Update app.py

app.py

@@ -5,16 +5,36 @@ from transformers import pipeline
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import time
 
-model_id = "LinkLinkWu/Boss_Stock_News_Analysis"
+# ----------- 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)
 
-# Load tokenizer & Model
+# ----------- Model Setup -----------
+model_id = "LinkLinkWu/Boss_Stock_News_Analysis"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForSequenceClassification.from_pretrained(model_id)
-
-# Initialize sentiment analysis pipeline
 sentiment_pipeline = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
 
-# Function to fetch top 50 news articles from FinViz
+# ----------- Function Definitions -----------
 def fetch_news(ticker):
     try:
         url = f"https://finviz.com/quote.ashx?t={ticker}"
@@ -32,7 +52,6 @@ def fetch_news(ticker):
         st.error(f"Failed to fetch news for {ticker}: {e}")
         return []
 
-# Function to analyze sentiment of news title
 def analyze_sentiment(text):
     try:
         result = sentiment_pipeline(text)[0]
@@ -41,51 +60,59 @@ def analyze_sentiment(text):
         st.error(f"Sentiment analysis failed: {e}")
         return "Unknown"
 
-# Streamlit UI
-st.title("Stock News Sentiment Analysis")
+# ----------- Streamlit UI -----------
+st.title("📊 Stock News Sentiment Analysis")
+st.markdown("""
+This tool parses stock tickers and analyzes the sentiment of related news articles.
+
+💡 *Example input:* `META, NVDA, AAPL, NTES, NCTY`
+""")
 
 # Input field for stock tickers
-tickers_input = st.text_input("Enter five stock tickers separated by commas (e.g., AAPL, MSFT, GOOGL, AMZN, TSLA):")
+tickers_input = st.text_input("Enter stock tickers separated by commas:", "META, NVDA, AAPL, NTES, NCTY")
 
+# Parse and display cleaned tickers in real-time
+if tickers_input:
+    tickers = [ticker.strip().upper() for ticker in tickers_input.split(",") if ticker.strip()]
+    cleaned_input = ", ".join(tickers)
+    st.markdown(f"🔎 **Parsed Tickers:** `{cleaned_input}`")
+else:
+    tickers = []
+
+# Button to trigger sentiment analysis
 if st.button("Get News and Sentiment"):
-    if tickers_input:
-        tickers = [ticker.strip().upper() for ticker in tickers_input.split(',')]
-        
-        # Validate input
-        if len(tickers) != 5:
-            st.error("Please enter exactly five stock tickers.")
-        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 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:
+                # Analyze sentiment for all news articles (up to 50)
+                sentiments = []
+                for news in news_list:
+                    sentiment = analyze_sentiment(news['title'])
+                    sentiments.append(sentiment)
                 
-                if news_list:
-                    # Analyze sentiment for all news articles (up to 50)
-                    sentiments = []
-                    for news in news_list:
-                        sentiment = analyze_sentiment(news['title'])
-                        sentiments.append(sentiment)
-                    
-                    # Determine overall sentiment based on majority
-                    positive_count = sentiments.count("Positive")
-                    negative_count = sentiments.count("Negative")
-                    overall_sentiment = "Positive" if positive_count > negative_count else "Negative"
-                    
-                    # Display top 3 news articles with sentiment
-                    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}**")
-                    
-                    # Display overall sentiment
-                    st.write(f"**Overall Sentiment for {ticker}: {overall_sentiment}**")
-                else:
-                    st.write(f"No news available for {ticker}.")
+                # Determine overall sentiment based on majority
+                positive_count = sentiments.count("Positive")
+                negative_count = sentiments.count("Negative")
+                overall_sentiment = "Positive" if positive_count > negative_count else "Negative"
                 
-                # Update progress bar
-                progress_bar.progress((idx + 1) / total_stocks)
-                time.sleep(0.1)  # Simulate processing time
-    else:
-        st.warning("Please enter stock tickers.")
+                # Display top 3 news articles with sentiment
+                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}**")
+                
+                # Display overall sentiment
+                st.write(f"**Overall Sentiment for {ticker}: {overall_sentiment}**")
+            else:
+                st.write(f"No news available for {ticker}.")
+            
+            # Update progress bar
+            progress_bar.progress((idx + 1) / total_stocks)
+            time.sleep(0.1)  # Simulate processing time