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| import subprocess | |
| subprocess.run(["pip", "uninstall", "gradio"]) | |
| subprocess.run(["pip", "install", "gradio"]) | |
| 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) |