Create app.py for deployment

app.py

@@ -0,0 +1,19 @@
+from keybert import KeyBERT
+from keyphrase_vectorizers import KeyphraseCountVectorizer
+import gradio as gr
+
+embedding = 'all-mpnet-base-v2'
+key_model = KeyBERT(model=embedding)
+vectorizer_params = KeyphraseCountVectorizer(spacy_pipeline='en_core_web_sm', pos_pattern='<J.*>*<N.*>+', 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()