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| import os | |
| os.environ["PYOPENGL_PLATFORM"] = "egl" | |
| os.environ["MESA_GL_VERSION_OVERRIDE"] = "4.1" | |
| os.system('pip install /home/user/app/pyrender') | |
| os.system('pip install eventlet') | |
| import gradio as gr | |
| import random | |
| import torch | |
| import time | |
| import cv2 | |
| import numpy as np | |
| import OpenGL.GL as gl | |
| import imageio | |
| import pytorch_lightning as pl | |
| import moviepy.editor as mp | |
| 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 scipy.spatial.transform import Rotation as RRR | |
| import mGPT.render.matplot.plot_3d_global as plot_3d | |
| from mGPT.render.pyrender.hybrik_loc2rot import HybrIKJointsToRotmat | |
| from mGPT.render.pyrender.smpl_render import SMPLRender | |
| from transformers import WhisperProcessor, WhisperForConditionalGeneration | |
| import librosa | |
| from huggingface_hub import snapshot_download | |
| import eventlet | |
| # Load model | |
| cfg = parse_args(phase="webui") # parse config file | |
| 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") | |
| # HTML Style | |
| 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 = fname + '.npy' | |
| output_npy_path = os.path.join(output_dir, feats_fname) | |
| output_mp4_path = os.path.join(output_dir, video_fname) | |
| np.save(output_npy_path, feats) | |
| if method == 'slow': | |
| if len(data.shape) == 4: | |
| data = data[0] | |
| data = data - data[0, 0] | |
| pose_generator = HybrIKJointsToRotmat() | |
| pose = pose_generator(data) | |
| pose = np.concatenate([ | |
| pose, | |
| np.stack([np.stack([np.eye(3)] * pose.shape[0], 0)] * 2, 1) | |
| ], 1) | |
| shape = [768, 768] | |
| render = SMPLRender(cfg.RENDER.SMPL_MODEL_PATH) | |
| r = RRR.from_rotvec(np.array([np.pi, 0.0, 0.0])) | |
| pose[:, 0] = np.matmul(r.as_matrix().reshape(1, 3, 3), pose[:, 0]) | |
| vid = [] | |
| aroot = data[[0], 0] | |
| aroot[:, 1] = -aroot[:, 1] | |
| params = dict(pred_shape=np.zeros([1, 10]), | |
| pred_root=aroot, | |
| pred_pose=pose) | |
| render.init_renderer([shape[0], shape[1], 3], params) | |
| for i in range(data.shape[0]): | |
| renderImg = render.render(i) | |
| vid.append(renderImg) | |
| out = np.stack(vid, axis=0) | |
| output_gif_path = output_mp4_path[:-4] + '.gif' | |
| imageio.mimwrite(output_gif_path, out, duration=50) | |
| out_video = mp.VideoFileClip(output_gif_path) | |
| out_video.write_videofile(output_mp4_path) | |
| del out, render | |
| elif method == 'fast': | |
| output_gif_path = output_mp4_path[:-4] + '.gif' | |
| if len(data.shape) == 3: | |
| data = data[None] | |
| if isinstance(data, torch.Tensor): | |
| data = data.cpu().numpy() | |
| pose_vis = plot_3d.draw_to_batch(data, [''], [output_gif_path]) | |
| out_video = mp.VideoFileClip(output_gif_path) | |
| out_video.write_videofile(output_mp4_path) | |
| del pose_vis | |
| 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] | |
| # feats = model.datamodule.normalize(feats) | |
| # Motion tokens | |
| 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] | |
| # Motion rendered | |
| 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 # whisper training sampling rate, do not modify | |
| 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: | |
| # Examples | |
| chat_instruct = gr.State([ | |
| (None, | |
| "π Hi, I'm MotionGPT! I can generate realistic human motion from text, or generate text from motion." | |
| ), | |
| (None, | |
| "π‘ You can chat with me in pure text like generating human motion following your descriptions." | |
| ), | |
| (None, | |
| "π‘ 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, | |
| "π‘ With the human motion feature file downloaded or got from dataset, you are able to ask me to translate it!" | |
| ), | |
| (None, | |
| "π‘ Of courser, you can also purely chat with me and let me give you human motion in text, here are some examples!" | |
| ), | |
| (None, | |
| "π‘ 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, | |
| "π‘ 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, | |
| "π‘ 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, "π Follow the examples and try yourself!"), | |
| ]) | |
| t2m_examples = gr.State([ | |
| (None, | |
| "π‘ 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, | |
| "π‘ 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, | |
| "π‘ 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:] | |
| # Variables | |
| 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") | |
| chatbot = gr.Chatbot(Init_chatbot, | |
| elem_id="mGPT", | |
| height=600, | |
| label="MotionGPT", | |
| avatar_images=(("assets/images/avatar_user.png"), | |
| ("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(source="microphone", | |
| label="Speak input", | |
| type='filepath') | |
| btn = gr.UploadButton("π Upload motion", | |
| elem_id="upload", | |
| file_types=["file"]) | |
| regen = gr.Button("π Regenerate", elem_id="regen") | |
| clear = gr.ClearButton([txt, chatbot, aud], value='ποΈ 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", "δΈζ"], | |
| 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]) | |
| regen_msg = regen.click(bot, | |
| [chatbot, motion_uploaded, data_stored, method], | |
| [chatbot, motion_uploaded, data_stored], | |
| queue=False) | |
| instruct_msg = instruct_eg.click(bot_example, [chatbot, chat_instruct], | |
| [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) | |