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import streamlit as st
from transformers import pipeline
from bs4 import BeautifulSoup
import requests
# Set up models
ner_model = "Cassie-0121/fin-bert-finetuned-ner"
sentiment_model = "yiyanghkust/finbert-tone"
text_gen_model = "gpt2"
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
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"}
response = requests.get(url, headers=headers)
soup = BeautifulSoup(response.text, 'html.parser')
articles = soup.find_all('h3', class_="Mb(5px)")
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
# App title
st.title("AI-Powered Financial Analysis App")
# Sidebar with stock data examples
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()))
# 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}"
# 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 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}")
# 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}")
# 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 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'])
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
)
# Footer
st.sidebar.write("Powered by Hugging Face and Streamlit")
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