Update func.py

func.py

@@ -2,26 +2,17 @@ from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassifica
 from bs4 import BeautifulSoup
 import requests
 
-# ----------- Lazy Initialization of Pipelines -----------
-_sentiment_pipeline = None
-_ner_pipeline = None
+# ----------- Eager Initialization of Pipelines -----------
+# Sentiment pipeline
+model_id = "LinkLinkWu/ISOM5240HKUSTBASE"
+sentiment_tokenizer = AutoTokenizer.from_pretrained(model_id)
+sentiment_model = AutoModelForSequenceClassification.from_pretrained(model_id)
+sentiment_pipeline = pipeline("sentiment-analysis", model=sentiment_model, tokenizer=sentiment_tokenizer)
 
-def get_sentiment_pipeline():
-    global _sentiment_pipeline
-    if _sentiment_pipeline is None:
-        model_id = "LinkLinkWu/ISOM5240HKUSTBASE"
-        tokenizer = AutoTokenizer.from_pretrained(model_id)
-        model = AutoModelForSequenceClassification.from_pretrained(model_id)
-        _sentiment_pipeline = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
-    return _sentiment_pipeline
-
-def get_ner_pipeline():
-    global _ner_pipeline
-    if _ner_pipeline is None:
-        tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER")
-        model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER")
-        _ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer, grouped_entities=True)
-    return _ner_pipeline
+# NER pipeline
+ner_tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER")
+ner_model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER")
+ner_pipeline = pipeline("ner", model=ner_model, tokenizer=ner_tokenizer, grouped_entities=True)
 
 # ----------- Core Functions -----------
 def fetch_news(ticker):
@@ -58,14 +49,14 @@ def fetch_news(ticker):
     except Exception:
         return []
 
-def analyze_sentiment(text, sentiment_pipeline):
+def analyze_sentiment(text):
     try:
         result = sentiment_pipeline(text)[0]
         return "Positive" if result['label'] == 'POSITIVE' else "Negative"
     except Exception:
         return "Unknown"
 
-def extract_org_entities(text, ner_pipeline):
+def extract_org_entities(text):
     try:
         entities = ner_pipeline(text)
         org_entities = []