import gradio as gr
import os
import time
import sys
import io
import tempfile
import subprocess
import requests
from urllib.parse import urlparse
from pydub import AudioSegment
import logging
import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
import yt_dlp

class LogCapture(io.StringIO):
    def __init__(self, callback):
        super().__init__()
        self.callback = callback

    def write(self, s):
        super().write(s)
        self.callback(s)

# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

# Clone and install faster-whisper from GitHub
try:
    subprocess.run(["git", "clone", "https://github.com/SYSTRAN/faster-whisper.git"], check=True)
    subprocess.run(["pip", "install", "-e", "./faster-whisper"], check=True)
except subprocess.CalledProcessError as e:
    logging.error(f"Error during faster-whisper installation: {e}")
    sys.exit(1)

sys.path.append("./faster-whisper")

from faster_whisper import WhisperModel
from faster_whisper.transcribe import BatchedInferencePipeline

# Check for CUDA availability
device = "cuda:0" if torch.cuda.is_available() else "cpu"
logging.info(f"Using device: {device}")

def download_audio(url, method_choice):
    """
    Downloads audio from a given URL using the specified method.
    
    Args:
        url (str): The URL of the audio.
        method_choice (str): The method to use for downloading audio.
    
    Returns:
        tuple: (path to the downloaded audio file, is_temp_file), or (error message, False).
    """
    parsed_url = urlparse(url)
    logging.info(f"Downloading audio from URL: {url} using method: {method_choice}")
    try:
        if 'youtube.com' in parsed_url.netloc or 'youtu.be' in parsed_url.netloc:
            audio_file = download_youtube_audio(url, method_choice)
        elif parsed_url.scheme == 'rtsp':
            audio_file = download_rtsp_audio(url)
        else:
            audio_file = download_direct_audio(url, method_choice)
        
        if not audio_file or not os.path.exists(audio_file):
            raise Exception(f"Failed to download audio from {url}")
        return audio_file, True
    except Exception as e:
        logging.error(f"Error downloading audio: {str(e)}")
        return f"Error: {str(e)}", False

def download_youtube_audio(url, method_choice):
    """
    Downloads audio from a YouTube URL using the specified method.
    
    Args:
        url (str): The YouTube URL.
        method_choice (str): The method to use for downloading.
    
    Returns:
        str: Path to the downloaded audio file, or None if failed.
    """
    methods = {
        'yt-dlp': yt_dlp_method,
        'pytube': pytube_method,
    }
    method = methods.get(method_choice, yt_dlp_method)
    try:
        logging.info(f"Attempting to download YouTube audio using {method_choice}")
        return method(url)
    except Exception as e:
        logging.error(f"Error downloading using {method_choice}: {str(e)}")
        return None

def yt_dlp_method(url):
    """
    Downloads YouTube audio using yt-dlp.
    
    Args:
        url (str): The YouTube URL.
    
    Returns:
        str: Path to the downloaded audio file.
    """
    logging.info("Using yt-dlp method")
    ydl_opts = {
        'format': 'bestaudio/best',
        'postprocessors': [{
            'key': 'FFmpegExtractAudio',
            'preferredcodec': 'mp3',
            'preferredquality': '192',
        }],
        'outtmpl': '%(id)s.%(ext)s',
    }
    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
        info = ydl.extract_info(url, download=True)
        output_file = f"{info['id']}.mp3"
        logging.info(f"Downloaded YouTube audio: {output_file}")
        return output_file

def pytube_method(url):
    """
    Downloads audio using pytube.
    
    Args:
        url (str): The YouTube URL.
    
    Returns:
        str: Path to the downloaded audio file.
    """
    logging.info("Using pytube method")
    from pytube import YouTube
    yt = YouTube(url)
    audio_stream = yt.streams.filter(only_audio=True).first()
    out_file = audio_stream.download()
    base, ext = os.path.splitext(out_file)
    new_file = base + '.mp3'
    os.rename(out_file, new_file)
    logging.info(f"Downloaded and converted audio to: {new_file}")
    return new_file

def download_rtsp_audio(url):
    """
    Downloads audio from an RTSP URL using FFmpeg.
    
    Args:
        url (str): The RTSP URL.
    
