from audiocraft.models import MusicGen
import streamlit as st
import os
import torch
import torchaudio
from io import BytesIO



st.set_page_config(
    page_icon=":musical_note:",
    page_title="Music Gen"
)

with open("style.css") as f:
        st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)

@st.cache_resource
def load_model():
    model = MusicGen.get_pretrained("facebook/musicgen-small")
    return model

def generate_music_tensors(description, duration: int):
    print("Description:", description)
    print("Duration:", duration)
    model = load_model()

    model.set_generation_params(
        use_sampling=True,
        top_k=250,
        duration=duration
    )

    output = model.generate(
        descriptions=[description],
        progress=True,
        return_tokens=True
    )
    return output[0]

def save_audio_to_bytes(samples: torch.Tensor):
    sample_rate = 32000
    assert samples.dim() == 2 or samples.dim() == 3
    samples = samples.detach().cpu()

    if samples.dim() == 2:
        samples = samples[None, ...]  # Add batch dimension if missing
    
    audio_buffer = BytesIO()
    torchaudio.save(audio_buffer, samples[0], sample_rate=sample_rate, format="wav")
    audio_buffer.seek(0)  # Move to the start of the buffer
    return audio_buffer



video_background = """
<style>
.video-container {
    position: fixed;
    top: 0;
    left: 0;
    width: 100%;
    height: 100%;
    overflow: hidden;
    z-index: -1;
}
video {
    position: absolute;
    top: 50%;
    left: 50%;
    min-width: 100%;
    min-height: 100%;
    width: auto;
    height: auto;
    z-index: -1;
    transform: translate(-50%, -50%);
    background-size: cover;
}
</style>

<div class="video-container">
    <video autoplay loop muted>
        <source src="https://go.screenpal.com/watch/cZX2oynVXxQ" type="video/mp4">
    </video>
</div>
"""

st.markdown(video_background, unsafe_allow_html=True)




def main():
    st.title("Your Music")

    with st.expander("See Explanation"):
        st.write("This app uses Meta's Audiocraft Music Gen model to generate audio based on your description.")

    text_area = st.text_area("Enter description")
    time_slider = st.slider("Select time duration (seconds)", 2, 20, 5)

    if text_area and time_slider:
        st.json(
            {
                "Description": text_area,
                "Selected duration": time_slider
            }
        )
        st.write("Generating your music... please wait.")



def main():
    st.title("Your Music")

    with st.expander("See Explanation"):
        st.write("This app uses Meta's Audiocraft Music Gen model to generate audio based on your description.")

    text_area = st.text_area("Enter description")
    time_slider = st.slider("Select time duration (seconds)", 2, 20, 5)

    if text_area and time_slider:
        st.json(
            {
                "Description": text_area,
                "Selected duration": time_slider
            }
        )
        st.write("We will be back with your music... please enjoy doing the rest of your tasks while we come back in some time :)")
        
        st.subheader("Generated Music")
        music_tensors = generate_music_tensors(text_area, time_slider)
        
        # Convert audio to bytes for playback and download
        audio_buffer = save_audio_to_bytes(music_tensors)
        
        # Play audio
        st.audio(audio_buffer, format="audio/wav")
        
        # Download button for audio
        st.download_button(
            label="Download Audio",
            data=audio_buffer,
            file_name="generated_music.wav",
            mime="audio/wav"
        )

if __name__ == "__main__":
    main()