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| #importing the necessary libraries | |
| import pandas as pd | |
| import numpy as np | |
| import gradio as gr | |
| from sentence_transformers import SentenceTransformer | |
| from keybert import KeyBERT | |
| from keyphrase_vectorizers import KeyphraseCountVectorizer | |
| # Defining a function to read in the text file | |
| def read_in_text(url): | |
| with open(url, 'r') as file: | |
| article = file.read() | |
| return article | |
| #tmp_model = SentenceTransformer('valurank/MiniLM-L6-Keyword-Extraction') | |
| kw_extractor = KeyBERT('valurank/MiniLM-L6-Keyword-Extraction') | |
| def get_keybert_results_with_vectorizer(text, number_of_results=20): | |
| try: | |
| keywords = kw_extractor.extract_keywords(text, vectorizer=KeyphraseCountVectorizer(), stop_words=None, top_n=number_of_results) | |
| keywords = [i for i in keywords if i[1] > 0.20] | |
| keybert_diversity_phrases = [] | |
| for i, j in keywords: | |
| keybert_diversity_phrases.append(i) | |
| output_df = pd.DataFrame() | |
| output_df['keyword'] = np.array(keybert_diversity_phrases) | |
| return output_df.head(20) | |
| except Exception: | |
| return "Error" | |
| demo = gr.Interface(get_keybert_results_with_vectorizer, inputs=gr.inputs.Textbox(), | |
| outputs=gr.outputs.Dataframe(), | |
| title = "Keyword Extraction") | |
| if __name__ == "__main__": | |
| demo.launch(debug=True) | |