| | import os |
| | os.system('pip install numpy==1.24.0') |
| | os.system('pip install git+https://github.com/mattloper/chumpy') |
| | os.system('pip install /home/user/app/pyrender') |
| | os.system('pip install pyglet==1.4.0a1') |
| | os.system('pip install triangle==20220202') |
| |
|
| | import gradio as gr |
| | import torch |
| | import time |
| | import numpy as np |
| | import pytorch_lightning as pl |
| | import subprocess |
| | from pathlib import Path |
| | from mGPT.data.build_data import build_data |
| | from mGPT.models.build_model import build_model |
| | from mGPT.config import parse_args |
| | from transformers import WhisperProcessor, WhisperForConditionalGeneration |
| | import librosa |
| | from huggingface_hub import snapshot_download |
| |
|
| | |
| | cfg = parse_args(phase="webui") |
| | cfg.FOLDER = 'cache' |
| | output_dir = Path(cfg.FOLDER) |
| | output_dir.mkdir(parents=True, exist_ok=True) |
| | pl.seed_everything(cfg.SEED_VALUE) |
| | if torch.cuda.is_available(): |
| | device = torch.device("cuda") |
| | else: |
| | device = torch.device("cpu") |
| |
|
| | model_path = snapshot_download(repo_id="bill-jiang/MotionGPT-base") |
| |
|
| | datamodule = build_data(cfg, phase="test") |
| | model = build_model(cfg, datamodule) |
| | state_dict = torch.load(f'{model_path}/motiongpt_s3_h3d.tar', |
| | map_location="cpu")["state_dict"] |
| | model.load_state_dict(state_dict) |
| | model.to(device) |
| |
|
| | audio_processor = WhisperProcessor.from_pretrained(cfg.model.whisper_path) |
| | audio_model = WhisperForConditionalGeneration.from_pretrained( |
| | cfg.model.whisper_path).to(device) |
| | forced_decoder_ids_zh = audio_processor.get_decoder_prompt_ids( |
| | language="zh", task="translate") |
| | forced_decoder_ids_en = audio_processor.get_decoder_prompt_ids( |
| | language="en", task="translate") |
| |
|
| | |
| | Video_Components = """ |
| | <div class="side-video" style="position: relative;"> |
| | <video width="340" autoplay loop> |
| | <source src="file/{video_path}" type="video/mp4"> |
| | </video> |
| | <a class="videodl-button" href="file/{video_path}" download="{video_fname}" title="Download Video"> |
| | <svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="#000000" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-video"><path d="m22 8-6 4 6 4V8Z"/><rect width="14" height="12" x="2" y="6" rx="2" ry="2"/></svg> |
| | </a> |
| | <a class="npydl-button" href="file/{motion_path}" download="{motion_fname}" title="Download Motion"> |
| | <svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="#000000" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-file-box"><path d="M14.5 22H18a2 2 0 0 0 2-2V7.5L14.5 2H6a2 2 0 0 0-2 2v4"/><polyline points="14 2 14 8 20 8"/><path d="M2.97 13.12c-.6.36-.97 1.02-.97 1.74v3.28c0 .72.37 1.38.97 1.74l3 1.83c.63.39 1.43.39 2.06 0l3-1.83c.6-.36.97-1.02.97-1.74v-3.28c0-.72-.37-1.38-.97-1.74l-3-1.83a1.97 1.97 0 0 0-2.06 0l-3 1.83Z"/><path d="m7 17-4.74-2.85"/><path d="m7 17 4.74-2.85"/><path d="M7 17v5"/></svg> |
| | </a> |
| | </div> |
| | """ |
| |
|
| | Video_Components_example = """ |
| | <div class="side-video" style="position: relative;"> |
| | <video width="340" autoplay loop controls> |
| | <source src="file/{video_path}" type="video/mp4"> |
| | </video> |
| | <a class="npydl-button" href="file/{video_path}" download="{video_fname}" title="Download Video"> |
| | <svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-video"><path d="m22 8-6 4 6 4V8Z"/><rect width="14" height="12" x="2" y="6" rx="2" ry="2"/></svg> |
| | </a> |
| | </div> |
| | """ |
| |
|
| | Text_Components = """ |
| | <h3 class="side-content" >{msg}</h3> |
| | """ |
| |
|
| |
|
| | def motion_token_to_string(motion_token, lengths, codebook_size=512): |
| | motion_string = [] |
| | for i in range(motion_token.shape[0]): |
| | motion_i = motion_token[i].cpu( |