    Returns:
        str: Path to the downloaded audio file, or None if failed.
    """
    logging.info("Using FFmpeg to download RTSP stream")
    output_file = tempfile.mktemp(suffix='.mp3')
    command = ['ffmpeg', '-i', url, '-acodec', 'libmp3lame', '-ab', '192k', '-y', output_file]
    try:
        subprocess.run(command, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
        logging.info(f"Downloaded RTSP audio to: {output_file}")
        return output_file
    except subprocess.CalledProcessError as e:
        logging.error(f"FFmpeg error: {e.stderr.decode()}")
        return None
    except Exception as e:
        logging.error(f"Error downloading RTSP audio: {str(e)}")
        return None

def download_direct_audio(url, method_choice):
    """
    Downloads audio from a direct URL using the specified method.
    
    Args:
        url (str): The direct URL of the audio file.
        method_choice (str): The method to use for downloading.
    
    Returns:
        str: Path to the downloaded audio file, or None if failed.
    """
    logging.info(f"Downloading direct audio from: {url} using method: {method_choice}")
    methods = {
        'wget': wget_method,
        'requests': requests_method,
        'yt-dlp': yt_dlp_direct_method,
        'ffmpeg': ffmpeg_method,
        'aria2': aria2_method,
    }
    method = methods.get(method_choice, requests_method)
    try:
        return method(url)
    except Exception as e:
        logging.error(f"Error downloading direct audio: {str(e)}")
        return None

def requests_method(url):
    """
    Downloads audio using the requests library.
    
    Args:
        url (str): The URL of the audio file.
    
    Returns:
        str: Path to the downloaded audio file, or None if failed.
    """
    try:
        response = requests.get(url, stream=True)
        if response.status_code == 200:
            with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as temp_file:
                for chunk in response.iter_content(chunk_size=8192):
                    if chunk:
                        temp_file.write(chunk)
            logging.info(f"Downloaded direct audio to: {temp_file.name}")
            return temp_file.name
        else:
            logging.error(f"Failed to download audio from {url} with status code {response.status_code}")
            return None
    except Exception as e:
        logging.error(f"Error in requests_method: {str(e)}")
        return None

def wget_method(url):
    """
    Downloads audio using the wget command-line tool.
    
    Args:
        url (str): The URL of the audio file.
    
    Returns:
        str: Path to the downloaded audio file, or None if failed.
    """
    logging.info("Using wget method")
    output_file = tempfile.mktemp(suffix='.mp3')
    command = ['wget', '-O', output_file, url]
    try:
        subprocess.run(command, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
        logging.info(f"Downloaded audio to: {output_file}")
        return output_file
    except subprocess.CalledProcessError as e:
        logging.error(f"Wget error: {e.stderr.decode()}")
        return None
    except Exception as e:
        logging.error(f"Error in wget_method: {str(e)}")
        return None

def yt_dlp_direct_method(url):
    """
    Downloads audio using yt-dlp (supports various protocols and sites).
    
    Args:
        url (str): The URL of the audio or webpage containing audio.
    
    Returns:
        str: Path to the downloaded audio file, or None if failed.
    """
    logging.info("Using yt-dlp direct method")
    output_file = tempfile.mktemp(suffix='.mp3')
    ydl_opts = {
        'format': 'bestaudio/best',
        'outtmpl': output_file,
        'quiet': True,
        'no_warnings': True,
        'postprocessors': [{
            'key': 'FFmpegExtractAudio',
            'preferredcodec': 'mp3',
            'preferredquality': '192',
        }],
    }
    try:
        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
            ydl.download([url])
        logging.info(f"Downloaded audio to: {output_file}")
        return output_file
    except Exception as e:
        logging.error(f"Error in yt_dlp_direct_method: {str(e)}")
        return None

def ffmpeg_method(url):
    """
    Downloads audio using FFmpeg.
    
    Args:
        url (str): The URL of the audio file.
    
    Returns:
        str: Path to the downloaded audio file, or None if failed.
    """
    logging.info("Using ffmpeg method")
    output_file = tempfile.mktemp(suffix='.mp3')
    command = ['ffmpeg', '-i', url, '-vn', '-acodec', 'libmp3lame', '-q:a', '2', output_file]
    try:
        subprocess.run(command, check=True, capture_output=True, text=True)
        logging.info(f"Downloaded and converted audio to: {output_file}")
        return output_file
    except subprocess.CalledProcessError as e:
        logging.error(f"FFmpeg error: {e.stderr}")
        return None
    except Exception as e:
        logging.error(f"Error in ffmpeg_method: {str(e)}")
        return None

def aria2_method(url):
    """
    Downloads audio using aria2.
    
