Update nlp.py

nlp.py

@@ -21,7 +21,11 @@ from constants import StreamlitException
 from constants import API_URL_summary, API_URL_name, HEADERS, TECH_SKILLS
 from constants import SENTENCE_TRANSFORMER_MODEL, LLM_REPO_ID
 
+from streamlit import cache_data
+
+
 # Function to summarize resume text
+@cache_data(show_spinner=False)
 def summarize_text(text, max_length=100):
     if text != '':
         data = json.dumps(
@@ -42,6 +46,7 @@ def summarize_text(text, max_length=100):
         return 'nan'
     
 # Function to extract candidate name(s) from resume text
+@cache_data(show_spinner=False)
 def extract_person_names_and_email(text):
     print(text)
     emails = re.findall(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b', text)
@@ -58,11 +63,12 @@ def extract_person_names_and_email(text):
     return set(person_names), set(emails)
 
 # Function to extract key technical skills from resume text
-def extract_tech_skills(doc):
-    keywords = [token.text.upper() for token in doc if token.text.lower() in TECH_SKILLS]
+def extract_tech_skills(_doc):
+    keywords = [token.text.upper() for token in _doc if token.text.lower() in TECH_SKILLS]
     return set(keywords)
 
 # Function to calculate overall percentage match between job description and resume
+@cache_data(show_spinner=False)
 def calculate_similarity(job_description, resume):
     if job_description != '':
         model = SentenceTransformer(SENTENCE_TRANSFORMER_MODEL)
@@ -104,6 +110,7 @@ def split_text(string):
     return sentences
 
 # Function to calculate overall percentage match
+@cache_data(show_spinner=False)
 def get_average_similarity_scores(job_description, resumes):
     # Calculate cosine similarity matrix between job description and resumes
     model = SentenceTransformer(SENTENCE_TRANSFORMER_MODEL)