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| from utca.core import RenameAttribute | |
| from utca.implementation.predictors import TokenSearcherPredictor, TokenSearcherPredictorConfig | |
| from utca.implementation.tasks import TokenSearcherNER, TokenSearcherNERPostprocessor, TokenSearcherRelationExtraction, TokenSearcherRelationExtractionPostprocessor | |
| from typing import Dict, Union | |
| import gradio as gr | |
| text = """ | |
| Dr. Paul Hammond, a renowned neurologist at Johns Hopkins University, has recently published a paper in the prestigious journal \"Nature Neuroscience\". | |
| His research focuses on a rare genetic mutation, found in less than 0.01% of the population, that appears to prevent the development of Alzheimer's disease. | |
| Collaborating with researchers at the University of California, San Francisco, the team is now working to understand the mechanism by which this mutation confers its protective effect. | |
| Funded by the National Institutes of Health, their research could potentially open new avenues for Alzheimer's treatment. | |
| """ | |
| predictor = TokenSearcherPredictor( | |
| TokenSearcherPredictorConfig( | |
| device="cpu", | |
| model="knowledgator/UTC-DeBERTa-large-v2" | |
| ) | |
| ) | |
| pipe = ( | |
| TokenSearcherNER( # TokenSearcherNER task produces classified entities that will be at the "output" key. | |
| predictor=predictor, | |
| postprocess=TokenSearcherNERPostprocessor( | |
| threshold=0.5 # Entity threshold | |
| ) | |
| ) | |
| | RenameAttribute("output", "entities") # Rename output entities from TokenSearcherNER task to use them as inputs in TokenSearcherRelationExtraction | |
| | TokenSearcherRelationExtraction( # TokenSearcherRelationExtraction is used for relation extraction. | |
| predictor=predictor, | |
| postprocess=TokenSearcherRelationExtractionPostprocessor( | |
| threshold=0.5 # Relation threshold | |
| ) | |
| ) | |
| ) | |
| def process( | |
| relation: str, text, distance_threshold: str, pairs_filter: str, labels: str | |
| ) -> Dict[str, Union[str, int, float]]: | |
| pairs_filter = [tuple(pair.strip() for pair in pair.split("->")) for pair in pairs_filter.split(",")] | |
| if len(distance_threshold) < 1 or not distance_threshold or not distance_threshold.strip().isdigit(): | |
| r = pipe.run({ | |
| "text": text, | |
| "labels": [label.strip() for label in labels.split(",")], | |
| "relations": [{ | |
| "relation": relation, | |
| "pairs_filter": pairs_filter | |
| }] | |
| }) | |
| elif int(distance_threshold.strip()): | |
| r = pipe.run({ | |
| "text": text, | |
| "labels": [label.strip() for label in labels.split(",")], | |
| "relations": [{ | |
| "relation": relation, | |
| "pairs_filter": pairs_filter, | |
| "distance_threshold": int(distance_threshold.replace(" ", "")) | |
| }] | |
| }) | |
| return r["output"] | |
| relation_e_examples = [ | |
| [ | |
| "worked at", | |
| text, | |
| "None", | |
| "scientist -> university, scientist -> other", | |
| "scientist, university, city, research, journal"] | |
| ] | |
| with gr.Blocks(title="Open Information Extracting") as relation_e_interface: | |
| relation = gr.Textbox(label="Relation", placeholder="Enter relation you want to extract here") | |
| input_text = gr.Textbox(label="Text input", placeholder="Enter your text here") | |
| labels = gr.Textbox(label="Labels", placeholder="Enter your labels here (comma separated)", scale=2) | |
| pairs_filter = gr.Textbox(label="Pairs Filter", placeholder="It specifies possible members of relations by their entity labels. Write as: source -> target,..") | |
| distance_threshold = gr.Textbox(label="Distance Threshold", placeholder="It specifies the max distance in characters between spans in the text") | |
| output = gr.Textbox(label="Predicted Relation") | |
| submit_btn = gr.Button("Submit") | |
| examples = gr.Examples( | |
| relation_e_examples, | |
| fn=process, | |
| inputs=[relation, input_text, distance_threshold, pairs_filter, labels], | |
| outputs=output, | |
| cache_examples=True | |
| ) | |
| theme=gr.themes.Base() | |
| input_text.submit(fn=process, inputs=[relation, input_text, distance_threshold, pairs_filter, labels], outputs=output) | |
| labels.submit(fn=process, inputs=[relation, input_text, distance_threshold, pairs_filter, labels], outputs=output) | |
| pairs_filter.submit(fn=process, inputs=[relation, input_text, distance_threshold, pairs_filter, labels], outputs=output) | |
| submit_btn.click(fn=process, inputs=[relation, input_text, distance_threshold, pairs_filter, labels], outputs=output) | |
| distance_threshold.submit(fn=process, inputs=[relation, input_text, distance_threshold, pairs_filter, labels], outputs=output) | |
| if __name__ == "__main__": | |
| relation_e_interface.launch() |