import os
import torch
import shutil
import librosa
import binascii
import warnings
import midi2audio
import pytube as pt    # to download the youtube videos as audios
import gradio as gr
from transformers import Pop2PianoForConditionalGeneration, Pop2PianoProcessor


yt_video_dir = "./yt_dir"
outputs_dir = "./midi_wav_outputs"
os.makedirs(outputs_dir, exist_ok=True)
os.makedirs(yt_video_dir, exist_ok=True)

device = "cuda" if torch.cuda.is_available() else "cpu"
model = Pop2PianoForConditionalGeneration.from_pretrained("sweetcocoa/pop2piano").to(device)
processor = Pop2PianoProcessor.from_pretrained("sweetcocoa/pop2piano")
composers = model.generation_config.composer_to_feature_token.keys()

def get_audio_from_yt_video(yt_link):
    try:
        yt = pt.YouTube(yt_link)
        t = yt.streams.filter(only_audio=True)
        filename = os.path.join(yt_video_dir, binascii.hexlify(os.urandom(8)).decode() + ".mp4")
        t[0].download(filename=filename)
    except:
        warnings.warn(f"Video Not Found at {yt_link}")
        filename = None
    
    return filename, filename

def prepare_output_file(tokenizer_output):
    # Add some random values so that no two file names are same
    output_file_name = "output_" + binascii.hexlify(os.urandom(8)).decode()
    midi_output = os.path.join(outputs_dir, output_file_name + ".mid")
        
    # write the .mid file
    tokenizer_output[0].write(midi_output)
    
    # convert .mid file to .wav using `midi2audio`
    wav_output = midi_output.replace(".mid", ".wav")
    midi2audio.FluidSynth().midi_to_audio(midi_output, wav_output)

    from IPython.display import Audio
    return wav_output, wav_output, midi_output
    
def inference(file_uploaded, composer):
    # to save the native sampling rate of the file, sr=None is used, but this can cause some silent errors where the
    # generated output will not be upto the desired quality. If that happens please consider switching sr to 44100 Hz.
    waveform, sr = librosa.load(file_uploaded, sr=None) 
    
    inputs = processor(audio=waveform, sampling_rate=sr, return_tensors="pt").to(device)
    model_output = model.generate(input_features=inputs["input_features"], composer=composer)
    tokenizer_output = processor.batch_decode(token_ids=model_output.to("cpu"), feature_extractor_output=inputs.to("cpu"))["pretty_midi_objects"]

    return prepare_output_file(tokenizer_output)    


block = gr.Blocks()


with block:
    gr.HTML(
        """
            <div style="text-align: center; max-width: 700px; margin: 0 auto;">
              <div
                style="
                  display: inline-flex;
                  align-items: center;
                  gap: 0.8rem;
                  font-size: 1.75rem;
                "
              >
                <h1 style="font-weight: 900; margin-bottom: 7px;">
                  Pop2piano
                </h1>
              </div>
              <p style="margin-bottom: 10px; font-size: 94%">
                A demo for Pop2Piano:Pop Audio-based Piano Cover Generation.<br>
                Please select the composer(Arranger) and upload the pop audio or enter the YouTube link and then click Generate.
              </p>
            </div>
        """
    )
    with gr.Group():
        with gr.Box():
            with gr.Row().style(mobile_collapse=False, equal_height=True):
                file_uploaded = gr.Audio(label="Upload an audio", type="filepath")

                with gr.Column():
                    with gr.Row():
                        yt_link = gr.Textbox(label="Enter YouTube link of the Video")
                        yt_btn = gr.Button("Get Audio from the YT link(Press this before pressing Generate)")
                    
                    
                    yt_audio_path = gr.Audio(label="Audio Extracted from the YouTube Video", interactive=False)
                    
                    yt_btn.click(get_audio_from_yt_video, inputs=[yt_link], outputs=[yt_audio_path, file_uploaded])

        with gr.Box():
            with gr.Row().style(mobile_collapse=False, equal_height=True):
                composer = gr.Dropdown(label="Arranger", choices=composers, value="composer1")
                
            with gr.Row().style(mobile_collapse=False, equal_height=True):
                btn = gr.Button("Generate")
        
        with gr.Box():
            with gr.Row().style(mobile_collapse=False, equal_height=True):
                wav_output2 = gr.File(label="Download the Generated MIDI (.wav)")
                wav_output1 = gr.Audio(label="Listen to the Generated MIDI")
                midi_output = gr.File(label="Download the Generated MIDI (.mid)")
                btn.click(inference, inputs=[file_uploaded, composer], outputs=[wav_output1, wav_output2, midi_output])
                
        gr.Examples([
            ["./examples/custom_song.mp3", "composer1"],
            ["./examples/BornThisWay.mp3", "composer1"],
            ["./examples/Sk8erBoi.mp3", "composer2"],
        ],
            fn=inference,
            inputs=[file_uploaded, composer],
            outputs=[wav_output1, wav_output2, midi_output],
            cache_examples=True
        )
        gr.HTML(
            """
        <div class="footer">
                    <p><a href="http://sweetcocoa.github.io/pop2piano_samples" style="text-decoration: underline;" target="_blank">Project Page</a>
                    </p>
        </div>
        """
        )

block.launch(debug=False)
shutil.rmtree("./midi_wav_outputs")