|
|
|
|
|
import argparse |
|
|
|
import gradio as gr |
|
import platform |
|
|
|
|
|
def get_args(): |
|
parser = argparse.ArgumentParser() |
|
|
|
args = parser.parse_args() |
|
return args |
|
|
|
|
|
model_names = { |
|
"allennlp_text_classification": { |
|
"qgyd2021/language_identification": "https://huggingface.co/qgyd2021/language_identification" |
|
} |
|
} |
|
|
|
|
|
def click_button_allennlp_text_classification(text: str, model_name: str): |
|
print(text) |
|
print(model_name) |
|
return "label", 0.0 |
|
|
|
|
|
def main(): |
|
args = get_args() |
|
|
|
brief_description = """ |
|
## NLP Tools |
|
|
|
NLP Tools Demo |
|
""" |
|
|
|
|
|
with gr.Blocks() as blocks: |
|
gr.Markdown(value=brief_description) |
|
|
|
with gr.Tabs(): |
|
with gr.TabItem("AllenNLP Text Classification"): |
|
with gr.Row(): |
|
with gr.Column(scale=3): |
|
text = gr.Text(label="text") |
|
ground_true = gr.Text(label="ground_true") |
|
model_name = gr.Dropdown( |
|
choices=list(model_names["allennlp_text_classification"].keys()) |
|
) |
|
button = gr.Button("infer", variant="primary") |
|
|
|
with gr.Column(scale=3): |
|
label = gr.Text(label="label") |
|
prob = gr.Text(label="prob") |
|
|
|
gr.Examples( |
|
examples=[ |
|
["你好", "zh", "qgyd2021/language_identification"] |
|
], |
|
inputs=[text, ground_true, model_name], |
|
outputs=[label, prob], |
|
) |
|
button.click( |
|
click_button_allennlp_text_classification, |
|
inputs=[text, model_name], |
|
outputs=[label, prob] |
|
) |
|
|
|
blocks.queue().launch( |
|
share=False if platform.system() == "Windows" else False |
|
) |
|
return |
|
|
|
|
|
if __name__ == '__main__': |
|
main() |
|
|
