging
try:
    logging.basicConfig(
        filename='./logs/main.log',
        level=logging.INFO,
        format='%(asctime)s %(levelname)s: %(message)s',
        datefmt='%Y-%m-%d %H:%M:%S'
    )
except FileNotFoundError as e:
    with open("./logs/main.log", "x"):
        pass
    logging.basicConfig(
        filename='./logs/main.log',
        level=logging.INFO,
        format='%(asctime)s %(levelname)s: %(message)s',
        datefmt='%Y-%m-%d %H:%M:%S'
    )
    logging.info(f"No main.log file was found ({e}), so one was created.")

class StreamOutput:
    """
    Class to encapsulate a session's streaming output.
    
    Attributes:
        delta (str): Last delta update.
        response (str): Cumulative response from the AI.
        done (bool): Flag indicating if streaming is complete.
        buffer (list): List of output delta strings pending streaming.
    """
    def __init__(self):
        self.delta: str = ""
        self.response: str = ""
        self.done: bool = False
        self.buffer: list = []
    
    def reset(self):
        """
        Reset the stream output to its initial state.
        """
        self.delta = ""
        self.response = ""
        self.done = False
        self.buffer = []
    
    def send_delta(self, delta):
        """
        Process a new delta string. This method is a synchronous wrapper that calls the async
        method process_delta.
        
        Args:
            delta (str): The delta string to process.
        """
        awaiter(self.process_delta(delta))
    
    async def process_delta(self, delta):
        """
        Process a new delta chunk asynchronously to update buffering.
        
        Args:
            delta (str): The delta portion of the response.
        """
        self.delta = delta
        self.response += delta

        def get_index(lst):
            return 0 if not lst else len(lst) - 1

        if self.buffer:
            try:
                if self.delta != self.buffer[get_index(self.buffer)]:
                    self.buffer.append(delta)
            except IndexError as index_error:
                logging.error(f"Caught IndexError: {str(index_error)}")
                self.buffer.append(delta)
        else:
            self.buffer.append(delta)
        return

# OpenAI Client configuration
client = OpenAI(
    organization='org-7ANUFsqOVIXLLNju8Rvmxu3h',
    project="proj_NGz8Kux8CSka7DRJucAlDCz6",
    api_key=os.getenv("OPENAI_API_KEY")
)

asst_screw_bardo_id = "asst_JGFaX6uOIotqy5mIJnu3Yyp7"  # Assistant ID for processing

class EventHandler(AssistantEventHandler):
    """
    Event handler for processing OpenAI assistant events.
    
    Attributes:
        output_stream (StreamOutput): The output stream to write updates to.
    """
    def __init__(self, output_stream: StreamOutput):
        """
        Initialize the event handler with a specific output stream.
        
        Args:
            output_stream (StreamOutput): The session specific stream output instance.
        """
        super().__init__()
        self.output_stream = output_stream

    def on_text_created(self, text) -> None:
        """
        Event triggered when text is first created.
        
        Args:
            text (str): The initial response text.
        """
        self.output_stream.send_delta("Response Received:\n\nScrew-Bardo:\n\n")
        logging.info("Text created event handled.")

    def on_text_delta(self, delta, snapshot):
        """
        Event triggered when a new text delta is available.
        
        Args:
            delta (Any): Object that contains the new delta information.
            snapshot (Any): A snapshot of the current output (if applicable).
        """
        self.output_stream.send_delta(delta.value)
        logging.debug(f"Text delta received: {delta.value}")

    def on_tool_call_created(self, tool_call):
        """
        Handle the case when the assistant attempts to call a tool.
        Raises an exception as this behavior is unexpected.
        
        Args:
            tool_call (Any): The tool call info.
            
        Raises:
            Exception: Always, since tool calls are not allowed.
        """
        error_msg = "Assistant shouldn't be calling tools."
        logging.error(error_msg)
        raise Exception(error_msg)

def create_and_stream(transcript, session_id):
    """
    Create a new thread that runs the OpenAI stream for a given session and transcript.
    
    Args:
        transcript (str): The transcript from the YouTube video.
        session_id (str): The unique session identifier.
    """
    logging.info(f"Starting OpenAI stream thread for session {session_id}.")
    event_handler = EventHandler(user_streams[session_id]['output_stream'])
    try:
        with client.beta.threads.create_and_run_stream(
            assistant_id=asst_screw_bardo_id,
            thread={
                "messages": [{"role": "user", "content": transcript}]
            },
            event_handler=event_handler
        ) as stream:
            stream.until_done()
            with stream_lock:
                user_streams[session_id]['output_stream'].done = True
            logging.info(f"OpenAI stream completed for session {session_id}.")
    except Exception as e:
        logging.exception(f"Exception occurred during create_and_stream for session {session_id}.")

def yoink(session_id):
    """
    Generator that yields streaming output for a session.
    
