Create app.py

app.py

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+import streamlit as st
+from urllib.request import urlopen, Request
+from bs4 import BeautifulSoup
+import pandas as pd
+import plotly.express as px
+import json
+import nltk
+import datetime
+from nltk.sentiment.vader import SentimentIntensityAnalyzer
+
+# Ensure nltk dependencies are downloaded
+nltk.download('vader_lexicon')
+
+# Page Config
+st.set_page_config(page_title="StockSim News Sentiment Analyzer", layout="wide")
+
+# Custom CSS for Glassmorphism & Styling
+st.markdown("""
+    <style>
+    body {
+        font-family: 'Arial', sans-serif;
+    }
+    .glass {
+        background: rgba(255, 255, 255, 0.1);
+        border-radius: 16px;
+        box-shadow: 0 4px 30px rgba(0, 0, 0, 0.1);
+        backdrop-filter: blur(10px);
+        -webkit-backdrop-filter: blur(10px);
+        padding: 20px;
+    }
+    .button {
+        background: linear-gradient(135deg, #ff7eb3, #ff758c);
+        color: black;
+        padding: 10px;
+        border-radius: 10px;
+        text-align: center;
+        font-size: 16px;
+        cursor: pointer;
+        transition: 0.3s;
+    }
+    .button:hover {
+        background: linear-gradient(135deg, #ff758c, #ff7eb3);
+        transform: scale(1.05);
+    }
+    h1 {
+        color: black;
+        text-align: center;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+# Header
+st.markdown('<h1>📈 StockSim News Sentiment Analyzer 📊</h1>', unsafe_allow_html=True)
+
+# Finviz URL
+finviz_url = 'https://finviz.com/quote.ashx?t='
+
+def get_news(ticker):
+    try:
+        url = finviz_url + ticker
+        req = Request(url, headers={'User-Agent': 'Mozilla/5.0'}) 
+        response = urlopen(req)
+        html = BeautifulSoup(response, 'html.parser')
+        news_table = html.find(id='news-table')
+        return news_table
+    except Exception as e:
+        st.error(f"Error fetching news: {e}")
+        return None
+
+def parse_news(news_table):
+    parsed_news = []
+    today_string = datetime.datetime.today().strftime('%Y-%m-%d')
+    
+    if news_table:
+        for x in news_table.findAll('tr'):
+            try:
+                text = x.a.get_text() if x.a else ""
+                date_scrape = x.td.text.split() if x.td else []
+                date = today_string if len(date_scrape) == 1 else date_scrape[0]
+                time = date_scrape[-1] if date_scrape else "00:00"
+                parsed_news.append([date, time, text])
+            except Exception as e:
+                st.warning(f"Skipping a row due to error: {e}")
+    
+    parsed_news_df = pd.DataFrame(parsed_news, columns=['date', 'time', 'headline'])
+    parsed_news_df['datetime'] = pd.to_datetime(parsed_news_df['date'] + ' ' + parsed_news_df['time'], errors='coerce')
+    parsed_news_df.dropna(inplace=True)
+    return parsed_news_df
+
+def score_news(parsed_news_df):
+    vader = SentimentIntensityAnalyzer()
+    scores = parsed_news_df['headline'].apply(lambda x: vader.polarity_scores(x) if isinstance(x, str) else {'compound': 0}).tolist()
+    scores_df = pd.DataFrame(scores)
+    parsed_and_scored_news = parsed_news_df.join(scores_df).set_index('datetime')
+    parsed_and_scored_news = parsed_and_scored_news.rename(columns={"compound": "sentiment_score"})
+    return parsed_and_scored_news
+
+def plot_sentiment(parsed_and_scored_news, ticker, freq):
+    if not parsed_and_scored_news.empty:
+        mean_scores = parsed_and_scored_news.resample(freq).mean()
+        fig = px.bar(mean_scores, x=mean_scores.index, y='sentiment_score', 
+                     title=f'{ticker} {freq} Sentiment Scores')
+        return fig
+    else:
+        st.warning("No sentiment data available for plotting.")
+        return None
+
+# UI Elements
+ticker = st.text_input('🔍 Enter Stock Ticker', '').upper()
+if ticker:
+    try:
+        st.markdown('<div class="glass">', unsafe_allow_html=True)
+        st.subheader(f"📊 Sentiment Analysis for {ticker} Stock")
+        news_table = get_news(ticker)
+        
+        if news_table:
+            parsed_news_df = parse_news(news_table)
+            parsed_and_scored_news = score_news(parsed_news_df)
+            
+            fig_hourly = plot_sentiment(parsed_and_scored_news, ticker, 'H')
+            fig_daily = plot_sentiment(parsed_and_scored_news, ticker, 'D')
+            
+            if fig_hourly:
+                st.plotly_chart(fig_hourly)
+            if fig_daily:
+                st.plotly_chart(fig_daily)
+            
+            st.markdown("""
+            The above charts show **hourly and daily sentiment scores** for the stock.
+            News headlines are obtained from **FinViz** and analyzed using **NLTK Vader**.
+            """)
+            
+            st.table(parsed_and_scored_news[['headline', 'sentiment_score']])
+        else:
+            st.warning("No news data available for this ticker.")
+        
+        st.markdown('</div>', unsafe_allow_html=True)
+    except Exception as e:
+        st.error(f"⚠️ An unexpected error occurred: {str(e)}")
+else:
+    st.info("ℹ️ Enter a valid stock ticker (e.g., AAPL) and hit Enter.")