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from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification
from bs4 import BeautifulSoup
import requests
# ----------- Lazy Initialization of Pipelines -----------
_sentiment_pipeline = None
_ner_pipeline = None
def get_sentiment_pipeline():
global _sentiment_pipeline
if _sentiment_pipeline is None:
model_id = "LinkLinkWu/Stock_Analysis_Test_Ahamed"
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
# ----------- Core Functions -----------
def fetch_news(ticker):
try:
url = f"https://finviz.com/quote.ashx?t={ticker}"
headers = {
'User-Agent': 'Mozilla/5.0',
'Accept': 'text/html',
'Accept-Language': 'en-US,en;q=0.5',
'Referer': 'https://finviz.com/',
'Connection': 'keep-alive',
}
response = requests.get(url, headers=headers)
if response.status_code != 200:
return []
soup = BeautifulSoup(response.text, 'html.parser')
title = soup.title.text if soup.title else ""
if ticker not in title:
return []
news_table = soup.find(id='news-table')
if news_table is None:
return []
news = []
for row in news_table.findAll('tr')[:30]:
a_tag = row.find('a')
if a_tag:
title = a_tag.get_text()
link = a_tag['href']
news.append({'title': title, 'link': link})
return news
except Exception:
return []
def analyze_sentiment(text, sentiment_pipeline):
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):
try:
entities = ner_pipeline(text)
org_entities = []
for ent in entities:
if ent["entity_group"] == "ORG":
clean_word = ent["word"].replace("##", "").strip()
if clean_word.upper() not in org_entities:
org_entities.append(clean_word.upper())
if len(org_entities) >= 5:
break
return org_entities
except Exception:
return []
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