Create app.py

app.py

@@ -0,0 +1,50 @@
+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)
+