Delete app_onnx.py

app_onnx.py

@@ -1,594 +0,0 @@
-import spaces
-import random
-import argparse
-import glob
-import json
-import os
-import time
-from concurrent.futures import ThreadPoolExecutor
-
-import gradio as gr
-import numpy as np
-import onnxruntime as rt
-import tqdm
-from huggingface_hub import hf_hub_download
-
-import MIDI
-from midi_synthesizer import MidiSynthesizer
-from midi_tokenizer import MIDITokenizer
-
-MAX_SEED = np.iinfo(np.int32).max
-in_space = os.getenv("SYSTEM") == "spaces"
-
-
-def softmax(x, axis):
-    x_max = np.amax(x, axis=axis, keepdims=True)
-    exp_x_shifted = np.exp(x - x_max)
-    return exp_x_shifted / np.sum(exp_x_shifted, axis=axis, keepdims=True)
-
-
-def sample_top_p_k(probs, p, k, generator=None):
-    if generator is None:
-        generator = np.random
-    probs_idx = np.argsort(-probs, axis=-1)
-    probs_sort = np.take_along_axis(probs, probs_idx, -1)
-    probs_sum = np.cumsum(probs_sort, axis=-1)
-    mask = probs_sum - probs_sort > p
-    probs_sort[mask] = 0.0
-    mask = np.zeros(probs_sort.shape[-1])
-    mask[:k] = 1
-    probs_sort = probs_sort * mask
-    probs_sort /= np.sum(probs_sort, axis=-1, keepdims=True)
-    shape = probs_sort.shape
-    probs_sort_flat = probs_sort.reshape(-1, shape[-1])
-    probs_idx_flat = probs_idx.reshape(-1, shape[-1])
-    next_token = np.stack([generator.choice(idxs, p=pvals) for pvals, idxs in zip(probs_sort_flat, probs_idx_flat)])
-    next_token = next_token.reshape(*shape[:-1])
-    return next_token
-
-
-def apply_io_binding(model: rt.InferenceSession, inputs, outputs, batch_size, past_len, cur_len):
-    io_binding = model.io_binding()
-    for input_ in  model.get_inputs():
-        name = input_.name
-        if name.startswith("past_key_values"):
-            present_name = name.replace("past_key_values", "present")
-            if present_name in outputs:
-                v = outputs[present_name]
-            else:
-                v = rt.OrtValue.ortvalue_from_shape_and_type(
-                    (batch_size, input_.shape[1], past_len, input_.shape[3]),
-                    element_type=np.float32,
-                    device_type=device)
-            inputs[name] = v
-        else:
-            v = inputs[name]
-        io_binding.bind_ortvalue_input(name, v)
-
-    for output in model.get_outputs():
-        name = output.name
-        if name.startswith("present"):
-            v = rt.OrtValue.ortvalue_from_shape_and_type(
-                (batch_size, output.shape[1], cur_len, output.shape[3]),
-                element_type=np.float32,
-                device_type=device)
-            outputs[name] = v
-        else:
-            v = outputs[name]
-        io_binding.bind_ortvalue_output(name, v)
-    return io_binding
-
-def generate(model, prompt=None, batch_size=1, max_len=512, temp=1.0, top_p=0.98, top_k=20,
-             disable_patch_change=False, disable_control_change=False, disable_channels=None, generator=None):
-    tokenizer = model[2]
-    if disable_channels is not None:
-        disable_channels = [tokenizer.parameter_ids["channel"][c] for c in disable_channels]
-    else:
-        disable_channels = []
-    if generator is None:
-        generator = np.random
-    max_token_seq = tokenizer.max_token_seq
-    if prompt is None:
-        input_tensor = np.full((1, max_token_seq), tokenizer.pad_id, dtype=np.int64)
-        input_tensor[0, 0] = tokenizer.bos_id  # bos
-        input_tensor = input_tensor[None, :, :]
-        input_tensor = np.repeat(input_tensor, repeats=batch_size, axis=0)
-    else:
-        if len(prompt.shape) == 2:
-            prompt = prompt[None, :]
-            prompt = np.repeat(prompt, repeats=batch_size, axis=0)
-        elif prompt.shape[0] == 1:
-            prompt = np.repeat(prompt, repeats=batch_size, axis=0)
-        elif len(prompt.shape) != 3 or prompt.shape[0] != batch_size:
-            raise ValueError(f"invalid shape for prompt, {prompt.shape}")
-        prompt = prompt[..., :max_token_seq]
-        if prompt.shape[-1] < max_token_seq:
-            prompt = np.pad(prompt, ((0, 0), (0, 0), (0, max_token_seq - prompt.shape[-1])),
