import numpy as np
import tensorflow as tf
import librosa
git lfs install
git clone https://huggingface.co/amongusrickroll68/MeloMind
from diffusers import DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained("amongusrickroll68/MeloMind")
class TextToMusicGenerator:
    def __init__(self):
        self.model = tf.keras.models.load_model('path/to/model')
        self.sampling_rate = 22050
        
    def generate_music(self, prompt):
        prompt_encoded = self._encode_prompt(prompt)
        sequence = self._generate_sequence(prompt_encoded)
        audio = self._sequence_to_audio(sequence)
        return audio
    
    def _encode_prompt(self, prompt):
        # encode text prompt as input for the model
        # ...
        return prompt_encoded
    
    def _generate_sequence(self, prompt_encoded):
        # generate sequence of musical notes from encoded prompt
        # ...
        return sequence
    
    def _sequence_to_audio(self, sequence):
        # convert sequence to audio waveform
        notes = self._sequence_to_notes(sequence)
        audio = self._notes_to_audio(notes)
        return audio
    
    def _sequence_to_notes(self, sequence):
        # convert sequence of musical notes to Note objects
        # ...
        return notes
    
    def _notes_to_audio(self, notes):
        # convert Note objects to audio waveform
        # ...
        return audio
    
generator = TextToMusicGenerator()
prompt = "Generate a cheerful and upbeat song in the key of C major with a tempo of 120 bpm"
audio = generator.generate_music(prompt)
librosa.output.write_wav('generated_music.wav', audio, sr=generator.sampling_rate)
import gradio as gr

gr.Interface.load("models/amongusrickroll68/MeloMind").launch()