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| #The libraries used | |
| import gradio as gr | |
| import pandas as pd | |
| from transformers import pipeline | |
| #Implementing the Hugging Face NER model | |
| ner = pipeline('ner', model = 'FacebookAI/xlm-roberta-large-finetuned-conll03-english', grouped_entities = True) | |
| #a function to split each sentence containing an entity in the text by commas. | |
| #start to comma, comma to comma, last comma to the remaining text | |
| def split_sentences(text, start, end): | |
| #comma before entity | |
| start_comma = text.rfind(',', 0, start) | |
| if start_comma == -1: #if rfind did not find a comma before the entity: | |
| start_comma = 0 #start from the beginning (first sentence) | |
| else: | |
| start_comma += 1 #if comma found, then start from the char after the comma | |
| # comma after the entity | |
| end_comma = text.find(',', end) | |
| if end_comma == -1: | |
| return text[start_comma:].strip() #if it did not find a comma, return the text from the last comma to the end | |
| else: #if it did find a comma, go to that comma | |
| return text[start_comma:end_comma].strip() | |
| #Conveting the NER output into a DataFrame: | |
| def entities_to_df(text): | |
| all_entities = [] | |
| entities = ner(text)#the NER model will be used on the input text | |
| #putting the entities into a data frame with the needed keys + calling the split sentences fumction in the for loop | |
| for entity in entities: | |
| sentence = split_sentences(text, entity['start'], entity['end']) | |
| all_entities.append({ | |
| "Entity": entity['word'], | |
| "Type" : entity['entity_group'], #loc, org, per, misc | |
| "Score": float((entity['score'])), | |
| "Start": entity['start'], | |
| "End": entity['end'], | |
| "Sentence": sentence, | |
| }) | |
| df = pd.DataFrame(all_entities) | |
| #the df in the output did not round the score above so I rounded it after creating the df | |
| df['Score'] = df['Score'].round(4) | |
| return df | |
| #a function to highlight the entitties of the Dataframe using HTML | |
| def highlight_entities(text): | |
| df = entities_to_df(text) | |
| highlighted_text = "" | |
| last_idx = 0 | |
| # Iterating the DF rows in order | |
| for i, entity in df.iterrows(): #iterrows is a function in the df to iterate by rows | |
| # Add the text before the entity | |
| highlighted_text += text[last_idx:entity['Start']] | |
| #highlighting the entities in RED by using HTML div and css and thiers types(per, org,loc or misc) | |
| highlighted_text += f"<div style='background-color: red; display: inline;'>{entity['Entity']} ({entity['Type']})</div>" | |
| #updating the index after the current entity | |
| last_idx = entity['End'] | |
| # add the text after the last entity | |
| highlighted_text += text[last_idx:] | |
| # again we will use an HTML div block to make the output looks better :) | |
| return f"<div>{highlighted_text}</div>" | |
| # The last function which will combine the two previous functions and will be used in the interface | |
| def NER_output(text): | |
| html = highlight_entities(text) | |
| df = entities_to_df(text) | |
| return html,df | |
| #a defualt value that will be used in the gradio interface input | |
| default_value ="J.K. Rowling wrote the Harry Potter series, which was published by Bloomsbury Publishing." | |
| # Gradio Interface | |
| demo = gr.Interface( | |
| fn=NER_output, | |
| inputs=gr.Textbox(label="Enter text:", lines=6, value = default_value), | |
| outputs=[gr.HTML(label="Entities Highlighted"), gr.Dataframe(label="Entities in DataFrame format")], | |
| title = "NER model with highlighted entities" | |
| #above, we used the NER_output, and since that function return the html and the df there will be two outputs | |
| #The first is gr.HTML and the second gr.Datagrame | |
| ) | |
| demo.launch() |