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import yfinance as yf
import requests
from bs4 import BeautifulSoup
from transformers import pipeline
import time
import urllib.parse
from typing import List, Dict, Any
import logging
# Set up logging
logger = logging.getLogger(__name__)
# --- Model Loading ---
sentiment_pipeline = None
MODEL_PATH = '/code/sentiment_model'
def load_sentiment_pipeline():
global sentiment_pipeline
if sentiment_pipeline is None:
logger.info("Loading sentiment analysis pipeline...")
try:
# Try to load the custom model
sentiment_pipeline = pipeline('text-classification', model=MODEL_PATH, tokenizer=MODEL_PATH)
logger.info("Custom sentiment pipeline loaded.")
except Exception as e:
logger.warning(f"Could not load custom model ({e}), using default pipeline...")
try:
# Fallback to default sentiment analysis
sentiment_pipeline = pipeline('sentiment-analysis')
logger.info("Default sentiment pipeline loaded.")
except Exception as e2:
logger.error(f"Could not load any sentiment pipeline: {e2}")
# Create a dummy pipeline that always returns neutral
sentiment_pipeline = lambda texts, **kwargs: [{'label': 'NEUTRAL', 'score': 0.5} for _ in texts]
# --- Helper function for making web requests ---
def get_session():
session = requests.Session()
session.headers.update({
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36',
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',
'Accept-Language': 'en-US,en;q=0.5',
'Connection': 'keep-alive',
})
return session
# --- PRODUCTION NEWS SCRAPING TOOLS ---
def scrape_google_news(company_name: str) -> List[Dict[str, Any]]:
"""Scrape Google News - this is working perfectly based on your test"""
logger.info(f"Fetching Google News for {company_name}...")
articles_data = []
try:
session = get_session()
# Try multiple query variations (your test showed this works)
queries = [
f'"{company_name}" stock',
f'{company_name} share price',
company_name
]
for query in queries:
try:
encoded_query = urllib.parse.quote(query)
url = f"https://news.google.com/rss/search?q={encoded_query}&hl=en&gl=US&ceid=US:en"
response = session.get(url, timeout=15)
if response.status_code == 200:
soup = BeautifulSoup(response.content, 'xml')
items = soup.find_all('item')
for item in items[:10]: # Top 10 articles
title_elem = item.find('title')
if title_elem and title_elem.text:
articles_data.append({
"title": title_elem.text.strip(),
"url": item.find('link').text if item.find('link') else '',
"source": item.find('source').text if item.find('source') else 'Google News'
})
if articles_data:
break # Stop if we found articles
except Exception as e:
logger.error(f"Google News query '{query}' failed: {e}")
continue
except Exception as e:
logger.error(f"Google News scraping failed: {e}")
logger.info(f"-> Google News returned {len(articles_data)} articles.")
return articles_data
def scrape_yahoo_finance_news(ticker: str) -> List[Dict[str, Any]]:
"""Yahoo Finance news scraper"""
logger.info(f"Fetching Yahoo Finance News for {ticker}...")
articles_data = []
try:
# Try yfinance first
stock = yf.Ticker(ticker)
news = stock.news
if news:
for article in news[:10]: # Top 10
if article.get('title'):
articles_data.append({
"title": article['title'].strip(),
"url": article.get('link', ''),
"source": article.get('publisher', 'Yahoo Finance'),
})
except Exception as e:
logger.error(f"Yahoo Finance scraping failed: {e}")
logger.info(f"-> Yahoo Finance returned {len(articles_data)} articles.")
return articles_data
def scrape_reddit_mentions(company_name: str) -> List[Dict[str, Any]]:
"""Reddit mentions scraper - working well based on your test"""
logger.info(f"Fetching Reddit mentions for {company_name}...")
