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from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification |
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from bs4 import BeautifulSoup |
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import requests |
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_sentiment_pipeline = None |
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_ner_pipeline = None |
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def get_sentiment_pipeline(): |
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global _sentiment_pipeline |
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if _sentiment_pipeline is None: |
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model_id = "LinkLinkWu/Stock_Analysis_Test_Ahamed" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForSequenceClassification.from_pretrained(model_id) |
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_sentiment_pipeline = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer) |
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return _sentiment_pipeline |
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def get_ner_pipeline(): |
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global _ner_pipeline |
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if _ner_pipeline is None: |
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tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER") |
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model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER") |
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_ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer, grouped_entities=True) |
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return _ner_pipeline |
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def fetch_news(ticker): |
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try: |
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url = f"https://finviz.com/quote.ashx?t={ticker}" |
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headers = { |
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'User-Agent': 'Mozilla/5.0', |
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'Accept': 'text/html', |
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'Accept-Language': 'en-US,en;q=0.5', |
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'Referer': 'https://finviz.com/', |
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'Connection': 'keep-alive', |
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} |
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response = requests.get(url, headers=headers) |
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if response.status_code != 200: |
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return [] |
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soup = BeautifulSoup(response.text, 'html.parser') |
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title = soup.title.text if soup.title else "" |
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if ticker not in title: |
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return [] |
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news_table = soup.find(id='news-table') |
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if news_table is None: |
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return [] |
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news = [] |
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for row in news_table.findAll('tr')[:30]: |
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a_tag = row.find('a') |
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if a_tag: |
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title = a_tag.get_text() |
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link = a_tag['href'] |
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news.append({'title': title, 'link': link}) |
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return news |
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except Exception: |
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return [] |
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def analyze_sentiment(text, sentiment_pipeline): |
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try: |
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result = sentiment_pipeline(text)[0] |
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return "Positive" if result['label'] == 'POSITIVE' else "Negative" |
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except Exception: |
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return "Unknown" |
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def extract_org_entities(text, ner_pipeline): |
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try: |
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entities = ner_pipeline(text) |
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org_entities = [] |
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for ent in entities: |
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if ent["entity_group"] == "ORG": |
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clean_word = ent["word"].replace("##", "").strip() |
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if clean_word.upper() not in org_entities: |
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org_entities.append(clean_word.upper()) |
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if len(org_entities) >= 5: |
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break |
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return org_entities |
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except Exception: |
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return [] |
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