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from typing import Dict, Union
from gliner import GLiNER
import gradio as gr
model = GLiNER.from_pretrained("knowledgator/gliner-multitask-large-v0.5").to('cpu')
text1 = """
"I recently purchased the Sony WH-1000XM4 Wireless Noise-Canceling Headphones from Amazon and I must say, I'm thoroughly impressed. The package arrived in New York within 2 days, thanks to Amazon Prime's expedited shipping.
The headphones themselves are remarkable. The noise-canceling feature works like a charm in the bustling city environment, and the 30-hour battery life means I don't have to charge them every day. Connecting them to my Samsung Galaxy S21 was a breeze, and the sound quality is second to none.
I also appreciated the customer service from Amazon when I had a question about the warranty. They responded within an hour and provided all the information I needed.
However, the headphones did not come with a hard case, which was listed in the product description. I contacted Amazon, and they offered a 10% discount on my next purchase as an apology.
Overall, I'd give these headphones a 4.5/5 rating and highly recommend them to anyone looking for top-notch quality in both product and service."""
open_ie_examples = [
[
f"Extract all brands, please",
text1,
0.5,
False
]]
def merge_entities(entities):
if not entities:
return []
merged = []
current = entities[0]
for next_entity in entities[1:]:
if next_entity['entity'] == current['entity'] and (next_entity['start'] == current['end'] + 1 or next_entity['start'] == current['end']):
current['word'] += ' ' + next_entity['word']
current['end'] = next_entity['end']
else:
merged.append(current)
current = next_entity
merged.append(current)
return merged
def process(
prompt:str, text, threshold: float, nested_ner: bool, labels: str = ["match"]
) -> Dict[str, Union[str, int, float]]:
text = prompt + "\n" + text
r = {
"text": text,
"entities": [
{
"entity": entity["label"],
"word": entity["text"],
"start": entity["start"],
"end": entity["end"],
"score": 0,
}
for entity in model.predict_entities(
text, labels, flat_ner=not nested_ner, threshold=threshold
)
],
}
r["entities"] = merge_entities(r["entities"])
return r
with gr.Blocks(title="Open Information Extracting") as open_ie_interface:
prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here")
input_text = gr.Textbox(label="Text input", placeholder="Enter your text here")
threshold = gr.Slider(0, 1, value=0.3, step=0.01, label="Threshold", info="Lower the threshold to increase how many entities get predicted.")
nested_ner = gr.Checkbox(label="Nested NER", info="Allow for nested NER?")
output = gr.HighlightedText(label="Predicted Entities")
submit_btn = gr.Button("Submit")
theme=gr.themes.Base()
input_text.submit(fn=process, inputs=[prompt, input_text, threshold, nested_ner], outputs=output)
prompt.submit(fn=process, inputs=[prompt, input_text, threshold, nested_ner], outputs=output)
threshold.release(fn=process, inputs=[prompt, input_text, threshold, nested_ner], outputs=output)
submit_btn.click(fn=process, inputs=[prompt, input_text, threshold, nested_ner], outputs=output)
nested_ner.change(fn=process, inputs=[prompt, input_text, threshold, nested_ner], outputs=output)
if __name__ == "__main__":
open_ie_interface.launch() |