import gradio as gr
import torch

from scipy.io import wavfile
import numpy as np
from PIL import Image

from spectro import wav_bytes_from_spectrogram_image, spectrogram_from_waveform, image_from_spectrogram

from diffusers import StableDiffusionPipeline
from diffusers import StableDiffusionImg2ImgPipeline

from share_btn import community_icon_html, loading_icon_html, share_js

MODEL_ID = "riffusion/riffusion-model-v1"
pipe = StableDiffusionPipeline.from_pretrained(MODEL_ID, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
pipe2 = StableDiffusionImg2ImgPipeline.from_pretrained(MODEL_ID, torch_dtype=torch.float16)
pipe2 = pipe2.to("cuda")

def predict(prompt, audio_input, duration):
    if audio_input == None:
        return classic(prompt, duration)
    else:
        return audio_transfer(prompt, audio_input)
    

def classic(prompt, duration):
    if duration == 5:
        width_duration=512
    else :
        width_duration = 512 + ((int(duration)-5) * 128)
    spec = pipe(prompt, height=512, width=width_duration).images[0]
    print(spec)
    wav = wav_bytes_from_spectrogram_image(spec)
    with open("output.wav", "wb") as f:
        f.write(wav[0].getbuffer())
    return spec, 'output.wav', gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)

def audio_transfer(prompt, audio):
    # read uploaded file to wav
    rate, data = wavfile.read(audio)
    
    # convert to mono
    data = np.mean(data, axis=0)
    
    # convert to float32
    data = data.astype(np.float32)
    
    # take a random 7 second slice of the audio
    data = data[rate*7:rate*14]
    
    spectrogram = spectrogram_from_waveform(
        waveform=data,
        sample_rate=rate,
        # width=768,
        n_fft=8192,
        hop_length=512,
        win_length=8192,
    )

    spec = image_from_spectrogram(spectrogram)

    images = pipe2(
        prompt=prompt,
        image=spec,
        strength=0.5,
        guidance_scale=7
    ).images

    wav = wav_bytes_from_spectrogram_image(images[0])
    with open("output.wav", "wb") as f:
        f.write(wav[0].getbuffer())
    return images[0], 'output.wav', gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)

title = """
    <div style="text-align: center; max-width: 500px; margin: 0 auto;">
        <div
        style="
            display: inline-flex;
            align-items: center;
            gap: 0.8rem;
            font-size: 1.75rem;
            margin-bottom: 10px;
            line-height: 1em;
        "
        >
        <h1 style="font-weight: 600; margin-bottom: 7px;">
            Riffusion real-time music generation
        </h1>
        </div>
        <p style="margin-bottom: 10px;font-size: 94%;font-weight: 100;line-height: 1.5em;">
        Describe a musical prompt, generate music by getting a spectrogram image & sound.
        </p>
    </div>
"""

article = """
    <p style="font-size: 0.8em;line-height: 1.2em;border: 1px solid #374151;border-radius: 8px;padding: 20px;">
    About the model: Riffusion is a latent text-to-image diffusion model capable of generating spectrogram images given any text input. These spectrograms can be converted into audio clips.
    <br />—
    <br />The Riffusion model was created by fine-tuning the Stable-Diffusion-v1-5 checkpoint.
    <br />—
    <br />The model is intended for research purposes only. Possible research areas and tasks include 
    generation of artworks, audio, and use in creative processes, applications in educational or creative tools, research on generative models.

    </p>
    <div class="footer">
        <p>
        <a href="https://huggingface.co/riffusion/riffusion-model-v1" target="_blank">Riffusion model</a> by Seth Forsgren and Hayk Martiros - 
        Demo by 🤗 <a href="https://twitter.com/fffiloni" target="_blank">Sylvain Filoni</a>
        </p>
    </div>

    <p style="text-align: center;font-size: 94%">
        Do you need faster results ? You can skip the queue by duplicating this space: 
        <span style="display: flex;align-items: center;justify-content: center;height: 30px;">
            <a href="https://huggingface.co/fffiloni/spectrogram-to-music?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a>       
            <a href="https://colab.research.google.com/drive/1FhH3HlN8Ps_Pr9OR6Qcfbfz7utDvICl0?usp=sharing" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
        </span>
    </p>
"""

css = '''
    #col-container, #col-container-2 {max-width: 510px; margin-left: auto; margin-right: auto;}
    a {text-decoration-line: underline; font-weight: 600;}
    div#record_btn > .mt-6 {
        margin-top: 0!important;
    }
    div#record_btn > .mt-6 button {
        width: 100%;
        height: 40px;
    }
    .footer {
        margin-bottom: 45px;
        margin-top: 10px;
        text-align: center;
        border-bottom: 1px solid #e5e5e5;
    }
    .footer>p {
        font-size: .8rem;
        display: inline-block;
        padding: 0 10px;
        transform: translateY(10px);
        background: white;
    }
    .dark .footer {
        border-color: #303030;
    }
    .dark .footer>p {
        background: #0b0f19;
    }
    .animate-spin {
        animation: spin 1s linear infinite;
    }
    @keyframes spin {
        from {
            transform: rotate(0deg);
        }
        to {
            transform: rotate(360deg);
        }
    }
    #share-btn-container {
        display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
    }
    #share-btn {
        all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;right:0;
    }
    #share-btn * {
        all: unset;
    }
    #share-btn-container div:nth-child(-n+2){
        width: auto !important;
        min-height: 0px !important;
    }
    #share-btn-container .wrap {
        display: none !important;
    }

'''
 


with gr.Blocks(css=css) as demo:
    
    with gr.Column(elem_id="col-container"):
        
        gr.HTML(title)
        
        prompt_input = gr.Textbox(placeholder="a cat diva singing in a New York jazz club", label="Musical prompt", elem_id="prompt-in")
        audio_input = gr.Audio(label="audio input", type="filepath", source="upload")
        duration_input = gr.Slider(label="Duration in seconds", minimum=5, maximum=10, step=1, value=8, elem_id="duration-slider")
        send_btn = gr.Button(value="Get a new spectrogram ! ", elem_id="submit-btn")
            
    with gr.Column(elem_id="col-container-2"):
        
        spectrogram_output = gr.Image(label="spectrogram image result", elem_id="img-out")
        sound_output = gr.Audio(type='filepath', label="spectrogram sound", elem_id="music-out")
        
        with gr.Group(elem_id="share-btn-container"):
            community_icon = gr.HTML(community_icon_html, visible=False)
            loading_icon = gr.HTML(loading_icon_html, visible=False)
            share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
        
        gr.HTML(article)
    
    send_btn.click(predict, inputs=[prompt_input, audio_input, duration_input], outputs=[spectrogram_output, sound_output, share_button, community_icon, loading_icon])
    share_button.click(None, [], [], _js=share_js)

demo.queue(max_size=250).launch(debug=True)