import subprocess import sys def install(package): subprocess.check_call([sys.executable, "-m", "pip", "install", package], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) install("keybert") install("keyphrase_vectorizers") import warnings from keybert import KeyBERT from keyphrase_vectorizers import KeyphraseCountVectorizer import gradio as gr with warnings.catch_warnings(): warnings.simplefilter("ignore", category=UserWarning) embedding = 'all-mpnet-base-v2' key_model = KeyBERT(model=embedding) vectorizer_params = KeyphraseCountVectorizer(spacy_pipeline='en_core_web_sm', pos_pattern='*+', stop_words='english', lowercase=True) def get_keywords(course_name, course_desc): keywords_list = [] course_name, course_desc = course_name.strip().lower(), course_desc.strip().lower() data = course_name+". "+course_desc keywords = key_model.extract_keywords(data, vectorizer=vectorizer_params, stop_words='english', top_n=7, use_mmr=True) keywords_list = list(dict(keywords).keys()) return ", ".join(keywords_list) iface = gr.Interface(fn=get_keywords, inputs=[gr.Textbox(label="Course Name"), gr.Textbox(label="Course Description")], outputs=gr.Textbox(label="Relevant Tags"), title="College Course Tags Generator", description="Generating tags/keywords based on Keyphrase-BERT Extraction'") iface.launch()