| | ) if motion_token.device.type == 'cuda' else motion_token[i] |
| | motion_list = motion_i.tolist()[:lengths[i]] |
| | motion_string.append( |
| | (f'<motion_id_{codebook_size}>' + |
| | ''.join([f'<motion_id_{int(i)}>' for i in motion_list]) + |
| | f'<motion_id_{codebook_size + 1}>')) |
| | return motion_string |
| |
|
| |
|
| | def render_motion(data, feats, method='fast'): |
| | fname = time.strftime("%Y-%m-%d-%H_%M_%S", time.localtime( |
| | time.time())) + str(np.random.randint(10000, 99999)) |
| | video_fname = fname + '.mp4' |
| | feats_fname = f"{fname}_feats" + '.npy' |
| | data_fname = f"{fname}_joints" + '.npy' |
| | output_npy_path = os.path.join(output_dir, feats_fname) |
| | output_joints_path = os.path.join(output_dir, data_fname) |
| | output_mp4_path = os.path.join(output_dir, video_fname) |
| | np.save(output_npy_path, feats) |
| | np.save(output_joints_path, data) |
| | |
| | render_cmd = ["python", "-m", "render", "--joints_path", output_joints_path, "--method", method, "--output_mp4_path", output_mp4_path, "--smpl_model_path", cfg.RENDER.SMPL_MODEL_PATH] |
| | os.system(" ".join(render_cmd)) |
| | |
| | |
| | return output_mp4_path, video_fname, output_npy_path, feats_fname |
| |
|
| |
|
| | def load_motion(motion_uploaded, method): |
| | file = motion_uploaded['file'] |
| |
|
| | feats = torch.tensor(np.load(file), device=model.device) |
| | if len(feats.shape) == 2: |
| | feats = feats[None] |
| | |
| |
|
| | |
| | motion_lengths = feats.shape[0] |
| | motion_token, _ = model.vae.encode(feats) |
| |
|
| | motion_token_string = model.lm.motion_token_to_string( |
| | motion_token, [motion_token.shape[1]])[0] |
| | motion_token_length = motion_token.shape[1] |
| |
|
| | |
| | joints = model.datamodule.feats2joints(feats.cpu()).cpu().numpy() |
| | output_mp4_path, video_fname, output_npy_path, joints_fname = render_motion( |
| | joints, |
| | feats.to('cpu').numpy(), method) |
| |
|
| | motion_uploaded.update({ |
| | "feats": feats, |
| | "joints": joints, |
| | "motion_video": output_mp4_path, |
| | "motion_video_fname": video_fname, |
| | "motion_joints": output_npy_path, |
| | "motion_joints_fname": joints_fname, |
| | "motion_lengths": motion_lengths, |
| | "motion_token": motion_token, |
| | "motion_token_string": motion_token_string, |
| | "motion_token_length": motion_token_length, |
| | }) |
| |
|
| | return motion_uploaded |
| |
|
| |
|
| | def add_text(history, text, motion_uploaded, data_stored, method): |
| | data_stored = data_stored + [{'user_input': text}] |
| |
|
| | text = f"""<h3>{text}</h3>""" |
| | history = history + [(text, None)] |
| | if 'file' in motion_uploaded.keys(): |
| | motion_uploaded = load_motion(motion_uploaded, method) |
| | output_mp4_path = motion_uploaded['motion_video'] |
| | video_fname = motion_uploaded['motion_video_fname'] |
| | output_npy_path = motion_uploaded['motion_joints'] |
| | joints_fname = motion_uploaded['motion_joints_fname'] |
| | history = history + [(Video_Components.format( |
| | video_path=output_mp4_path, |
| | video_fname=video_fname, |
| | motion_path=output_npy_path, |
| | motion_fname=joints_fname), None)] |
| |
|
| | return history, gr.update(value="", |
| | interactive=False), motion_uploaded, data_stored |
| |
|
| |
|
| | def add_audio(history, audio_path, data_stored, language='en'): |
| | audio, sampling_rate = librosa.load(audio_path, sr=16000) |
| | input_features = audio_processor( |
| | audio, sampling_rate, return_tensors="pt" |
| | ).input_features |
| | input_features = torch.Tensor(input_features).to(device) |
| |
|
| | if language == 'English': |
| | forced_decoder_ids = forced_decoder_ids_en |
| | else: |
| | forced_decoder_ids = forced_decoder_ids_zh |
| | predicted_ids = audio_model.generate(input_features, |
| | forced_decoder_ids=forced_decoder_ids) |