mentions_data = []
try:
session = get_session()
subreddits = ['stocks', 'investing', 'IndiaInvestments', 'SecurityAnalysis', 'ValueInvesting']
for subreddit in subreddits:
try:
# Search queries that worked in your test
search_queries = [
f'"{company_name}"',
company_name.split()[0] if ' ' in company_name else company_name
]
for query in search_queries:
search_url = f"https://www.reddit.com/r/{subreddit}/search.json"
params = {
'q': query,
'sort': 'new',
'limit': 10,
'restrict_sr': 'true',
't': 'month'
}
response = session.get(search_url, params=params, timeout=10)
if response.status_code == 200:
data = response.json()
posts = data.get('data', {}).get('children', [])
for post in posts:
post_data = post.get('data', {})
if post_data.get('title'):
mentions_data.append({
"title": post_data['title'].strip(),
"url": f"https://reddit.com{post_data.get('permalink', '')}",
"source": f"r/{subreddit}"
})
if posts:
break # Found posts with this query
time.sleep(0.5) # Rate limiting
except Exception as e:
logger.error(f"Reddit r/{subreddit} failed: {e}")
time.sleep(1) # Rate limiting between subreddits
except Exception as e:
logger.error(f"Reddit scraping failed: {e}")
logger.info(f"-> Reddit returned {len(mentions_data)} mentions.")
return mentions_data
# --- THE MAIN TOOL FUNCTION ---
def get_combined_news_and_sentiment(ticker: str, company_name: str) -> Dict[str, Any]:
"""Main function that combines all news sources and analyzes sentiment"""
logger.info(f"Starting news analysis for {ticker} ({company_name})")
# Load sentiment pipeline
load_sentiment_pipeline()
all_sources = []
# Collect from all sources (based on your successful test)
try:
google_articles = scrape_google_news(company_name)
all_sources.extend(google_articles)
except Exception as e:
logger.error(f"Google News failed: {e}")
try:
yahoo_articles = scrape_yahoo_finance_news(ticker)
all_sources.extend(yahoo_articles)
except Exception as e:
logger.error(f"Yahoo Finance failed: {e}")
try:
reddit_mentions = scrape_reddit_mentions(company_name)
all_sources.extend(reddit_mentions)
except Exception as e:
logger.error(f"Reddit failed: {e}")
logger.info(f"Total items collected from all sources: {len(all_sources)}")
if not all_sources:
return {
"articles": [],
"sentiment_summary": {
"total_items": 0,
"positive": 0,
"negative": 0,
"neutral": 0,
"error": "Could not fetch any news from any source."
}
}
# Perform sentiment analysis
try:
titles = [item['title'] for item in all_sources if item.get('title')]
results = sentiment_pipeline(titles, truncation=True, max_length=512)
# Map sentiment results back to articles
for i, item in enumerate(all_sources):
if i < len(results):
label = results[i]['label']
# Normalize different label formats
if label.upper() in ['POSITIVE', 'POS', 'LABEL_2']:
sentiment = 'Positive'
elif label.upper() in ['NEGATIVE', 'NEG', 'LABEL_0']:
sentiment = 'Negative'
else:
sentiment = 'Neutral'
item['sentiment'] = sentiment
item['sentiment_score'] = round(results[i]['score'], 2)
else:
# Fallback: simple keyword-based sentiment
title_lower = item['title'].lower()
if any(word in title_lower for word in ['gain', 'rise', 'growth', 'profit', 'strong', 'bullish']):
item['sentiment'] = 'Positive'
item['sentiment_score'] = 0.7
elif any(word in title_lower for word in ['fall', 'decline', 'loss', 'weak', 'bearish', 'drop']):
item['sentiment'] = 'Negative'
item['sentiment_score'] = 0.7
else:
item['sentiment'] = 'Neutral'
item['sentiment_score'] = 0.5
# Count sentiments
counts = {'Positive': 0, 'Negative': 0, 'Neutral': 0}
for item in all_sources:
counts[item.get('sentiment', 'Neutral')] += 1
except Exception as e:
logger.error(f"Sentiment analysis failed: {e}")
# Fallback to neutral sentiment for all articles
for item in all_sources:
item['sentiment'] = 'Neutral'
item['sentiment_score'] = 0.5
counts = {'Positive': 0, 'Negative': 0, 'Neutral': len(all_sources)}
result = {
"articles": all_sources,
"sentiment_summary": {
"total_items": len(all_sources),
"positive": counts['Positive'],
"negative": counts['Negative'],
"neutral": counts['Neutral']
}
}
logger.info(f"News analysis completed: {len(all_sources)} articles, {counts}")
return result |