import torch
import gradio as gr
import yt_dlp as youtube_dl
from transformers import WhisperProcessor, WhisperForConditionalGeneration
import tempfile
import os
import time

# Constants
MODEL_NAME = "openai/whisper-large-v3"
BATCH_SIZE = 8
FILE_LIMIT_MB = 25  # File size limit in MB
YT_LENGTH_LIMIT_S = 3600  # 1 hour YouTube file limit

# Device configuration (CUDA if available)
device = 0 if torch.cuda.is_available() else "cpu"

# Load Whisper model and processor
processor = WhisperProcessor.from_pretrained(MODEL_NAME)
model = WhisperForConditionalGeneration.from_pretrained(MODEL_NAME).to(device)

def transcribe_audio(inputs):
    """Transcribe audio using Whisper model."""
    if inputs is None:
        raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
    
    # Check file size (max 25MB)
    if os.path.getsize(inputs) > FILE_LIMIT_MB * 1024 * 1024:
        raise gr.Error(f"File size exceeds {FILE_LIMIT_MB}MB limit.")
    
    # Preprocess audio input
    audio_input = processor(inputs, return_tensors="pt", sampling_rate=16000).to(device)
    
    # Generate transcription
    predicted_ids = model.generate(audio_input.input_values, max_length=448)
    
    # Decode the transcription output
    transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
    return transcription

def _return_yt_html_embed(yt_url):
    """Return YouTube embed HTML for display."""
    video_id = yt_url.split("?v=")[-1]
    html_embed = f'<center><iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"></iframe></center>'
    return html_embed

def download_yt_audio(yt_url, filename):
    """Download audio from a YouTube URL."""
    info_loader = youtube_dl.YoutubeDL()
    
    try:
        info = info_loader.extract_info(yt_url, download=False)
    except youtube_dl.utils.DownloadError as err:
        raise gr.Error(f"Download error: {str(err)}")
    
    # Check video length
    file_length_s = int(info.get("duration", 0))
    
    if file_length_s > YT_LENGTH_LIMIT_S:
        yt_length_limit_hms = time.strftime("%HH:%MM:%SS", time.gmtime(YT_LENGTH_LIMIT_S))
        file_length_hms = time.strftime("%HH:%MM:%SS", time.gmtime(file_length_s))
        raise gr.Error(f"Maximum YouTube video length is {yt_length_limit_hms}, but video is {file_length_hms}.")
    
    # Download the video
    ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"}
    with youtube_dl.YoutubeDL(ydl_opts) as ydl:
        try:
            ydl.download([yt_url])
        except youtube_dl.utils.ExtractorError as err:
            raise gr.Error(f"Error while downloading video: {str(err)}")

def yt_transcribe(yt_url):
    """Transcribe YouTube video using Whisper model."""
    html_embed = _return_yt_html_embed(yt_url)

    with tempfile.TemporaryDirectory() as tmpdirname:
        filepath = os.path.join(tmpdirname, "video.mp4")
        download_yt_audio(yt_url, filepath)

        with open(filepath, "rb") as file:
            audio_input = file.read()

    # Process and transcribe
    transcription = transcribe_audio(audio_input)
    return html_embed, transcription

# Create Gradio interface
demo = gr.Blocks()

# Microphone transcription interface
mf_transcribe = gr.Interface(
    fn=transcribe_audio,
    inputs=[
        gr.inputs.Audio(source="microphone", type="filepath", optional=True),
    ],
    outputs="text",
    layout="horizontal",
    theme="huggingface",
    title="Whisper Transcription (Microphone)",
    description="Transcribe audio from your microphone. File size limit is 25MB."
)

# File upload transcription interface
file_transcribe = gr.Interface(
    fn=transcribe_audio,
    inputs=[
        gr.inputs.Audio(source="upload", type="filepath", optional=True, label="Audio file"),
    ],
    outputs="text",
    layout="horizontal",
    theme="huggingface",
    title="Whisper Transcription (File)",
    description="Upload an audio file to transcribe. File size limit is 25MB."
)

# YouTube video transcription interface
yt_transcribe = gr.Interface(
    fn=yt_transcribe,
    inputs=[
        gr.inputs.Textbox(lines=1, placeholder="Paste YouTube URL", label="YouTube URL"),
    ],
    outputs=["html", "text"],
    layout="horizontal",
    theme="huggingface",
    title="Free Transcript Maker",
    description="Upload an audio file (WAV, MP3, etc.) up to 25MB to get its transcription. The transcript will be displayed and available for download. Please use responsibly."
)

with demo:
    gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"])

demo.launch(enable_queue=True)