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from typing import List, Tuple
from transformers import (
pipeline,
AutoTokenizer,
AutoModelForSequenceClassification,
AutoModelForTokenClassification,
)
from bs4 import BeautifulSoup
import requests
# ---------------------------------------------------------------------------
# Model identifiers
# ---------------------------------------------------------------------------
SENTIMENT_MODEL_ID = "ahmedrachid/FinancialBERT-Sentiment-Analysis" # returns: positive / neutral / negative
NER_MODEL_ID = "dslim/bert-base-NER"
# ---------------------------------------------------------------------------
# Eager initialisation of Hugging Face pipelines (shared across requests)
# ---------------------------------------------------------------------------
# Sentiment pipeline (binary decision will be made later)
sentiment_tokenizer = AutoTokenizer.from_pretrained(SENTIMENT_MODEL_ID)
sentiment_model = AutoModelForSequenceClassification.from_pretrained(SENTIMENT_MODEL_ID)
sentiment_pipeline = pipeline(
"sentiment-analysis",
model=sentiment_model,
tokenizer=sentiment_tokenizer,
)
# Named‑entity‑recognition pipeline (ORG extraction)
ner_tokenizer = AutoTokenizer.from_pretrained(NER_MODEL_ID)
ner_model = AutoModelForTokenClassification.from_pretrained(NER_MODEL_ID)
ner_pipeline = pipeline(
"ner",
model=ner_model,
tokenizer=ner_tokenizer,
grouped_entities=True,
)
# ---------------------------------------------------------------------------
# Core functionality
# ---------------------------------------------------------------------------
def fetch_news(ticker: str) -> List[dict]:
"""Scrape *up to* 30 recent headlines from Finviz for a given *ticker*.
Returns a list of dictionaries with ``{"title": str, "link": str}`` or an
empty list on any error/edge‑case (e.g. anti‑scraping redirect).
"""
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, timeout=10)
if response.status_code != 200:
return []
soup = BeautifulSoup(response.text, "html.parser")
page_title = soup.title.text if soup.title else ""
if ticker.upper() not in page_title.upper():
# Finviz sometimes redirects to a placeholder page if the ticker is unknown.
return []
news_table = soup.find(id="news-table")
if news_table is None:
return []
latest_news: List[dict] = []
for row in news_table.find_all("tr")[:30]: # keep only the 30 most recent rows
link_tag = row.find("a")
if link_tag:
latest_news.append({
"title": link_tag.get_text(strip=True),
"link": link_tag["href"],
})
return latest_news
except Exception:
# swallow all exceptions and degrade gracefully
return []
# ---------------------------------------------------------------------------
# Sentiment analysis helpers
# ---------------------------------------------------------------------------
# Raw labels coming from the FinancialBERT model
_POSITIVE = "positive"
_NEGATIVE = "negative"
_DEFAULT_THRESHOLD = 0.55 # default probability threshold; callers may override
def analyze_sentiment(
text: str,
pipe=None,
threshold: float = _DEFAULT_THRESHOLD,
) -> Tuple[str, float]:
"""Classify *text* as **Positive/Negative** and return its positive probability.
The underlying model is three‑class (positive/neutral/negative). We keep the
**positive** score only and compare it against *threshold* to obtain a binary
label. The function is **side‑effect free** and will never raise; on any
internal error it falls back to ``("Unknown", 0.0)``.
"""
try:
sentiment_pipe = pipe or sentiment_pipeline
raw_scores = sentiment_pipe(text, return_all_scores=True, truncation=True)[0]
score_lookup = {item["label"].lower(): item["score"] for item in raw_scores}
pos_score = score_lookup.get(_POSITIVE, 0.0)
label = "Positive" if pos_score >= threshold else "Negative"
return label, pos_score
except Exception:
return "Unknown", 0.0
# ---------------------------------------------------------------------------
# Aggregation logic – turning many headlines into one overall label
# ---------------------------------------------------------------------------
def aggregate_sentiments(
results: List[Tuple[str, float]],
avg_threshold: float = _DEFAULT_THRESHOLD,
) -> str:
"""Combine individual headline results into a single overall label.
The rule is simple: compute the *mean* positive probability across all
headlines and compare it with *avg_threshold*. If the list is empty, the
function returns ``"Unknown"``.
"""
if not results:
return "Unknown"
avg_pos = sum(score for _, score in results) / len(results)
return "Positive" if avg_pos >= avg_threshold else "Negative"
# ---------------------------------------------------------------------------
# Public helpers (kept for backward compatibility with app.py)
# ---------------------------------------------------------------------------
def get_sentiment_pipeline():
"""Expose the initialised sentiment pipeline (singleton)."""
return sentiment_pipeline
def get_ner_pipeline():
"""Expose the initialised NER pipeline (singleton)."""
return ner_pipeline
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