-                            mode="constant", constant_values=tokenizer.pad_id)
-        input_tensor = prompt
-    cur_len = input_tensor.shape[1]
-    bar = tqdm.tqdm(desc="generating", total=max_len - cur_len, disable=in_space)
-    model0_inputs = {}
-    model0_outputs = {}
-    emb_size = 1024
-    for output in model[0].get_outputs():
-        if output.name == "hidden":
-            emb_size = output.shape[2]
-    past_len = 0
-    with bar:
-        while cur_len < max_len:
-            end = [False] * batch_size
-            model0_inputs["x"] = rt.OrtValue.ortvalue_from_numpy(input_tensor[:, past_len:], device_type=device)
-            model0_outputs["hidden"] = rt.OrtValue.ortvalue_from_shape_and_type(
-                (batch_size, cur_len - past_len, emb_size),
-                element_type=np.float32,
-                device_type=device)
-            io_binding = apply_io_binding(model[0], model0_inputs, model0_outputs, batch_size, past_len, cur_len)
-            io_binding.synchronize_inputs()
-            model[0].run_with_iobinding(io_binding)
-            io_binding.synchronize_outputs()
-
-            hidden = model0_outputs["hidden"].numpy()[:, -1:]
-            next_token_seq = np.zeros((batch_size, 0), dtype=np.int64)
-            event_names = [""] * batch_size
-            model1_inputs = {"hidden": rt.OrtValue.ortvalue_from_numpy(hidden, device_type=device)}
-            model1_outputs = {}
-            for i in range(max_token_seq):
-                mask = np.zeros((batch_size, tokenizer.vocab_size), dtype=np.int64)
-                for b in range(batch_size):
-                    if end[b]:
-                        mask[b, tokenizer.pad_id] = 1
-                        continue
-                    if i == 0:
-                        mask_ids = list(tokenizer.event_ids.values()) + [tokenizer.eos_id]
-                        if disable_patch_change:
-                            mask_ids.remove(tokenizer.event_ids["patch_change"])
-                        if disable_control_change:
-                            mask_ids.remove(tokenizer.event_ids["control_change"])
-                        mask[b, mask_ids] = 1
-                    else:
-                        param_names = tokenizer.events[event_names[b]]
-                        if i > len(param_names):
-                            mask[b, tokenizer.pad_id] = 1
-                            continue
-                        param_name = param_names[i - 1]
-                        mask_ids = tokenizer.parameter_ids[param_name]
-                        if param_name == "channel":
-                            mask_ids = [i for i in mask_ids if i not in disable_channels]
-                        mask[b, mask_ids] = 1
-                mask = mask[:, None, :]
-                x = next_token_seq
-                if i != 0:
-                    # cached
-                    if i == 1:
-                        hidden = np.zeros((batch_size, 0, emb_size), dtype=np.float32)
-                        model1_inputs["hidden"] = rt.OrtValue.ortvalue_from_numpy(hidden, device_type=device)
-                    x = x[:, -1:]
-                model1_inputs["x"] = rt.OrtValue.ortvalue_from_numpy(x, device_type=device)
-                model1_outputs["y"] = rt.OrtValue.ortvalue_from_shape_and_type(
-                    (batch_size, 1, tokenizer.vocab_size),
-                    element_type=np.float32,
-                    device_type=device
-                )
-                io_binding = apply_io_binding(model[1], model1_inputs, model1_outputs, batch_size, i, i+1)
-                io_binding.synchronize_inputs()
-                model[1].run_with_iobinding(io_binding)
-                io_binding.synchronize_outputs()
-                logits = model1_outputs["y"].numpy()
-                scores = softmax(logits / temp, -1) * mask
-                samples = sample_top_p_k(scores, top_p, top_k, generator)
-                if i == 0:
-                    next_token_seq = samples
-                    for b in range(batch_size):
-                        if end[b]:
-                            continue
