Update app.py

app.py

@@ -1,7 +1,8 @@
 import streamlit as st
-from transformers import pipeline
-from bs4 import BeautifulSoup
 import requests
+from bs4 import BeautifulSoup
+from transformers import pipeline
+import yfinance as yf
 
 # Set up models
 ner_model = "Cassie-0121/fin-bert-finetuned-ner"
@@ -12,94 +13,82 @@ ner = pipeline("ner", model=ner_model)
 sentiment_analyzer = pipeline("sentiment-analysis", model=sentiment_model)
 text_generator = pipeline("text-generation", model=text_gen_model)
 
-# Function to scrape recent stock news
+# Function to fetch stock news using web scraping from Yahoo Finance
 def get_stock_news(stock_symbol):
-    url = f"https://finance.yahoo.com/quote/{stock_symbol}/news?p={stock_symbol}"
-    headers = {"User-Agent": "Mozilla/5.0"}
+    url = f'https://finance.yahoo.com/quote/{stock_symbol}?p={stock_symbol}'
+    headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'}
+    
+    # Send request and parse the page
     response = requests.get(url, headers=headers)
-    soup = BeautifulSoup(response.text, 'html.parser')
-    articles = soup.find_all('h3', class_="Mb(5px)")
+    soup = BeautifulSoup(response.content, 'html.parser')
+    
+    # Extracting news headlines (For Yahoo Finance, it's under the 'Recent News' section)
+    headlines = []
+    news_section = soup.find_all('li', {'class': 'js-stream-content'})
+    for news_item in news_section:
+        headline = news_item.find('h3')
+        if headline:
+            headlines.append(headline.get_text())
+    
+    return headlines
 
-    news_list = []
-    for article in articles[:5]:  # Limit to the top 5 news articles
-        headline = article.get_text()
-        news_list.append(headline)
-    return news_list
+# Function to fetch stock data using Yahoo Finance (yfinance library)
+def get_stock_data(stock_symbol):
+    stock = yf.Ticker(stock_symbol)
+    stock_info = stock.history(period="1d")  # Fetching the latest stock data (1-day)
+    return stock_info
 
 # App title
 st.title("AI-Powered Financial Analysis App")
 
-# Sidebar with stock data examples
+# Sidebar for stock symbol input
 st.sidebar.header("Stock Data & Analysis")
-examples = {
-    "Apple Inc. (AAPL)": "AAPL",
-    "Tesla Inc. (TSLA)": "TSLA",
-    "Amazon.com Inc. (AMZN)": "AMZN",
-    "Microsoft Corp. (MSFT)": "MSFT",
-    "Alphabet Inc. (GOOGL)": "GOOGL",
-    "Other": ""  # Placeholder for custom input
-}
-
-selected_example = st.sidebar.selectbox("Select a stock symbol or choose 'Other' to enter custom text:", list(examples.keys()))
+stock_symbol = st.sidebar.text_input("Enter Stock Symbol (e.g., AAPL for Apple):", "AAPL")
 
-# If "Other" is selected, provide an input box for custom text
-if selected_example == "Other":
-    input_text = st.sidebar.text_area("Enter your own stock data for analysis:")
-    stock_symbol = None
-else:
-    stock_symbol = examples[selected_example]
-    input_text = f"Latest news for {selected_example}"
+# Fetch stock data and news
+if stock_symbol:
+    st.sidebar.write(f"Fetching data for {stock_symbol}...")
 
-# Display selected or inputted text for review
-st.subheader("Stock Data & Analysis")
-st.write(input_text if input_text else "Please select a stock or enter custom text for analysis.")
+    # Fetch stock news
+    stock_news = get_stock_news(stock_symbol)
 
-# Fetch and display news for the selected stock
-if stock_symbol:
-    st.subheader("Latest Stock News")
-    news_articles = get_stock_news(stock_symbol)
-    for article in news_articles:
-        st.write(f"- {article}")
+    # Fetch stock data (latest price, volume, etc.)
+    stock_data = get_stock_data(stock_symbol)
+    
+    # Display stock data
+    st.subheader(f"Latest Stock Data for {stock_symbol}")
+    st.write(stock_data)
 
-# Key Financial Entities extraction with filtering
-st.subheader("Extracted Key Financial Entities")
-if input_text:
-    entities = ner(input_text)
-    filtered_entities = [entity for entity in entities if entity['score'] > 0.7 and entity['word'].isalpha()]  # Filter low-score and non-alphabetic tokens
-    for entity in filtered_entities:
-        st.write(f"Entity: {entity['word']}, Label: {entity.get('entity', 'N/A')}, Score: {entity['score']:.2f}")
+    # Display stock news
+    st.subheader(f"Latest News for {stock_symbol}")
+    for news in stock_news:
+        st.write(news)
 
-# Sentiment Analysis
-st.subheader("Sentiment Analysis")
-if input_text:
-    sentiment = sentiment_analyzer(input_text)
-    for result in sentiment:
-        st.write(f"Sentiment: {result['label']}, Score: {result['score']:.2f}")
+    # Text for analysis
+    input_text = ' '.join(stock_news)  # Combine news for analysis
 
-# Investment Advice or Strategy Generation with better prompt handling
-st.subheader("Investment Advice or Strategy")
-if input_text:
-    prompt = f"Provide a clear and concise investment strategy for {selected_example if selected_example != 'Other' else 'the selected stock'} based on recent news and financial performance. "
-    advice = text_generator(prompt + input_text, max_length=80, num_return_sequences=1)
-    st.write(advice[0]['generated_text'])
+    # Display extracted entities
+    st.subheader("Extracted Key Financial Entities")
+    if input_text:
+        entities = ner(input_text)
+        for entity in entities:
+            st.write(f"Entity: {entity['word']}, Label: {entity.get('entity', 'N/A')}, Score: {entity.get('score', 0.0):.2f}")
+    
+    # Sentiment Analysis
+    st.subheader("Sentiment Analysis")
+    if input_text:
+        sentiment = sentiment_analyzer(input_text)
+        for result in sentiment:
+            st.write(f"Sentiment: {result['label']}, Score: {result['score']:.2f}")
+    
+    # Investment Advice or Strategy
+    st.subheader("Investment Advice or Strategy")
+    if input_text:
+        prompt = f"Provide a focused and complete investment strategy for {stock_symbol} based on the latest news and trends."
+        advice = text_generator(prompt + input_text, max_length=100, num_return_sequences=1, do_sample=True, temperature=0.7)
+        st.write(advice[0]['generated_text'])
 else:
-    st.write("No investment advice generated. Please select a stock or enter custom text.")
-
-# Make the app visually more attractive
-st.markdown(
-    """
-    <style>
-        .stApp {
-            background-color: #F5F5F5;
-        }
-        .stSidebar {
-            background-color: #333333;
-            color: white;
-        }
-    </style>
-    """,
-    unsafe_allow_html=True
-)
+    st.write("Please enter a valid stock symbol to get analysis.")
 
-# Footer
-st.sidebar.write("Powered by Hugging Face and Streamlit")
+# Optional: Footer for extra branding
+st.sidebar.write("Powered by Hugging Face, Streamlit, and Web Scraping")