    Args:
        url (str): The URL of the audio file.
    
    Returns:
        str: Path to the downloaded audio file, or None if failed.
    """
    logging.info("Using aria2 method")
    output_file = tempfile.mktemp(suffix='.mp3')
    command = ['aria2c', '--split=4', '--max-connection-per-server=4', '--out', output_file, url]
    try:
        subprocess.run(command, check=True, capture_output=True, text=True)
        logging.info(f"Downloaded audio to: {output_file}")
        return output_file
    except subprocess.CalledProcessError as e:
        logging.error(f"Aria2 error: {e.stderr}")
        return None
    except Exception as e:
        logging.error(f"Error in aria2_method: {str(e)}")
        return None

def trim_audio(audio_path, start_time, end_time):
    """
    Trims an audio file to the specified start and end times.
    
    Args:
        audio_path (str): Path to the audio file.
        start_time (float): Start time in seconds.
        end_time (float): End time in seconds.
    
    Returns:
        str: Path to the trimmed audio file.
    
    Raises:
        gr.Error: If invalid start or end times are provided.
    """
    try:
        logging.info(f"Trimming audio from {start_time} to {end_time}")
        audio = AudioSegment.from_file(audio_path)
        audio_duration = len(audio) / 1000  # Duration in seconds

        # Default start and end times if None
        start_time = max(0, start_time) if start_time is not None else 0
        end_time = min(audio_duration, end_time) if end_time is not None else audio_duration

        # Validate times
        if start_time >= end_time:
            raise gr.Error("End time must be greater than start time.")

        trimmed_audio = audio[int(start_time * 1000):int(end_time * 1000)]
        with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as temp_audio_file:
            trimmed_audio.export(temp_audio_file.name, format="wav")
            logging.info(f"Trimmed audio saved to: {temp_audio_file.name}")
        return temp_audio_file.name
    except Exception as e:
        logging.error(f"Error trimming audio: {str(e)}")
        raise gr.Error(f"Error trimming audio: {str(e)}")

def save_transcription(transcription):
    """
    Saves the transcription text to a temporary file.
    
    Args:
        transcription (str): The transcription text.
    
    Returns:
        str: The path to the transcription file.
    """
    with tempfile.NamedTemporaryFile(delete=False, suffix='.txt', mode='w', encoding='utf-8') as temp_file:
        temp_file.write(transcription)
        logging.info(f"Transcription saved to: {temp_file.name}")
        return temp_file.name

def get_model_options(pipeline_type):
    """
    Returns a list of model IDs based on the selected pipeline type.
    
    Args:
        pipeline_type (str): The type of pipeline.
    
    Returns:
        list: A list of model IDs.
    """
    if pipeline_type == "transformers":
        return ["openai/whisper-large-v3", "openai/whisper-large-v2", "openai/whisper-medium", "openai/whisper-small"]
    else:
        return []

# Dictionary to store loaded models
loaded_models = {}

def transcribe_audio(input_source, pipeline_type, model_id, dtype, batch_size, download_method, start_time=None, end_time=None, verbose=False):
    """
    Transcribes audio from a given source using the specified pipeline and model.

    Args:
        input_source (str or file): URL of audio, path to local file, or uploaded file object.
        pipeline_type (str): Type of pipeline to use ('faster-batched', 'faster-sequenced', or 'transformers').
        model_id (str): The ID of the model to use.
        dtype (str): Data type for model computations ('int8', 'float16', or 'float32').
        batch_size (int): Batch size for transcription.
        download_method (str): Method to use for downloading audio.
        start_time (float, optional): Start time in seconds for trimming audio.
        end_time (float, optional): End time in seconds for trimming audio.
        verbose (bool, optional): Whether to output verbose logging.