-                        eid = samples[b].item()
-                        if eid == tokenizer.eos_id:
-                            end[b] = True
-                        else:
-                            event_names[b] = tokenizer.id_events[eid]
-                else:
-                    next_token_seq = np.concatenate([next_token_seq, samples], axis=1)
-                    if all([len(tokenizer.events[event_names[b]]) == i for b in range(batch_size) if not end[b]]):
-                        break
-            if next_token_seq.shape[1] < max_token_seq:
-                next_token_seq = np.pad(next_token_seq,
-                                        ((0, 0), (0, max_token_seq - next_token_seq.shape[-1])),
-                                        mode="constant", constant_values=tokenizer.pad_id)
-            next_token_seq = next_token_seq[:, None, :]
-            input_tensor = np.concatenate([input_tensor, next_token_seq], axis=1)
-            past_len = cur_len
-            cur_len += 1
-            bar.update(1)
-            yield next_token_seq[:, 0]
-            if all(end):
-                break
-
-
-def create_msg(name, data):
-    return {"name": name, "data": data}
-
-
-def send_msgs(msgs):
-    return json.dumps(msgs)
-
-
-def get_duration(model_name, tab, mid_seq, continuation_state, continuation_select, instruments, drum_kit, bpm,
-                 time_sig, key_sig, mid, midi_events, reduce_cc_st, remap_track_channel, add_default_instr,
-                 remove_empty_channels, seed, seed_rand, gen_events, temp, top_p, top_k, allow_cc):
-    t = gen_events // 28
-    if "large" in model_name:
-        t = gen_events // 20
-    return t + 10
-
-
-@spaces.GPU(duration=get_duration)
-def run(model_name, tab, mid_seq, continuation_state, continuation_select, instruments, drum_kit, bpm, time_sig,
-        key_sig, mid, midi_events, reduce_cc_st, remap_track_channel, add_default_instr, remove_empty_channels,
-        seed, seed_rand, gen_events, temp, top_p, top_k, allow_cc):
-    model = models[model_name]
-    model_base = rt.InferenceSession(model[0], providers=providers)
-    model_token = rt.InferenceSession(model[1], providers=providers)
-    tokenizer = model[2]
-    model = [model_base, model_token, tokenizer]
-    bpm = int(bpm)
-    if time_sig == "auto":
-        time_sig = None
-        time_sig_nn = 4
-        time_sig_dd = 2
-    else:
-        time_sig_nn, time_sig_dd = time_sig.split('/')
-        time_sig_nn = int(time_sig_nn)
-        time_sig_dd = {2: 1, 4: 2, 8: 3}[int(time_sig_dd)]
-    if key_sig == 0:
-        key_sig = None
-        key_sig_sf = 0
-        key_sig_mi = 0
-    else:
-        key_sig = (key_sig - 1)
-        key_sig_sf = key_sig // 2 - 7
-        key_sig_mi = key_sig % 2
-    gen_events = int(gen_events)
-    max_len = gen_events
-    if seed_rand:
-        seed = random.randint(0, MAX_SEED)
-    generator = np.random.RandomState(seed)
-    disable_patch_change = False
-    disable_channels = None
-    if tab == 0:
-        i = 0
-        mid = [[tokenizer.bos_id] + [tokenizer.pad_id] * (tokenizer.max_token_seq - 1)]
-        if tokenizer.version == "v2":
-            if time_sig is not None:
-                mid.append(tokenizer.event2tokens(["time_signature", 0, 0, 0, time_sig_nn - 1, time_sig_dd - 1]))
-            if key_sig is not None:
-                mid.append(tokenizer.event2tokens(["key_signature", 0, 0, 0, key_sig_sf + 7, key_sig_mi]))
-        if bpm != 0:
-            mid.append(tokenizer.event2tokens(["set_tempo", 0, 0, 0, bpm]))
-        patches = {}
-        if instruments is None:
-            instruments = []
-        for instr in instruments:
-            patches[i] = patch2number[instr]
-            i = (i + 1) if i != 8 else 10
-        if drum_kit != "None":
-            patches[9] = drum_kits2number[drum_kit]
-        for i, (c, p) in enumerate(patches.items()):
-            mid.append(tokenizer.event2tokens(["patch_change", 0, 0, i + 1, c, p]))
-        mid = np.asarray([mid] * OUTPUT_BATCH_SIZE, dtype=np.int64)
-        mid_seq = mid.tolist()
-        if len(instruments) > 0:
-            disable_patch_change = True
-            disable_channels = [i for i in range(16) if i not in patches]
-    elif tab == 1 and mid is not None:
-        eps = 4 if reduce_cc_st else 0
-        mid = tokenizer.tokenize(MIDI.midi2score(mid), cc_eps=eps, tempo_eps=eps,
-                                 remap_track_channel=remap_track_channel,
-                                 add_default_instr=add_default_instr,
-                                 remove_empty_channels=remove_empty_channels)
-        mid = mid[:int(midi_events)]
-        mid = np.asarray([mid] * OUTPUT_BATCH_SIZE, dtype=np.int64)
-        mid_seq = mid.tolist()
-    elif tab == 2 and mid_seq is not None:
-        mid = np.asarray(mid_seq, dtype=np.int64)
-        if continuation_select > 0:
-            continuation_state.append(mid_seq)
-            mid = np.repeat(mid[continuation_select - 1:continuation_select], repeats=OUTPUT_BATCH_SIZE, axis=0)
-            mid_seq = mid.tolist()
-        else:
-            continuation_state.append(mid.shape[1])
-    else:
-        continuation_state = [0]
-        mid = [[tokenizer.bos_id] + [tokenizer.pad_id] * (tokenizer.max_token_seq - 1)]
-        mid = np.asarray([mid] * OUTPUT_BATCH_SIZE, dtype=np.int64)
-        mid_seq = mid.tolist()
-
-    if mid is not None:
-        max_len += mid.shape[1]
-
-    init_msgs = [create_msg("progress", [0, gen_events])]
-    if not (tab == 2 and continuation_select == 0):
-        for i in range(OUTPUT_BATCH_SIZE):
-            events = [tokenizer.tokens2event(tokens) for tokens in mid_seq[i]]
-            init_msgs += [create_msg("visualizer_clear", [i, tokenizer.version]),
-                          create_msg("visualizer_append", [i, events])]
-    yield mid_seq, continuation_state, seed, send_msgs(init_msgs)
-    midi_generator = generate(model, mid, batch_size=OUTPUT_BATCH_SIZE, max_len=max_len, temp=temp,
-                              top_p=top_p, top_k=top_k, disable_patch_change=disable_patch_change,
-                              disable_control_change=not allow_cc, disable_channels=disable_channels,
-                              generator=generator)
-    events = [list() for i in range(OUTPUT_BATCH_SIZE)]
-    t = time.time() + 1
-    for i, token_seqs in enumerate(midi_generator):
-        token_seqs = token_seqs.tolist()
-        for j in range(OUTPUT_BATCH_SIZE):
-            token_seq = token_seqs[j]
-            mid_seq[j].append(token_seq)
-            events[j].append(tokenizer.tokens2event(token_seq))
-        if time.time() - t > 0.5:
-            msgs = [create_msg("progress", [i + 1, gen_events])]
-            for j in range(OUTPUT_BATCH_SIZE):
-                msgs += [create_msg("visualizer_append", [j, events[j]])]
-                events[j] = list()
-            yield mid_seq, continuation_state, seed, send_msgs(msgs)
-            t = time.time()
-    yield mid_seq, continuation_state, seed, send_msgs([])
-
-
-def finish_run(model_name, mid_seq):
-    if mid_seq is None:
-        outputs = [None] * OUTPUT_BATCH_SIZE
-        return *outputs, []
-    tokenizer = models[model_name][2]
-    outputs = []
-    end_msgs = [create_msg("progress", [0, 0])]
-    if not os.path.exists("outputs"):
-        os.mkdir("outputs")
-    for i in range(OUTPUT_BATCH_SIZE):
-        events = [tokenizer.tokens2event(tokens) for tokens in mid_seq[i]]
-        mid = tokenizer.detokenize(mid_seq[i])
-        with open(f"outputs/output{i + 1}.mid", 'wb') as f:
-            f.write(MIDI.score2midi(mid))
-        outputs.append(f"outputs/output{i + 1}.mid")
-        end_msgs += [create_msg("visualizer_clear", [i, tokenizer.version]),
-                     create_msg("visualizer_append", [i, events]),
-                     create_msg("visualizer_end", i)]
-    return *outputs, send_msgs(end_msgs)
-
-
-def synthesis_task(mid):
-    return synthesizer.synthesis(MIDI.score2opus(mid))
-
-def render_audio(model_name, mid_seq, should_render_audio):
-    if (not should_render_audio) or mid_seq is None:
-        outputs = [None] * OUTPUT_BATCH_SIZE
-        return tuple(outputs)
-    tokenizer = models[model_name][2]
