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') 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()