    Yields:
        Tuple[str, str, str or None]: Metrics and messages, transcription text, path to transcription file.
    """
    try:
        if verbose:
            logging.getLogger().setLevel(logging.INFO)
        else:
            logging.getLogger().setLevel(logging.WARNING)

        logging.info(f"Transcription parameters: pipeline_type={pipeline_type}, model_id={model_id}, dtype={dtype}, batch_size={batch_size}, download_method={download_method}")
        verbose_messages = f"Starting transcription with parameters:\nPipeline Type: {pipeline_type}\nModel ID: {model_id}\nData Type: {dtype}\nBatch Size: {batch_size}\nDownload Method: {download_method}\n"

        if verbose:
            yield verbose_messages, "", None

        # Determine if input_source is a URL or file
        audio_path = None
        is_temp_file = False

        if isinstance(input_source, str) and (input_source.startswith('http://') or input_source.startswith('https://')):
            # Input source is a URL
            audio_path, is_temp_file = download_audio(input_source, download_method)
            if not audio_path or audio_path.startswith("Error"):
                yield f"Error downloading audio: {audio_path}", "", None
                return
        elif isinstance(input_source, str) and os.path.exists(input_source):
            # Input source is a local file path
            audio_path = input_source
            is_temp_file = False
        elif hasattr(input_source, 'name'):
            # Input source is an uploaded file object
            audio_path = input_source.name
            is_temp_file = False
        else:
            yield "No valid audio source provided.", "", None
            return

        # Convert start_time and end_time to float or None
        start_time = float(start_time) if start_time else None
        end_time = float(end_time) if end_time else None

        if start_time is not None or end_time is not None:
            audio_path = trim_audio(audio_path, start_time, end_time)
            is_temp_file = True  # The trimmed audio is a temporary file
            verbose_messages += f"Audio trimmed from {start_time} to {end_time}\n"
            if verbose:
                yield verbose_messages, "", None

        # Model caching
        model_key = (pipeline_type, model_id, dtype)
        if model_key in loaded_models:
            model_or_pipeline = loaded_models[model_key]
            logging.info("Loaded model from cache")
        else:
            if pipeline_type == "faster-batched":
                model = WhisperModel(model_id, device=device, compute_type=dtype)
                model_or_pipeline = BatchedInferencePipeline(model=model)
            elif pipeline_type == "faster-sequenced":
                model_or_pipeline = WhisperModel(model_id, device=device, compute_type=dtype)
            elif pipeline_type == "transformers":
                # Adjust torch_dtype based on dtype and device
                if dtype == "float16" and device == "cpu":
                    torch_dtype = torch.float32
                elif dtype == "float16":
                    torch_dtype = torch.float16
                else:
                    torch_dtype = torch.float32

                model = AutoModelForSpeechSeq2Seq.from_pretrained(
                    model_id, torch_dtype=torch_dtype
                )
                processor = AutoProcessor.from_pretrained(model_id)
                model_or_pipeline = pipeline(
                    "automatic-speech-recognition",
                    model=model,
                    tokenizer=processor.tokenizer,
                    feature_extractor=processor.feature_extractor,
                    chunk_length_s=30,
                    batch_size=batch_size,
                    return_timestamps=True,
                    device=device,
                )
            else:
                raise ValueError("Invalid pipeline type")
            loaded_models[model_key] = model_or_pipeline  # Cache the model or pipeline

        start_time_perf = time.time()
        if pipeline_type == "faster-batched":
            segments, info = model_or_pipeline.transcribe(audio_path, batch_size=batch_size)
        elif pipeline_type == "faster-sequenced":
            segments, info = model_or_pipeline.transcribe(audio_path)
        else:
            result = model_or_pipeline(audio_path)
            segments = result["chunks"]
        end_time_perf = time.time()

        transcription_time = end_time_perf - start_time_perf
        audio_file_size = os.path.getsize(audio_path) / (1024 * 1024)

        metrics_output = (
            f"Transcription time: {transcription_time:.2f} seconds\n"
            f"Audio file size: {audio_file_size:.2f} MB\n"
        )

        if verbose:
            yield verbose_messages + metrics_output, "", None

        transcription = ""

        for segment in segments:
            if pipeline_type in ["faster-batched", "faster-sequenced"]:
                transcription_segment = f"[{segment.start:.2f}s -> {segment.end:.2f}s] {segment.text}\n"
            else:
                transcription_segment = f"[{segment['timestamp'][0]:.2f}s -> {segment['timestamp'][1]:.2f}s] {segment['text']}\n"
            transcription += transcription_segment
            if verbose:
                yield verbose_messages + metrics_output, transcription, None

        transcription_file = save_transcription(transcription)
        yield verbose_messages + metrics_output, transcription, transcription_file

    except Exception as e:
        logging.error(f"An error occurred during transcription: {str(e)}")
        yield f"An error occurred: {str(e)}", "", None

    finally:
        # Clean up temporary audio files
        if audio_path and is_temp_file and os.path.exists(audio_path):
            os.remove(audio_path)

with gr.Blocks() as iface:
    gr.Markdown("# Multi-Pipeline Transcription")
    gr.Markdown("Transcribe audio using multiple pipelines and models.")
    