| | text_input = audio_processor.batch_decode(predicted_ids, |
| | skip_special_tokens=True) |
| | text_input = str(text_input).strip('[]"') |
| | data_stored = data_stored + [{'user_input': text_input}] |
| | gr.update(value=data_stored, interactive=False) |
| | history = history + [(text_input, None)] |
| |
|
| | return history, data_stored |
| |
|
| |
|
| | def add_file(history, file, txt, motion_uploaded): |
| | motion_uploaded['file'] = file.name |
| | txt = txt.replace(" <Motion_Placeholder>", "") + " <Motion_Placeholder>" |
| | return history, gr.update(value=txt, interactive=True), motion_uploaded |
| |
|
| |
|
| | def bot(history, motion_uploaded, data_stored, method): |
| |
|
| | motion_length, motion_token_string = motion_uploaded[ |
| | "motion_lengths"], motion_uploaded["motion_token_string"] |
| |
|
| | input = data_stored[-1]['user_input'] |
| | prompt = model.lm.placeholder_fulfill(input, motion_length, |
| | motion_token_string, "") |
| | data_stored[-1]['model_input'] = prompt |
| | batch = { |
| | "length": [motion_length], |
| | "text": [prompt], |
| | } |
| |
|
| | outputs = model(batch, task="t2m") |
| | out_feats = outputs["feats"][0] |
| | out_lengths = outputs["length"][0] |
| | out_joints = outputs["joints"][:out_lengths].detach().cpu().numpy() |
| | out_texts = outputs["texts"][0] |
| | output_mp4_path, video_fname, output_npy_path, joints_fname = render_motion( |
| | out_joints, |
| | out_feats.to('cpu').numpy(), method) |
| |
|
| | motion_uploaded = { |
| | "feats": None, |
| | "joints": None, |
| | "motion_video": None, |
| | "motion_lengths": 0, |
| | "motion_token": None, |
| | "motion_token_string": '', |
| | "motion_token_length": 0, |
| | } |
| |
|
| | data_stored[-1]['model_output'] = { |
| | "feats": out_feats, |
| | "joints": out_joints, |
| | "length": out_lengths, |
| | "texts": out_texts, |
| | "motion_video": output_mp4_path, |
| | "motion_video_fname": video_fname, |
| | "motion_joints": output_npy_path, |
| | "motion_joints_fname": joints_fname, |
| | } |
| |
|
| | if '<Motion_Placeholder>' == out_texts: |
| | response = [ |
| | Video_Components.format(video_path=output_mp4_path, |
| | video_fname=video_fname, |
| | motion_path=output_npy_path, |
| | motion_fname=joints_fname) |
| | ] |
| | elif '<Motion_Placeholder>' in out_texts: |
| | response = [ |
| | Text_Components.format( |
| | msg=out_texts.split("<Motion_Placeholder>")[0]), |
| | Video_Components.format(video_path=output_mp4_path, |
| | video_fname=video_fname, |
| | motion_path=output_npy_path, |
| | motion_fname=joints_fname), |
| | Text_Components.format( |
| | msg=out_texts.split("<Motion_Placeholder>")[1]), |
| | ] |
| | else: |
| | response = f"""<h3>{out_texts}</h3>""" |
| |
|
| | history[-1][1] = "" |
| | for character in response: |
| | history[-1][1] += character |
| | time.sleep(0.02) |
| | yield history, motion_uploaded, data_stored |
| |
|
| |
|
| | def bot_example(history, responses): |
| | history = history + responses |
| | return history |
| |
|
| |
|
| | with open("assets/css/custom.css", "r", encoding="utf-8") as f: |
| | customCSS = f.read() |
| |
|
| | with gr.Blocks(css=customCSS) as demo: |
| |
|
| | |
| | chat_instruct = gr.State([ |
| | (None, |
| | "👋 Hi, I'm MotionGPT! I can generate realistic human motion from text, or generate text from motion." |
| | ), |
| | (None, |
| | "💡 You can chat with me in pure text like generating human motion following your descriptions." |
| | ), |
| | (None, |
| | "💡 After generation, you can click the button in the top right of generation human motion result to download the human motion video or feature stored in .npy format." |
| | ), |
| | (None, |
| | "💡 With the human motion feature file downloaded or got from dataset, you are able to ask me to translate it!" |
| | ), |
| | (None, |
| | "💡 Of courser, you can also purely chat with me and let me give you human motion in text, here are some examples!" |
