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import gradio as gr
from transformers import DebertaV2Tokenizer, DebertaV2ForTokenClassification
import torch
from globe import title, description, joinus, model_name, placeholder, modelinfor1, modelinfor2, modelinfor3, id2label
tokenizer = DebertaV2Tokenizer.from_pretrained(model_name)
model = DebertaV2ForTokenClassification.from_pretrained(model_name)
color_map = {
"author": "blue", "bibliography": "purple", "caption": "orange",
"contact": "cyan", "date": "green", "dialog": "yellow",
"footnote": "pink", "keywords": "lightblue", "math": "red",
"paratext": "lightgreen", "separator": "gray", "table": "brown",
"text": "lightgray", "title": "gold"
}
def segment_text(input_text):
tokens = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True)
with torch.no_grad():
outputs = model(**tokens)
logits = outputs.logits
predictions = torch.argmax(logits, dim=-1).squeeze().tolist()
tokens_decoded = tokenizer.convert_ids_to_tokens(tokens['input_ids'].squeeze())
segments = []
current_word = ""
for token, label_id in zip(tokens_decoded, predictions):
if token.startswith("▁"): # handle wordpieces
if current_word:
segments.append((current_word, id2label[str(label_id)]))
current_word = token.replace("▁", "") # start a new word
else:
current_word += token # append subword part to current word
if current_word:
segments.append((current_word, id2label[str(label_id)]))
return segments
with gr.Blocks(theme=gr.themes.Base()) as demo:
with gr.Row():
gr.Markdown(title)
with gr.Row():
with gr.Group():
gr.Markdown(description)
with gr.Accordion(label="PLeIAs/✂️📜 Segment Text Model Information ", open=False):
with gr.Row():
with gr.Group():
gr.Markdown(modelinfor1)
with gr.Group():
gr.Markdown(modelinfor2)
with gr.Group():
gr.Markdown(modelinfor3)
with gr.Accordion(label="Join Us", open=False):
gr.Markdown(joinus)
with gr.Row():
input_text = gr.Textbox(label="Enter your text here👇🏻", lines=5, placeholder=placeholder)
output_text = gr.HighlightedText(label=" PLeIAs/✂️📜 Segment Text", color_map=color_map, combine_adjacent=True, show_inline_category=True, show_legend=True)
def process(input_text):
return segment_text(input_text)
submit_button = gr.Button("Segment Text")
submit_button.click(fn=process, inputs=input_text, outputs=output_text)
if __name__ == "__main__":
demo.launch()
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