Update func.py

func.py

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+import streamlit as st
+import requests
+from bs4 import BeautifulSoup
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification
+
+# ---------------- Model Setup ----------------
+@st.cache_resource
+def load_sentiment_model():
+    model_id = "LinkLinkWu/Stock_Analysis_Test_Ahamed"
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    model = AutoModelForSequenceClassification.from_pretrained(model_id)
+    return pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+
+@st.cache_resource
+def load_ner_model():
+    tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER")
+    model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER")
+    return pipeline("ner", model=model, tokenizer=tokenizer, grouped_entities=True)
+
+sentiment_pipeline = load_sentiment_model()
+ner_pipeline = load_ner_model()
+
+# ---------------- Helper 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:
+            st.error(f"Failed to fetch page for {ticker}: Status code {response.status_code}")
+            return []
+
+        soup = BeautifulSoup(response.text, 'html.parser')
+        title = soup.title.text if soup.title else ""
+        if ticker not in title:
+            st.error(f"Page for {ticker} not found or access denied.")
+            return []
+
+        news_table = soup.find(id='news-table')
+        if news_table is None:
+            st.error(f"News table not found for {ticker}. The website structure might have changed.")
+            return []
+
+        news = []
+        for row in news_table.findAll('tr')[:50]:
+            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 as e:
+        st.error(f"Failed to fetch news for {ticker}: {e}")
+        return []
+
+def analyze_sentiment(text):
+    try:
+        result = sentiment_pipeline(text)[0]
+        return "Positive" if result['label'] == 'POSITIVE' else "Negative"
+    except Exception as e:
+        st.error(f"Sentiment analysis failed: {e}")
+        return "Unknown"
+
+def extract_org_entities(text):
+    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 as e:
+        st.error(f"NER entity extraction failed: {e}")
+        return []