| | ), |
| | (None, |
| | "💡 We provide two motion visulization methods. The default fast method is skeleton line ploting which is like the examples below:" |
| | ), |
| | (None, |
| | Video_Components_example.format( |
| | video_path="assets/videos/example0_fast.mp4", |
| | video_fname="example0_fast.mp4")), |
| | (None, |
| | "💡 And the slow method is SMPL model rendering which is more realistic but slower." |
| | ), |
| | (None, |
| | Video_Components_example.format( |
| | video_path="assets/videos/example0.mp4", |
| | video_fname="example0.mp4")), |
| | (None, |
| | "💡 If you want to get the video in our paper and website like below, you can refer to the scirpt in our [github repo](https://github.com/OpenMotionLab/MotionGPT#-visualization)." |
| | ), |
| | (None, |
| | Video_Components_example.format( |
| | video_path="assets/videos/example0_blender.mp4", |
| | video_fname="example0_blender.mp4")), |
| | (None, "👉 Follow the examples and try yourself!"), |
| | ]) |
| | chat_instruct_sum = gr.State([(None, ''' |
| | 👋 Hi, I'm MotionGPT! I can generate realistic human motion from text, or generate text from motion. |
| | |
| | 1. You can chat with me in pure text like generating human motion following your descriptions. |
| | 2. After generation, you can click the button in the top right of generation human motion result to download the human motion video or feature stored in .npy format. |
| | 3. With the human motion feature file downloaded or got from dataset, you are able to ask me to translate it! |
| | 4. Of course, you can also purely chat with me and let me give you human motion in text, here are some examples! |
| | ''')] + chat_instruct.value[-7:]) |
| |
|
| | t2m_examples = gr.State([ |
| | (None, |
| | "💡 You can chat with me in pure text, following are some examples of text-to-motion generation!" |
| | ), |
| | ("A person is walking forwards, but stumbles and steps back, then carries on forward.", |
| | Video_Components_example.format( |
| | video_path="assets/videos/example0.mp4", |
| | video_fname="example0.mp4")), |
| | ("Generate a man aggressively kicks an object to the left using his right foot.", |
| | Video_Components_example.format( |
| | video_path="assets/videos/example1.mp4", |
| | video_fname="example1.mp4")), |
| | ("Generate a person lowers their arms, gets onto all fours, and crawls.", |
| | Video_Components_example.format( |
| | video_path="assets/videos/example2.mp4", |
| | video_fname="example2.mp4")), |
| | ("Show me the video of a person bends over and picks things up with both hands individually, then walks forward.", |
| | Video_Components_example.format( |
| | video_path="assets/videos/example3.mp4", |
| | video_fname="example3.mp4")), |
| | ("Imagine a person is practing balancing on one leg.", |
| | Video_Components_example.format( |
| | video_path="assets/videos/example5.mp4", |
| | video_fname="example5.mp4")), |
| | ("Show me a person walks forward, stops, turns directly to their right, then walks forward again.", |
| | Video_Components_example.format( |
| | video_path="assets/videos/example6.mp4", |
| | video_fname="example6.mp4")), |
| | ("I saw a person sits on the ledge of something then gets off and walks away.", |
| | Video_Components_example.format( |
| | video_path="assets/videos/example7.mp4", |
| | video_fname="example7.mp4")), |
| | ("Show me a person is crouched down and walking around sneakily.", |
| | Video_Components_example.format( |
| | video_path="assets/videos/example8.mp4", |
| | video_fname="example8.mp4")), |
| | ]) |
| |
|
| | m2t_examples = gr.State([ |
| | (None, |
| | "💡 With the human motion feature file downloaded or got from dataset, you are able to ask me to translate it, here are some examples!" |
| | ), |
| | ("Please explain the movement shown in <Motion_Placeholder> using natural language.", |
