Delete app.py

app.py

@@ -1,166 +0,0 @@
-import streamlit as st
-import requests
-from bs4 import BeautifulSoup
-from transformers import pipeline
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification
-import time
-
-# ----------- Page Layout & Custom Styling -----------
-st.set_page_config(page_title="Stock News Sentiment Analysis", layout="centered")
-
-st.markdown("""
-    <style>
-        .main { background-color: #f9fbfc; }
-        .stTextInput>div>div>input {
-            font-size: 16px;
-            padding: 0.5rem;
-        }
-        .stButton>button {
-            background-color: #4CAF50;
-            color: white;
-            font-size: 16px;
-            padding: 0.5rem 1rem;
-            border-radius: 8px;
-        }
-        .stButton>button:hover {
-            background-color: #45a049;
-        }
-    </style>
-""", unsafe_allow_html=True)
-
-# ----------- Model Setup -----------
-sentiment_model_id = "LinkLinkWu/Boss_Stock_News_Analysis"
-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 -----------
-st.title("\U0001F4CA Stock News Sentiment Analysis")
-st.markdown("""
-This tool parses stock tickers and analyzes the sentiment of related news articles.
-\U0001F4A1 *Example input:* `META, NVDA, AAPL, NTES, NCTY`
-**Note:** If news fetching fails, it might be due to changes in the Finviz website structure or access restrictions. Please verify the website manually or try again later.
-""")
-
-input_mode = st.radio("Choose input method:", ("Text (auto detect)", "Manual tickers"))
-
-if input_mode == "Manual tickers":
-    tickers_input = st.text_input("Enter stock tickers separated by commas:", "META, NVDA, AAPL")
-    tickers = [ticker.strip().upper() for ticker in tickers_input.split(",") if ticker.strip()]
-else:
-    free_text = st.text_area("Enter text mentioning companies:", height=100)
-    tickers = extract_org_entities(free_text)
-
-if tickers:
-    cleaned_input = ", ".join(tickers)
-    st.markdown(f"\U0001F50E **Parsed Tickers:** `{cleaned_input}`")
-else:
-    tickers = []
-
-if st.button("Get News and Sentiment"):
-    if not tickers:
-        st.warning("Please enter at least one stock ticker.")
-    else:
-        progress_bar = st.progress(0)
-        total_stocks = len(tickers)
-        for idx, ticker in enumerate(tickers):
-            st.subheader(f"Analyzing {ticker}...")
-            news_list = fetch_news(ticker)
-
-            if news_list:
-                sentiments = []
-                for news in news_list:
-                    sentiment = analyze_sentiment(news['title'])
-                    sentiments.append(sentiment)
-
-                positive_count = sentiments.count("Positive")
-                negative_count = sentiments.count("Negative")
-                total = len(sentiments)
-                positive_ratio = positive_count / total if total else 0
-                negative_ratio = negative_count / total if total else 0
-
-                if positive_ratio >= 0.4:
-                    overall_sentiment = "Positive"
-                elif negative_ratio >= 0.6:
-                    overall_sentiment = "Negative"
-                else:
-                    overall_sentiment = "Neutral"
-
-                st.write(f"**Top 3 News Articles for {ticker}**")
-                for i, news in enumerate(news_list[:3], 1):
-                    sentiment = sentiments[i-1]
-                    st.markdown(f"{i}. [{news['title']}]({news['link']}) - **{sentiment}**")
-
-                st.write(f"**Overall Sentiment for {ticker}: {overall_sentiment}**")
-            else:
-                st.write(f"No news available for {ticker}.")
-
-            progress_bar.progress((idx + 1) / total_stocks)
-            time.sleep(0.1)