    with gr.Row():
        #input_source = gr.File(label="Audio Source (Upload a file or enter a URL/YouTube URL)")
        input_source = gr.Textbox(label="Audio Source (Upload a file or enter a URL/YouTube URL)")
        pipeline_type = gr.Dropdown(
            choices=["faster-batched", "faster-sequenced", "transformers"],
            label="Pipeline Type",
            value="faster-batched"
        )
        model_id = gr.Dropdown(
            label="Model",
            choices=get_model_options("faster-batched"),
            value=get_model_options("faster-batched")[0]
        )
    
    with gr.Row():
        dtype = gr.Dropdown(choices=["int8", "float16", "float32"], label="Data Type", value="int8")
        batch_size = gr.Slider(minimum=1, maximum=32, step=1, value=16, label="Batch Size")
        download_method = gr.Dropdown(
            choices=["yt-dlp", "pytube", "youtube-dl", "yt-dlp-alt", "ffmpeg", "aria2", "wget"],
            label="Download Method",
            value="yt-dlp"
        )
    
    with gr.Row():
        start_time = gr.Number(label="Start Time (seconds)", value=None, minimum=0)
        end_time = gr.Number(label="End Time (seconds)", value=None, minimum=0)
        verbose = gr.Checkbox(label="Verbose Output", value=True)  # Set to True by default
    
    transcribe_button = gr.Button("Transcribe")
    
    with gr.Row():
        metrics_output = gr.Textbox(label="Transcription Metrics and Verbose Messages", lines=10)
        transcription_output = gr.Textbox(label="Transcription", lines=10)
        transcription_file = gr.File(label="Download Transcription")
    
    def update_model_dropdown(pipeline_type):
        """
        Updates the model dropdown choices based on the selected pipeline type.
    
        Args:
            pipeline_type (str): The selected pipeline type.
    
        Returns:
            gr.update: Updated model dropdown component.
        """
        try:
            model_choices = get_model_options(pipeline_type)
            logging.info(f"Model choices for {pipeline_type}: {model_choices}")
            if model_choices:
                return gr.update(choices=model_choices, value=model_choices[0], visible=True)
            else:
                return gr.update(choices=["No models available"], value=None, visible=False)
        except Exception as e:
            logging.error(f"Error in update_model_dropdown: {str(e)}")
            return gr.update(choices=["Error"], value="Error", visible=True)

    # event handler for pipeline_type change
    pipeline_type.change(update_model_dropdown, inputs=[pipeline_type], outputs=[model_id])
    
    def transcribe_with_progress(*args):
        for result in transcribe_audio(*args):
            yield result
    
    transcribe_button.click(
        transcribe_with_progress,
        inputs=[input_source, pipeline_type, model_id, dtype, batch_size, download_method, start_time, end_time, verbose],
        outputs=[metrics_output, transcription_output, transcription_file]
    )
    
    gr.Examples(
        examples=[
            ["https://www.youtube.com/watch?v=daQ_hqA6HDo", "faster-batched", "cstr/whisper-large-v3-turbo-int8_float32", "int8", 16, "yt-dlp", None, None, True],
            ["https://mcdn.podbean.com/mf/web/dir5wty678b6g4vg/HoP_453_-_The_Price_is_Right_-_Law_and_Economics_in_the_Second_Scholastic5yxzh.mp3", "faster-sequenced", "deepdml/faster-whisper-large-v3-turbo-ct2", "float16", 1, "ffmpeg", 0, 300, True],
            ["path/to/local/audio.mp3", "transformers", "openai/whisper-large-v3", "float16", 16, "yt-dlp", 60, 180, True]
        ],
        inputs=[input_source, pipeline_type, model_id, dtype, batch_size, download_method, start_time, end_time, verbose],
    )

iface.launch()