Update app.py

app.py

@@ -1,11 +1,9 @@
 import streamlit as st
-import requests
-from bs4 import BeautifulSoup
-from transformers import pipeline
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification
+from func import fetch_news, analyze_sentiment, extract_org_entities
+
 import time
 
-# ----------- Page Layout & Custom Styling -----------
+# ---------------- Page Setup ----------------
 st.set_page_config(page_title="Stock News Sentiment Analysis", layout="centered")
 
 st.markdown("""
@@ -28,80 +26,7 @@ st.markdown("""
     </style>
 """, unsafe_allow_html=True)
 
-# ----------- Model Setup -----------
-sentiment_model_id = "LinkLinkWu/Stock_Analysis_Test_Ahamed"
-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)
-
-ner_tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER")
-ner_model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER")
-ner_pipeline = pipeline("ner", model=ner_model, tokenizer=ner_tokenizer, grouped_entities=True)
-
-# ----------- 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 []
-
-# ----------- UI -----------
+# ---------------- User Interface ----------------
 st.title("\U0001F4CA Stock News Sentiment Analysis")
 st.markdown("""
 This tool analyzes the sentiment of news articles related to companies you mention in text.
@@ -118,6 +43,7 @@ if tickers:
 else:
     tickers = []
 
+# ---------------- Button Trigger ----------------
 if st.button("Get News and Sentiment"):
     if not tickers:
         st.warning("Please mention at least one recognizable company.")
@@ -152,10 +78,7 @@ if st.button("Get News and Sentiment"):
                     sentiment = sentiments[i-1]
                     st.markdown(f"{i}. [{news['title']}]({news['link']}) - **{sentiment}**")
 
-                if overall_sentiment != "Undecided":
-                    st.write(f"**Overall Sentiment for {ticker}: {overall_sentiment}**")
-                else:
-                    st.write("**No clear sentiment (does not meet threshold conditions).**")
+                st.write(f"**Overall Sentiment for {ticker}: {overall_sentiment}**")
             else:
                 st.write(f"No news available for {ticker}.")