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 []