app.py

import os
import spaces 
import pickle
import subprocess
import torch
import torch.nn as nn
import gradio as gr
from dataclasses import asdict
from transformers import T5Tokenizer
from huggingface_hub import hf_hub_download

from transformer_model import Transformer  
from pyharp.core import ModelCard, build_endpoint
from pyharp.labels import LabelList

# Model/artifacts from HF Hub
REPO_ID = "amaai-lab/text2midi"
MODEL_PATH = hf_hub_download(repo_id=REPO_ID, filename="pytorch_model.bin")
TOKENIZER_PATH = hf_hub_download(repo_id=REPO_ID, filename="vocab_remi.pkl")
# Optional, only if you later add WAV preview:
SOUNDFONT_PATH = hf_hub_download(repo_id=REPO_ID, filename="soundfont.sf2")


# (Optional) MIDI -> WAV 
def save_wav(midi_path: str) -> str:
    directory = os.path.dirname(midi_path) or "."
    stem = os.path.splitext(os.path.basename(midi_path))[0]
    midi_filepath = os.path.join(directory, f"{stem}.mid")
    wav_filepath = os.path.join(directory, f"{stem}.wav")
    cmd = (
        f"fluidsynth -r 16000 {SOUNDFONT_PATH} -g 1.0 --quiet --no-shell "
        f"{midi_filepath} -T wav -F {wav_filepath} > /dev/null"
    )
    subprocess.run(cmd, shell=True, check=False)
    return wav_filepath


# Core Text -> MIDI
def generate_midi(prompt: str, temperature: float = 0.9, max_len: int = 500) -> str:
    device = "cuda" if torch.cuda.is_available() else "cpu"

    # Load REMI vocab/tokenizer (pickle dict used by the provided model)
    with open(TOKENIZER_PATH, "rb") as f:
        r_tokenizer = pickle.load(f)

    vocab_size = len(r_tokenizer)
    model = Transformer(
        vocab_size,    # vocab size
        768,           # d_model
        8,             # nhead
        2048,          # dim_feedforward
        18,            # nlayers
        1024,          # max_seq_len
        False,         # use_rotary
        8,             # rotary_dim
        device=device  # device
    )
    model.load_state_dict(torch.load(MODEL_PATH, map_location=device))
    model.eval()

    tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base")
    inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True)

    input_ids = nn.utils.rnn.pad_sequence(inputs.input_ids, batch_first=True, padding_value=0).to(device)
    attention_mask = nn.utils.rnn.pad_sequence(inputs.attention_mask, batch_first=True, padding_value=0).to(device)

    with torch.no_grad():
        output = model.generate(input_ids, attention_mask, max_len=max_len, temperature=temperature)

    output_list = output[0].tolist()
    generated_midi = r_tokenizer.decode(output_list)

    midi_path = "output.mid"
    generated_midi.dump_midi(midi_path)
    return midi_path

# HARP process function
# Return JSON first, MIDI second
@spaces.GPU(duration=120)
def process_fn(prompt: str, temperature: float, max_length: int):
    try:
        midi_path = generate_midi(prompt, float(temperature), int(max_length))
        labels = LabelList()  # add MidiLabel entries here if you have metadata
        return asdict(labels), midi_path
    except Exception as e:
        # On error: return JSON with error message, and no file
        return {"message": f"Error: {e}"}, None

# HARP Model Card
model_card = ModelCard(
    name="Text2MIDI (HARP)",
    description="Generate MIDI from a text prompt using a transformer decoder conditioned on T5 embeddings.",
    author="Keshav Bhandari, Abhinaba Roy, Kyra Wang, Geeta Puri, Simon Colton, Dorien Herremans",
    tags=["text-to-music", "midi", "generation"]
)

# Gradio + HARP UI
with gr.Blocks() as demo:
    gr.Markdown("## 🎶 text2midi")

    # Inputs
    prompt_in = gr.Textbox(label="Prompt").harp_required(True)
    temperature_in = gr.Slider(minimum=0.8, maximum=1.1, value=0.9, step=0.1, label="Temperature", interactive=True)
    maxlen_in = gr.Number(value=500, label="Max Length (tokens)", minimum=64, maximum=2000, step=64)

    # Outputs (JSON FIRST for HARP, then MIDI)
    labels_out = gr.JSON(label="Labels / Metadata")
    midi_out = gr.File(label="Generated MIDI", file_types=[".mid", ".midi"], type="filepath")

    # Build HARP endpoint
    _ = build_endpoint(
        model_card=model_card,
        input_components=[prompt_in, temperature_in, maxlen_in],
        output_components=[labels_out, midi_out],  # JSON first
        process_fn=process_fn
    )


# Launch App
demo.launch(share=True, show_error=True, debug=True)