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7e976d8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 | # SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
import argparse
import os
import gradio as gr
import numpy as np
from kimodo.model import resolve_target
from .gradio_theme import get_gradio_theme
os.environ["HF_ENABLE_PARALLEL_LOADING"] = "YES"
DEFAULT_TEXT = "A person walks and falls to the ground."
DEFAULT_SERVER_NAME = "0.0.0.0"
DEFAULT_SERVER_PORT = 9550
DEFAULT_TMP_FOLDER = "/tmp/text_encoder/"
DEFAULT_TEXT_ENCODER = "llm2vec"
TEXT_ENCODER_PRESETS = {
"llm2vec": {
"target": "kimodo.model.LLM2VecEncoder",
"kwargs": {
"base_model_name_or_path": "McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp",
"peft_model_name_or_path": "McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-supervised",
"dtype": "bfloat16",
"llm_dim": 4096,
"device": "auto",
},
"display_name": "LLM2Vec",
}
}
class DemoWrapper:
def __init__(self, text_encoder, tmp_folder):
self.text_encoder = text_encoder
self.tmp_folder = tmp_folder
def __call__(self, text, filename, progress=gr.Progress()):
# Compute text embedding
tensor, length = self.text_encoder(text)
embedding = tensor[:length]
embedding = embedding.cpu().numpy()
# Save text embedding
path = os.path.join(self.tmp_folder, filename)
np.save(path, embedding)
output_title = gr.Markdown(visible=True)
output_text = gr.Markdown(visible=True, value=f"Text: {text}")
download = gr.DownloadButton(visible=True, value=path)
return download, output_title, output_text
def _get_env(name: str, default):
return os.getenv(name, default)
def _build_text_encoder(name: str, fp32: bool = False):
if name not in TEXT_ENCODER_PRESETS:
available = ", ".join(sorted(TEXT_ENCODER_PRESETS))
raise ValueError(f"Unknown TEXT_ENCODER='{name}'. Available: {available}")
preset = TEXT_ENCODER_PRESETS[name]
target_cls = resolve_target(preset["target"])
if fp32:
preset["kwargs"]["dtype"] = "float32"
return target_cls(**preset["kwargs"])
def parse_args():
parser = argparse.ArgumentParser(description="Run text encoder Gradio server.")
parser.add_argument(
"--text-encoder",
default=_get_env("TEXT_ENCODER", DEFAULT_TEXT_ENCODER),
choices=sorted(TEXT_ENCODER_PRESETS.keys()),
help="Text encoder preset.",
)
parser.add_argument(
"--tmp-folder",
default=_get_env("TEXT_ENCODER_TMP_FOLDER", DEFAULT_TMP_FOLDER),
)
parser.add_argument(
"--fp32",
action="store_true",
help="Uses fp32 for the text encoder rather than default bfloat16.",
)
return parser.parse_args()
def main():
args = parse_args()
server_name = _get_env("GRADIO_SERVER_NAME", DEFAULT_SERVER_NAME)
server_port = int(_get_env("GRADIO_SERVER_PORT", DEFAULT_SERVER_PORT))
theme, css = get_gradio_theme()
os.makedirs(args.tmp_folder, exist_ok=True)
text_encoder = _build_text_encoder(args.text_encoder, args.fp32)
display_name = TEXT_ENCODER_PRESETS[args.text_encoder]["display_name"]
demo_wrapper_fn = DemoWrapper(text_encoder, args.tmp_folder)
with gr.Blocks(title="Text encoder", css=css, theme=theme) as demo:
gr.Markdown(f"# Text encoder: {display_name}")
gr.Markdown("## Description")
gr.Markdown("Get a embeddings from a text.")
gr.Markdown("## Inputs")
with gr.Row():
text = gr.Textbox(
placeholder="Type the motion you want to generate with a sentence",
show_label=True,
label="Text prompt",
value=DEFAULT_TEXT,
type="text",
)
with gr.Row(scale=3):
with gr.Column(scale=1):
btn = gr.Button("Encode", variant="primary")
with gr.Column(scale=1):
clear = gr.Button("Clear", variant="secondary")
with gr.Column(scale=3):
pass
output_title = gr.Markdown("## Outputs", visible=False)
output_text = gr.Markdown("", visible=False)
with gr.Row(scale=3):
with gr.Column(scale=1):
download = gr.DownloadButton("Download", variant="primary", visible=False)
with gr.Column(scale=4):
pass
filename = gr.Textbox(
visible=False,
value="embedding.npy",
)
def clear_fn():
return [
gr.DownloadButton(visible=False),
gr.Markdown(visible=False),
gr.Markdown(visible=False),
]
outputs = [download, output_title, output_text]
gr.on(
triggers=[text.submit, btn.click],
fn=clear_fn,
inputs=None,
outputs=outputs,
).then(
fn=demo_wrapper_fn,
inputs=[text, filename],
outputs=outputs,
)
def download_file():
return gr.DownloadButton()
download.click(
fn=download_file,
inputs=None,
outputs=[download],
)
clear.click(fn=clear_fn, inputs=None, outputs=outputs)
demo.launch(server_name=server_name, server_port=server_port)
if __name__ == "__main__":
main()
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