    This function starts the AI response thread, then continuously yields data from the session's output buffer
    until the response is marked as done.
    
    Args:
        session_id (str): The unique session identifier.
    
    Yields:
        bytes: Chunks of the AI generated response.
    """
    logging.info(f"Starting stream for session {session_id}...")
    with stream_lock:
        user_data = user_streams.get(session_id)
        if not user_data:
            logging.critical(f"User data not found for session id {session_id}?")
            return
        output_stream: StreamOutput = user_data.get('output_stream')
        thread: threading.Thread = user_data.get('thread')
        thread.start()
    while True:
        if not output_stream or not thread:
            logging.error(f"No output stream/thread for session {session_id}.")
            break  
        # Stop streaming when done and there is no pending buffered output.
        if output_stream.done and not output_stream.buffer:
            break
        try:
            if output_stream.buffer:
                delta = output_stream.buffer.pop(0)
                yield bytes(delta, encoding="utf-8")
            else:
                # A short sleep before looping again
                asyncio.run(sleep(0.018))
        except Exception as e:
            logging.exception(f"Exception occurred during streaming for session {session_id}: {e}")
            break
    logging.info(f"Stream completed successfully for session {session_id}.")
    logging.info(f"Completed Assistant Response for session {session_id}:\n{output_stream.response}")
    with stream_lock:
        thread.join()
        # Clean up the session data once done.
        del user_streams[session_id]
    logging.info(f"Stream thread joined and resources cleaned up for session {session_id}.")

def process(url, session_id):
    """
    Process a YouTube URL: parse the video id, retrieve its transcript, and prepare the session for AI processing.
    
    Args:
        url (str): The YouTube URL provided by the user.
        session_id (str): The unique session identifier.
    
    Returns:
        tuple: (success (bool), message (str or None), status_code (int or None))
    """
    current_time = datetime.now(pytz.timezone('America/New_York')).strftime('%Y-%m-%d %H:%M:%S')
    logging.info(f"New Entry at {current_time} for session {session_id}")
    logging.info(f"URL: {url}")
    video_id = get_video_id(url)
    if not video_id:
        logging.warning(f"Could not parse video id from URL: {url}")
        return (False, "Couldn't parse video ID from URL. (Are you sure you entered a valid YouTube.com or YouTu.be URL?)", 400)
    logging.info(f"Parsed Video ID: {video_id}")
    transcript = get_auto_transcript(video_id)
    if not transcript:
        logging.error(f"Error: could not retrieve transcript for session {session_id}. Assistant won't be called.")
        return (False, "Successfully parsed video ID from URL, however the transcript was disabled by the video owner or invalid.", 200)
    
    # Initialize session data for streaming.
    user_streams[session_id] = {
        'output_stream': None,
        'thread': None
    }
    with stream_lock:
        user_streams[session_id]['output_stream'] = StreamOutput()
        thread = threading.Thread(
            name=f"create_stream_{session_id}",
            target=create_and_stream,
            args=(transcript, session_id)
        )
        user_streams[session_id]['thread'] = thread
    logging.info(f"Stream preparation complete for session {session_id}, sending reply.")
    return (True, None, None)

def get_video_id(url):
    """
    Extract the YouTube video ID from a URL.
    
    Args:
        url (str): The YouTube URL.
    
    Returns:
        str or None: The video ID if found, otherwise None.
    """
    youtu_be = r'(?<=youtu.be/)([A-Za-z0-9_-]{11})'
    youtube_com = r'(?<=youtube\.com\/watch\?v=)([A-Za-z0-9_-]{11})'
    id_match = re.search(youtu_be, url)
    if not id_match:
        id_match = re.search(youtube_com, url)
    if not id_match:
        logging.warning(f"Failed to parse video ID from URL: {url}")
        return None
    return id_match.group(1)

def get_auto_transcript(video_id):
    """
    Retrieve and format the transcript from a YouTube video.
    
    Args:
        video_id (str): The YouTube video identifier.
    
    Returns:
        str or None: The formatted transcript if successful; otherwise None.
    """
    trans_api_errors = youtube_transcript_api._errors
    try:
        transcript = YouTubeTranscriptApi.get_transcript(video_id, languages=['en'], proxies=None, cookies=None, preserve_formatting=False)
    except trans_api_errors.TranscriptsDisabled as e:
        logging.exception(f"Exception while fetching transcript: {e}")
        return None
    formatter = TextFormatter()
    txt_transcript = formatter.format_transcript(transcript)
    logging.info("Transcript successfully retrieved and formatted.")
    return txt_transcript

# Initialize a global output_stream just for main module logging (not used for per-session streaming).
output_stream = StreamOutput()
logging.info(f"Main initialized at {datetime.now(pytz.timezone('America/New_York')).strftime('%Y-%m-%d %H:%M:%S')}. Application starting.")