-    outputs = []
-    if not os.path.exists("outputs"):
-        os.mkdir("outputs")
-    audio_futures = []
-    for i in range(OUTPUT_BATCH_SIZE):
-        mid = tokenizer.detokenize(mid_seq[i])
-        audio_future = thread_pool.submit(synthesis_task, mid)
-        audio_futures.append(audio_future)
-    for future in audio_futures:
-        outputs.append((44100, future.result()))
-    if OUTPUT_BATCH_SIZE == 1:
-        return outputs[0]
-    return tuple(outputs)
-
-
-def undo_continuation(model_name, mid_seq, continuation_state):
-    if mid_seq is None or len(continuation_state) < 2:
-        return mid_seq, continuation_state, send_msgs([])
-    tokenizer = models[model_name][2]
-    if isinstance(continuation_state[-1], list):
-        mid_seq = continuation_state[-1]
-    else:
-        mid_seq = [ms[:continuation_state[-1]] for ms in mid_seq]
-    continuation_state = continuation_state[:-1]
-    end_msgs = [create_msg("progress", [0, 0])]
-    for i in range(OUTPUT_BATCH_SIZE):
-        events = [tokenizer.tokens2event(tokens) for tokens in mid_seq[i]]
-        end_msgs += [create_msg("visualizer_clear", [i, tokenizer.version]),
-                     create_msg("visualizer_append", [i, events]),
-                     create_msg("visualizer_end", i)]
-    return mid_seq, continuation_state, send_msgs(end_msgs)
-
-
-def load_javascript(dir="javascript"):
-    scripts_list = glob.glob(f"{dir}/*.js")
-    javascript = ""
-    for path in scripts_list:
-        with open(path, "r", encoding="utf8") as jsfile:
-            js_content = jsfile.read()
-            js_content = js_content.replace("const MIDI_OUTPUT_BATCH_SIZE=4;",
-                                            f"const MIDI_OUTPUT_BATCH_SIZE={OUTPUT_BATCH_SIZE};")
-            javascript += f"\n<!-- {path} --><script>{js_content}</script>"
-    template_response_ori = gr.routes.templates.TemplateResponse
-
-    def template_response(*args, **kwargs):
-        res = template_response_ori(*args, **kwargs)
-        res.body = res.body.replace(
-            b'</head>', f'{javascript}</head>'.encode("utf8"))
-        res.init_headers()
-        return res
-
-    gr.routes.templates.TemplateResponse = template_response
-
-
-def hf_hub_download_retry(repo_id, filename):
-    print(f"downloading {repo_id} {filename}")
-    retry = 0
-    err = None
-    while retry < 30:
-        try:
-            return hf_hub_download(repo_id=repo_id, filename=filename)
-        except Exception as e:
-            err = e
-            retry += 1
-    if err:
-        raise err
-
-
-def get_tokenizer(repo_id):
-    config_path = hf_hub_download_retry(repo_id=repo_id, filename=f"config.json")
-    with open(config_path, "r") as f:
-        config = json.load(f)
-    tokenizer = MIDITokenizer(config["tokenizer"]["version"])
-    tokenizer.set_optimise_midi(config["tokenizer"]["optimise_midi"])
-    return tokenizer
-
-
-number2drum_kits = {-1: "None", 0: "Standard", 8: "Room", 16: "Power", 24: "Electric", 25: "TR-808", 32: "Jazz",
-                    40: "Blush", 48: "Orchestra"}
-patch2number = {v: k for k, v in MIDI.Number2patch.items()}
-drum_kits2number = {v: k for k, v in number2drum_kits.items()}
-key_signatures = ['C♭', 'A♭m', 'G♭', 'E♭m', 'D♭', 'B♭m', 'A♭', 'Fm', 'E♭', 'Cm', 'B♭', 'Gm', 'F', 'Dm',
-                  'C', 'Am', 'G', 'Em', 'D', 'Bm', 'A', 'F♯m', 'E', 'C♯m', 'B', 'G♯m', 'F♯', 'D♯m', 'C♯', 'A♯m']
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
-    parser.add_argument("--port", type=int, default=7860, help="gradio server port")
-    parser.add_argument("--device", type=str, default="cuda", help="device to run model")
-    parser.add_argument("--batch", type=int, default=8, help="batch size")
-    parser.add_argument("--max-gen", type=int, default=1024, help="max")
-    opt = parser.parse_args()
-    OUTPUT_BATCH_SIZE = opt.batch
-    soundfont_path = hf_hub_download_retry(repo_id="skytnt/midi-model", filename="soundfont.sf2")
-    thread_pool = ThreadPoolExecutor(max_workers=OUTPUT_BATCH_SIZE)
-    synthesizer = MidiSynthesizer(soundfont_path)
-    models_info = {
-        "generic pretrain model (tv2o-medium) by skytnt": [
