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import subprocess
subprocess.run(["pip", "uninstall", "gradio"])
subprocess.run(["pip", "install", "gliner"])
subprocess.run(["pip", "install", "gradio==4.31.5"])
import gradio as gr
from typing import Dict, Union
from gliner import GLiNER
import gradio as gr
model = GLiNER.from_pretrained("BioMike/logical-gliner-large").to("cpu")
qa_examples = [
[
"",
"For a student to graduate, they must complete all their required courses and pass the final exam. John has completed all his required courses but failed the final exam. Answer options: 1. John can graduate 2. John cannot graduate 3. John completed all his required courses 4. John passed the final exam",
0.5,
False
],
[
"",
"(P ∨ Q) → R, (R ∧ S) → T, ¬T, P. Answer options: 1. ¬R 2. ¬S 3. ¬Q 4. R 5. S 6. T",
0.5,
False
],
[
"",
"(A → B), (B → (C ∧ D)), ¬C, E → F, A, ¬F. Answer options: 1. ¬A 2. ¬B 3. ¬E 4. F",
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(
question:str, text, threshold: float, nested_ner: bool, labels: str = ["answer"]
) -> Dict[str, Union[str, int, float]]:
text = question + "\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="Question Answering Task") as qa_interface:
question = gr.Textbox(label="Question", placeholder="Enter your question 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")
examples = gr.Examples(
qa_examples,
fn=process,
inputs=[question, input_text, threshold, nested_ner],
outputs=output,
cache_examples=True
)
theme=gr.themes.Base()
input_text.submit(fn=process, inputs=[question, input_text, threshold, nested_ner], outputs=output)
question.submit(fn=process, inputs=[question, input_text, threshold, nested_ner], outputs=output)
threshold.release(fn=process, inputs=[question, input_text, threshold, nested_ner], outputs=output)
submit_btn.click(fn=process, inputs=[question, input_text, threshold, nested_ner], outputs=output)
nested_ner.change(fn=process, inputs=[question, input_text, threshold, nested_ner], outputs=output)
qa_interface.queue()
qa_interface.launch(debug=True, share=True) |