chore: add pre-commit fixes

.github/workflows/python-app.yaml

@@ -23,6 +23,10 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           pip install -r requirements.txt
+      - name: Run with black
+        run:
+          pip install black
+          black . --line-length=78
       - name: Lint with flake8
         run: |
           pip install flake8

src/app.py

@@ -8,36 +8,38 @@
 from sentiment import analyze_sentiment
 from visualization import plot_sentiment_summary
 
-st.title('Real-Time Stock Sentiment Analyzer')
+st.title("Real-Time Stock Sentiment Analyzer")
 
 
 # Data mode selection
-data_mode = st.radio('Select Data Mode:', ('Real-Time', 'Sample Data'))
+data_mode = st.radio("Select Data Mode:", ("Real-Time", "Sample Data"))
 
 
-ticker = st.text_input('Enter Stock Ticker:', 'AAPL')
-api_key = os.getenv('NEWS_API_KEY')
+ticker = st.text_input("Enter Stock Ticker:", "AAPL")
+api_key = os.getenv("NEWS_API_KEY")
 
 
-if st.button('Fetch Data'):
-    st.subheader('Stock Data')
-if data_mode == 'Real-Time':
+if st.button("Fetch Data"):
+    st.subheader("Stock Data")
+if data_mode == "Real-Time":
     data = get_stock_data(ticker)
 else:
-    data = pd.read_csv('data/sample_stock.csv')
+    data = pd.read_csv("data/sample_stock.csv")
     data.columns = data.columns.str.strip()  # Remove any spaces
     print(data.columns)
-    data = pd.read_csv('data/sample_stock.csv', parse_dates=['Date'], index_col='Date')
-st.line_chart(data['Close'])
-st.subheader('Latest News Sentiment')
+    data = pd.read_csv(
+        "data/sample_stock.csv", parse_dates=["Date"], index_col="Date"
+    )
+st.line_chart(data["Close"])
+st.subheader("Latest News Sentiment")
 sentiment_results = []
 try:
     articles = fetch_news(ticker)
     for title, desc in articles:
-        sentiment = analyze_sentiment(title + ' ' + str(desc))
+        sentiment = analyze_sentiment(title + " " + str(desc))
         sentiment_results.append(sentiment)
-        st.write(f'**{title}** - Sentiment: {sentiment}')
+        st.write(f"**{title}** - Sentiment: {sentiment}")
     if sentiment_results:
         plot_sentiment_summary(sentiment_results)
 except Exception as e:
-    st.warning(f'Cannot fetch real news: {e}')
+    st.warning(f"Cannot fetch real news: {e}")

src/fetch_news.py

@@ -5,12 +5,25 @@
 
 dotenv.load_dotenv()
 
+
 def fetch_news(query, page_size=10):
-    """Fetch latest news articles using NewsAPI"""
-    api_key = os.getenv('NEWS_API_KEY')
+    api_key = os.getenv("NEWS_API_KEY")
     if not api_key:
-        raise ValueError('Please set NEWS_API_KEY in .env file')
-    url = f'https://newsapi.org/v2/everything?q={query}&pageSize={page_size}&apiKey={api_key}'
-    response = requests.get(url)
-    articles = response.json().get('articles', [])
-    return [(a['title'], a['description']) for a in articles]
+        raise RuntimeError("NEWS_API_KEY not found")
+
+    url = "https://newsapi.org/v2/everything"
+    params = {
+        "q": query,
+        "pageSize": page_size,
+        "apiKey": api_key,
+        "language": "en",
+        "sortBy": "publishedAt",
+    }
+
+    headers = {"User-Agent": "StockSentimentAnalyzer/1.0"}
+
+    response = requests.get(url, params=params, headers=headers)
+    response.raise_for_status()
+
+    articles = response.json().get("articles", [])
+    return [(a["title"], a.get("description", "")) for a in articles]

src/fetch_stock.py

@@ -1,7 +1,7 @@
 import yfinance as yf
 
 
-def get_stock_data(ticker, period='1d', interval='1m'):
+def get_stock_data(ticker, period="1d", interval="1m"):
     stock = yf.Ticker(ticker)
     data = stock.history(period=period, interval=interval)
-    return data
+    return data

src/sentiment.py

@@ -4,8 +4,8 @@
 def analyze_sentiment(text):
     polarity = TextBlob(text).sentiment.polarity
     if polarity > 0:
-        return 'positive'
+        return "positive"
     elif polarity < 0:
-        return 'negative'
+        return "negative"
     else:
-        return 'neutral'
+        return "neutral"

src/visualization.py

@@ -1,25 +1,62 @@
 import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
 import streamlit as st
 
 
 def plot_stock(data, ticker):
-    plt.figure(figsize=(10,5))
-    plt.plot(data.index, data['Close'], label=f'{ticker} Close Price')
-    plt.xlabel('Time')
-    plt.ylabel('Price')
-    plt.title(f'{ticker} Stock Price')
+    plt.figure(figsize=(10, 5))
+
+    # Get today's high, low, and current price
+    today_high = data["High"].iloc[-1]
+    today_low = data["Low"].iloc[-1]
+    current_price = data["Close"].iloc[-1]
+
+    # Plot current price as a horizontal line
+    plt.hlines(
+        current_price,
+        xmin=data.index[0],
+        xmax=data.index[-1],
+        colors="red",
+        linewidth=2,
+        label=f"Current Price: {current_price:.2f}",
+    )
+
+    # Set y-axis limits to today's low/high with padding
+    plt.ylim(today_low * 0.995, today_high * 1.005)
+
+    # Format x-axis with ticks every 2 hours
+    ax = plt.gca()
+
+    # 2-hour interval
+    ax.xaxis.set_major_locator(mdates.HourLocator(interval=2))
+    ax.xaxis.set_major_formatter(mdates.DateFormatter("%H:%M"))
+    plt.xticks(rotation=45)
+
+    # Annotate current price
+    plt.text(
+        data.index[len(data) // 2],
+        current_price * 1.001,
+        f"{current_price:.2f}",
+        color="red",
+        fontsize=12,
+        ha="center",
+    )
+
+    plt.ylabel("Price")
+    plt.xlabel("Time (Market Hours)")
+    plt.title(f"{ticker} Price Today")
     plt.legend()
     st.pyplot(plt)
 
 
 def plot_sentiment_summary(sentiment_list):
-    counts = {'positive': 0, 'negative': 0, 'neutral': 0}
+    counts = {"positive": 0, "negative": 0, "neutral": 0}
     for s in sentiment_list:
         counts[s] += 1
     labels = counts.keys()
     values = counts.values()
     fig, ax = plt.subplots()
-    ax.bar(labels, values, color=['green', 'red', 'gray'])
-    ax.set_ylabel('Count')
-    ax.set_title('News Sentiment Summary')
-    st.pyplot(fig)
+    ax.bar(labels, values, color=["green", "red", "gray"])
+    ax.set_ylabel("Count")
+    ax.set_title("News Sentiment Summary")
+    st.pyplot(fig)