| | None), |
| | (Video_Components_example.format( |
| | video_path="assets/videos/example0.mp4", |
| | video_fname="example0.mp4"), |
| | "The person was pushed but didn't fall down"), |
| | ("What kind of action is being represented in <Motion_Placeholder>? Explain it in text.", |
| | None), |
| | (Video_Components_example.format( |
| | video_path="assets/videos/example4.mp4", |
| | video_fname="example4.mp4"), |
| | "The figure has its hands curled at jaw level, steps onto its left foot and raises right leg with bent knee to kick forward and return to starting stance." |
| | ), |
| | ("Provide a summary of the motion demonstrated in <Motion_Placeholder> using words.", |
| | None), |
| | (Video_Components_example.format( |
| | video_path="assets/videos/example2.mp4", |
| | video_fname="example2.mp4"), |
| | "A person who is standing with his arms up and away from his sides bends over, gets down on his hands and then his knees and crawls forward." |
| | ), |
| | ("Generate text for <Motion_Placeholder>:", None), |
| | (Video_Components_example.format( |
| | video_path="assets/videos/example5.mp4", |
| | video_fname="example5.mp4"), |
| | "The man tries to stand in a yoga tree pose and looses his balance."), |
| | ("Provide a summary of the motion depicted in <Motion_Placeholder> using language.", |
| | None), |
| | (Video_Components_example.format( |
| | video_path="assets/videos/example6.mp4", |
| | video_fname="example6.mp4"), |
| | "Person walks up some steps then leeps to the other side and goes up a few more steps and jumps dow" |
| | ), |
| | ("Describe the motion represented by <Motion_Placeholder> in plain English.", |
| | None), |
| | (Video_Components_example.format( |
| | video_path="assets/videos/example7.mp4", |
| | video_fname="example7.mp4"), |
| | "Person sits down, then stands up and walks forward. then the turns around 180 degrees and walks the opposite direction" |
| | ), |
| | ("Provide a description of the action in <Motion_Placeholder> using words.", |
| | None), |
| | (Video_Components_example.format( |
| | video_path="assets/videos/example8.mp4", |
| | video_fname="example8.mp4"), |
| | "This man is bent forward and walks slowly around."), |
| | ]) |
| |
|
| | t2t_examples = gr.State([ |
| | (None, |
| | "💡 Of course, you can also purely chat with me and let me give you human motion in text, here are some examples!" |
| | ), |
| | ('Depict a motion as like you have seen it.', |
| | "A person slowly walked forward in rigth direction while making the circle" |
| | ), |
| | ('Random say something about describing a human motion.', |
| | "A man throws punches using his right hand."), |
| | ('Describe the motion of someone as you will.', |
| | "Person is moving left to right in a dancing stance swaying hips, moving feet left to right with arms held out" |
| | ), |
| | ('Come up with a human motion caption.', |
| | "A person is walking in a counter counterclockwise motion."), |
| | ('Write a sentence about how someone might dance.', |
| | "A person with his hands down by his sides reaches down for something with his right hand, uses the object to make a stirring motion, then places the item back down." |
| | ), |
| | ('Depict a motion as like you have seen it.', |
| | "A person is walking forward a few feet, then turns around, walks back, and continues walking." |
| | ) |
| | ]) |
| |
|
| | Init_chatbot = chat_instruct.value[: |
| | 1] + t2m_examples.value[: |
| | 3] + m2t_examples.value[:3] + t2t_examples.value[:2] + chat_instruct.value[ |
| | -7:] |
| |
|
| | |
| | motion_uploaded = gr.State({ |
| | "feats": None, |
| | "joints": None, |
| | "motion_video": None, |
| | "motion_lengths": 0, |
| | "motion_token": None, |
| | "motion_token_string": '', |
| | "motion_token_length": 0, |
| | }) |
| | data_stored = gr.State([]) |
| |
|
| | gr.Markdown(''' |
