model.py

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from music21 import converter, instrument, note, chord, stream
import gradio as gr

# Load Models
melody_model = AutoModelForCausalLM.from_pretrained("your_melody_model")  # Replace with actual model name
harmony_model = AutoModelForCausalLM.from_pretrained("your_harmony_model")  # Replace with actual model name
rhythm_model = AutoModelForCausalLM.from_pretrained("your_rhythm_model")  # Replace with actual model name
tokenizer = AutoTokenizer.from_pretrained("gpt2")

# Define functions for each step

def generate_melody(prompt, length=50):
    """Generates a melody sequence based on the given prompt."""
    inputs = tokenizer(prompt, return_tensors="pt")
    melody_output = melody_model.generate(inputs['input_ids'], max_length=length)
    melody_notes = tokenizer.decode(melody_output[0], skip_special_tokens=True)
    return melody_notes

def generate_harmony(melody_sequence, length=100):
    """Generates harmonic support based on the melody."""
    harmony_input = torch.cat([tokenizer.encode(melody_sequence, return_tensors="pt"), tokenizer("add harmony", return_tensors="pt")['input_ids']], dim=1)
    harmony_output = harmony_model.generate(harmony_input, max_length=length)
    harmony_notes = tokenizer.decode(harmony_output[0], skip_special_tokens=True)
    return harmony_notes

def generate_rhythm(harmony_sequence, length=50):
    """Adds rhythm to the harmony for structure."""
    rhythm_input = torch.cat([tokenizer.encode(harmony_sequence, return_tensors="pt"), tokenizer("add rhythm", return_tensors="pt")['input_ids']], dim=1)
    rhythm_output = rhythm_model.generate(rhythm_input, max_length=length)
    rhythm_sequence = tokenizer.decode(rhythm_output[0], skip_special_tokens=True)
    return rhythm_sequence

def create_midi(melody, harmony, rhythm):
    """Converts melody, harmony, and rhythm sequences to MIDI format."""
    composition = stream.Stream()
    
    for part in [melody, harmony, rhythm]:
        for token in part.split():
            if token.isdigit():
                midi_note = note.Note(int(token))
                midi_note.quarterLength = 0.5
                composition.append(midi_note)
            elif token == "rest":
                rest_note = note.Rest()
                rest_note.quarterLength = 0.5
                composition.append(rest_note)
    
    midi_fp = "generated_music.mid"
    composition.write('midi', fp=midi_fp)
    return midi_fp

# Full generation function
def generate_music(prompt, length=50):
    melody = generate_melody(prompt, length)
    harmony = generate_harmony(melody, length)
    rhythm = generate_rhythm(harmony, length)
    midi_file = create_midi(melody, harmony, rhythm)
    return midi_file

# Set up Gradio interface
iface = gr.Interface(
    fn=generate_music,
    inputs=["text", "slider"],
    outputs="file",
    title="Multi-Model AI Music Generator",
    description="Generate music using a multi-model AI system that combines melody, harmony, and rhythm layers.",
)

iface.launch()