-            "skytnt/midi-model-tv2o-medium", "", {
-                "jpop": "skytnt/midi-model-tv2om-jpop-lora",
-                "touhou": "skytnt/midi-model-tv2om-touhou-lora"
-            }
-        ],
-        "generic pretrain model (tv2o-large) by asigalov61": [
-            "asigalov61/Music-Llama", "", {}
-        ],
-        "generic pretrain model (tv2o-medium) by asigalov61": [
-            "asigalov61/Music-Llama-Medium", "", {}
-        ],
-        "generic pretrain model (tv1-medium) by skytnt": [
-            "skytnt/midi-model", "", {}
-        ]
-    }
-    models = {}
-    providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
-    device = "cuda"
-
-    for name, (repo_id, path, loras) in models_info.items():
-        model_base_path = hf_hub_download_retry(repo_id=repo_id, filename=f"{path}onnx/model_base.onnx")
-        model_token_path = hf_hub_download_retry(repo_id=repo_id, filename=f"{path}onnx/model_token.onnx")
-        tokenizer = get_tokenizer(repo_id)
-        models[name] = [model_base_path, model_token_path, tokenizer]
-        for lora_name, lora_repo in loras.items():
-            model_base_path = hf_hub_download_retry(repo_id=lora_repo, filename=f"onnx/model_base.onnx")
-            model_token_path = hf_hub_download_retry(repo_id=lora_repo, filename=f"onnx/model_token.onnx")
-            models[f"{name} with {lora_name} lora"] = [model_base_path, model_token_path, tokenizer]
-
-    load_javascript()
-    app = gr.Blocks(theme=gr.themes.Soft())
-    with app:
-        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Midi Composer</h1>")
-        gr.Markdown("\n\n"
-                    "A modified version of the Midi-Generator for the IAT-360 Course by Ethan Lum\n\n"
-                    "Demo for [SkyTNT/midi-model](https://github.com/SkyTNT/midi-model)\n\n"
-                    "[Open In Colab]"
-                    "(https://colab.research.google.com/github/SkyTNT/midi-model/blob/main/demo.ipynb)"
-                    " or [download windows app](https://github.com/SkyTNT/midi-model/releases)"
-                    " for unlimited generation\n\n"
-                    "**Update v1.3**: MIDITokenizerV2 and new MidiVisualizer\n\n"
-                    "The current **best** model: generic pretrain model (tv2o-medium) by skytnt"
-                    )
-        js_msg = gr.Textbox(elem_id="msg_receiver", visible=False)
-        js_msg.change(None, [js_msg], [], js="""
-        (msg_json) =>{
-            let msgs = JSON.parse(msg_json);
-            executeCallbacks(msgReceiveCallbacks, msgs);
-            return [];
-        }
-        """)
-        input_model = gr.Dropdown(label="select model", choices=list(models.keys()),
-                                  type="value", value=list(models.keys())[0])
-        tab_select = gr.State(value=0)
-        with gr.Tabs():
-            
-            with gr.TabItem("midi prompt") as tab2:
-                input_midi = gr.File(label="input midi", file_types=[".midi", ".mid"], type="binary")
-                input_midi_events = gr.Slider(label="use first n midi events as prompt", minimum=1, maximum=512,
-                                              step=1,
-                                              value=128)
-                input_reduce_cc_st = gr.Checkbox(label="reduce control_change and set_tempo events", value=True)
-                input_remap_track_channel = gr.Checkbox(
-                    label="remap tracks and channels so each track has only one channel and in order", value=True)
-                input_add_default_instr = gr.Checkbox(
-                    label="add a default instrument to channels that don't have an instrument", value=True)
-                input_remove_empty_channels = gr.Checkbox(label="remove channels without notes", value=False)
-                example2 = gr.Examples([[file, 128] for file in glob.glob("example/*.mid")],
-                                       [input_midi, input_midi_events])
-            with gr.TabItem("last output prompt") as tab3:
-                gr.Markdown("Continue generating on the last output.")