| | # MotionGPT: Human Motion as a Foreign Language |
| | |
| | <div> |
| | <a style="display:inline-block" href="https://motion-gpt.github.io/"><img src='https://img.shields.io/badge/Project_page-9375A7'></a> |
| | <a style="display:inline-block; margin-left: .5em" href="https://arxiv.org/abs/2306.14795"><img src="https://img.shields.io/badge/2306.14795-f9f7f7?logo=data:image/png;base64,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"></a> |
| | <a style="display:inline-block; margin-left: .5em" href='https://github.com/OpenMotionLab/MotionGPT'><img src='https://img.shields.io/github/stars/OpenMotionLab/MotionGPT?style=social'/></a> |
| | <a style="display:inline-block; margin-left: .5em" href="https://github.com/OpenMotionLab/MotionGPT#-citation"><img src="https://img.shields.io/badge/Citation-4385FE?&logo=google-scholar&logoColor=white" alt="Citation"> </a> |
| | </div> |
| | |
| | ''') |
| |
|
| | chatbot = gr.Chatbot(Init_chatbot, |
| | elem_id="mGPT", |
| | height=600, |
| | label="MotionGPT", |
| | avatar_images=(None, |
| | ("assets/images/avatar_bot.jpg")), |
| | bubble_full_width=False) |
| |
|
| | with gr.Row(): |
| | with gr.Column(scale=0.85): |
| | with gr.Row(): |
| | txt = gr.Textbox( |
| | label="Text", |
| | show_label=False, |
| | elem_id="textbox", |
| | placeholder= |
| | "Enter text and press ENTER or speak to input. You can also upload motion.", |
| | container=False) |
| |
|
| | with gr.Row(): |
| | aud = gr.Audio(sources=["microphone"], |
| | label="Speak input", |
| | type='filepath') |
| | btn = gr.UploadButton("📁 Upload motion", |
| | elem_id="upload", |
| | file_types=["file"]) |
| | |
| | clear = gr.ClearButton([txt, chatbot, aud], value='🗑️ Clear') |
| |
|
| | with gr.Row(): |
| | gr.Markdown(''' |
| | ### You can get more examples (pre-generated for faster response) by clicking the buttons below: |
| | ''') |
| |
|
| | with gr.Row(): |
| | instruct_eg = gr.Button("Instructions", elem_id="instruct") |
| | t2m_eg = gr.Button("Text-to-Motion", elem_id="t2m") |
| | m2t_eg = gr.Button("Motion-to-Text", elem_id="m2t") |
| | t2t_eg = gr.Button("Random description", elem_id="t2t") |
| |
|
| | with gr.Column(scale=0.15, min_width=150): |
| | method = gr.Dropdown(["slow", "fast"], |
| | label="Visulization method", |
| | interactive=True, |
| | elem_id="method", |
| | value="slow") |
| |
|
| | language = gr.Dropdown(["English", "中文"], |
| | label="Speech language", |
| | interactive=True, |
| | elem_id="language", |
| | value="English") |
| |
|
| | txt_msg = txt.submit( |
| | add_text, [chatbot, txt, motion_uploaded, data_stored, method], |
| | [chatbot, txt, motion_uploaded, data_stored], |
| | queue=False).then(bot, [chatbot, motion_uploaded, data_stored, method], |
| | [chatbot, motion_uploaded, data_stored]) |
| |
|
| | txt_msg.then(lambda: gr.update(interactive=True), None, [txt], queue=False) |
| |
|
| | file_msg = btn.upload(add_file, [chatbot, btn, txt, motion_uploaded], |
| | [chatbot, txt, motion_uploaded], |
| | queue=False) |
| | aud_msg = aud.stop_recording( |
| | add_audio, [chatbot, aud, data_stored, language], |
| | [chatbot, data_stored], |
| | queue=False).then(bot, [chatbot, motion_uploaded, data_stored, method], |
| | [chatbot, motion_uploaded, data_stored]) |
| | |
| | |
| | |
| | |
| |
|
| | instruct_msg = instruct_eg.click(bot_example, [chatbot, chat_instruct_sum], |
| | [chatbot], |
| | queue=False) |
| | t2m_eg_msg = t2m_eg.click(bot_example, [chatbot, t2m_examples], [chatbot], |
| | queue=False) |
| | m2t_eg_msg = m2t_eg.click(bot_example, [chatbot, m2t_examples], [chatbot], |
| | queue=False) |
| | t2t_eg_msg = t2t_eg.click(bot_example, [chatbot, t2t_examples], [chatbot], |
| | queue=False) |
| |
|
| | chatbot.change(scroll_to_output=True) |
| |
|
| | if __name__ == "__main__": |
| | demo.launch(debug=True, allowed_paths=["."]) |
| |
|
