Update app.py

app.py

@@ -1,7 +1,4 @@
 import streamlit as st
-
-st.set_page_config(page_title="Stock News Sentiment Analysis", layout="centered")
-
 from func import (
     get_sentiment_pipeline,
     get_ner_pipeline,
@@ -11,79 +8,112 @@ from func import (
 )
 import time
 
+# ----------- Page Config -----------
+st.set_page_config(
+    page_title="Stock News Sentiment Analysis",
+    layout="centered",
+    initial_sidebar_state="collapsed"
+)
+
+# ----------- Custom CSS Styling -----------
+st.markdown("""
+    <style>
+        body {
+            background-color: #f9fbfc;
+        }
+        h1 {
+            color: #003366;
+            font-family: 'Segoe UI', sans-serif;
+        }
+        .stTextInput > div > div > input,
+        .stTextArea textarea {
+            font-size: 16px;
+        }
+        .stButton>button {
+            background-color: #003366;
+            color: white;
+            font-size: 16px;
+            padding: 0.4rem 1rem;
+            border-radius: 6px;
+        }
+        .stButton>button:hover {
+            background-color: #005299;
+        }
+    </style>
+""", unsafe_allow_html=True)
+
 # ----------- Header Section with Logo and Title -----------
 col1, col2 = st.columns([1, 10])
 with col1:
     st.image(
         "https://upload.wikimedia.org/wikipedia/commons/thumb/e/e8/Finance_Icon_Set_2013_-_Analytics.svg/1024px-Finance_Icon_Set_2013_-_Analytics.svg.png",
-        width=48
+        width=50
     )
 with col2:
-    st.markdown("<h1 style='margin-bottom: 0;'>Stock News Sentiment Analysis</h1>", unsafe_allow_html=True)
+    st.markdown(
+        "<h1 style='margin-bottom: 0.3rem;'>Stock News Sentiment Analysis</h1>",
+        unsafe_allow_html=True
+    )
 
-# ----------- Description -----------
+# ----------- Description Section -----------
 st.markdown("""
-<p style='font-size:17px; margin-top: 0.5rem;'>
-    Analyze the latest news sentiment of companies mentioned in your input.
-</p>
-<p style='font-size:17px;'>
-    <strong>💡 Try input like:</strong> <em>I want to check Apple, Tesla, and Microsoft.</em>
-</p>
-<p style='font-size:17px;'>
-    <strong>Note:</strong> News may fail to load if the Finviz website structure changes or access is restricted.
-</p>
+<div style='font-size:17px; line-height:1.6;'>
+    Gain quick insights into how the market perceives major companies based on news sentiment.<br>
+    <b>💬 Try:</b> <i>I want to check Apple, Tesla, and Microsoft.</i><br>
+    <span style='color:gray; font-size:15px;'>Note: News retrieval depends on Finviz availability and structure.</span>
+</div>
 """, unsafe_allow_html=True)
 
 # ----------- Input Area -----------
-free_text = st.text_area("Enter text mentioning companies:", height=100)
+st.markdown("### 📝 Enter Your Text")
+free_text = st.text_area("Mention company names to analyze:", height=100, placeholder="e.g. I'm interested in Apple, Google, and Nvidia")
+
 ner_pipeline = get_ner_pipeline()
 tickers = extract_org_entities(free_text, ner_pipeline)
 
 if tickers:
-    st.markdown(f"\U0001F50E **Identified Tickers:** `{', '.join(tickers)}`")
+    st.markdown(f"✅ **Identified Tickers:** `{', '.join(tickers)}`")
 else:
     tickers = []
 
 # ----------- Action Button -----------
+st.markdown("### 🚀 Run Analysis")
 if st.button("Get News and Sentiment"):
     if not tickers:
-        st.warning("Please mention at least one recognizable company.")
+        st.warning("⚠️ Please mention at least one recognizable company name.")
     else:
         sentiment_pipeline = get_sentiment_pipeline()
         progress_bar = st.progress(0)
         total_stocks = len(tickers)
 
         for idx, ticker in enumerate(tickers):
-            st.subheader(f"🔍 Analyzing {ticker}")
+            st.markdown(f"---\n### 🔍 Analyzing: `{ticker}`")
+
             news_list = fetch_news(ticker)
 
             if news_list:
-                sentiments = []
-                for news in news_list:
-                    sentiment = analyze_sentiment(news['title'], sentiment_pipeline)
-                    sentiments.append(sentiment)
+                sentiments = [analyze_sentiment(news['title'], sentiment_pipeline) for news in news_list]
 
-                positive_count = sentiments.count("Positive")
-                negative_count = sentiments.count("Negative")
+                pos_count = sentiments.count("Positive")
+                neg_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
+                pos_ratio = pos_count / total if total else 0
+                neg_ratio = neg_count / total if total else 0
 
-                if positive_ratio >= 0.3:
-                    overall_sentiment = "Positive"
-                elif negative_ratio >= 0.7:
-                    overall_sentiment = "Negative"
+                if pos_ratio >= 0.3:
+                    overall = "Positive"
+                elif neg_ratio >= 0.7:
+                    overall = "Negative"
                 else:
-                    overall_sentiment = "Neutral"
+                    overall = "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.markdown(f"**📰 Top News for `{ticker}`:**")
+                for i, news in enumerate(news_list[:3]):
+                    st.markdown(f"{i+1}. [{news['title']}]({news['link']}) — **{sentiments[i]}**")
 
-                st.success(f"**Overall Sentiment for {ticker}: {overall_sentiment}**")
+                st.success(f"📈 **Overall Sentiment for `{ticker}`: {overall}**")
             else:
-                st.write(f"No news available for {ticker}.")
+                st.info(f"No recent news available for `{ticker}`.")
 
             progress_bar.progress((idx + 1) / total_stocks)
             time.sleep(0.1)