-                input_continuation_select = gr.Radio(label="select output to continue generating", value="all",
-                                                     choices=["all"] + [f"output{i + 1}" for i in
-                                                                        range(OUTPUT_BATCH_SIZE)],
-                                                     type="index"
-                                                     )
-                undo_btn = gr.Button("undo the last continuation")
-
-        
-        tab2.select(lambda: 1, None, tab_select, queue=False)
-        tab3.select(lambda: 2, None, tab_select, queue=False)
-        input_seed = gr.Slider(label="seed", minimum=0, maximum=2 ** 31 - 1,
-                               step=1, value=0)
-        input_seed_rand = gr.Checkbox(label="random seed", value=True)
-        input_gen_events = gr.Slider(label="generate max n midi events", minimum=1, maximum=opt.max_gen,
-                                     step=1, value=opt.max_gen // 2)
-        with gr.Accordion("options", open=False):
-            input_temp = gr.Slider(label="temperature", minimum=0.1, maximum=1.2, step=0.01, value=1)
-            input_top_p = gr.Slider(label="top p", minimum=0.1, maximum=1, step=0.01, value=0.95)
-            input_top_k = gr.Slider(label="top k", minimum=1, maximum=128, step=1, value=20)
-            input_allow_cc = gr.Checkbox(label="allow midi cc event", value=True)
-            input_render_audio = gr.Checkbox(label="render audio after generation", value=True)
-            example3 = gr.Examples([[1, 0.94, 128], [1, 0.98, 20], [1, 0.98, 12]],
-                                   [input_temp, input_top_p, input_top_k])
-        run_btn = gr.Button("generate", variant="primary")
-        # stop_btn = gr.Button("stop and output")
-        output_midi_seq = gr.State()
-        output_continuation_state = gr.State([0])
-        midi_outputs = []
-        audio_outputs = []
-        with gr.Tabs(elem_id="output_tabs"):
-            for i in range(OUTPUT_BATCH_SIZE):
-                with gr.TabItem(f"output {i + 1}") as tab1:
-                    output_midi_visualizer = gr.HTML(elem_id=f"midi_visualizer_container_{i}")
-                    output_audio = gr.Audio(label="output audio", format="mp3", elem_id=f"midi_audio_{i}")
-                    output_midi = gr.File(label="output midi", file_types=[".mid"])
-                    midi_outputs.append(output_midi)
-                    audio_outputs.append(output_audio)
-        run_event = run_btn.click(run, [input_model, tab_select, output_midi_seq, output_continuation_state,
-                                        input_continuation_select, input_instruments, input_drum_kit, input_bpm,
-                                        input_time_sig, input_key_sig, input_midi, input_midi_events,
-                                        input_reduce_cc_st, input_remap_track_channel,
-                                        input_add_default_instr, input_remove_empty_channels,
-                                        input_seed, input_seed_rand, input_gen_events, input_temp, input_top_p,
-                                        input_top_k, input_allow_cc],
-                                  [output_midi_seq, output_continuation_state, input_seed, js_msg], queue=True)
-        finish_run_event = run_event.then(fn=finish_run,
-                                          inputs=[input_model, output_midi_seq],
-                                          outputs=midi_outputs + [js_msg],
-                                          queue=False)
-        finish_run_event.then(fn=render_audio,
-                              inputs=[input_model, output_midi_seq, input_render_audio],
-                              outputs=audio_outputs,
-                              queue=False)
-        # stop_btn.click(None, [], [], cancels=run_event,
-        #                queue=False)
-        undo_btn.click(undo_continuation, [input_model, output_midi_seq, output_continuation_state],
-                       [output_midi_seq, output_continuation_state, js_msg], queue=False)
-    app.queue().launch(server_port=opt.port, share=opt.share, inbrowser=True, ssr_mode=False)
-    thread_pool.shutdown()