app.py

import streamlit as st
from transformers import pipeline
import yfinance as yf
import requests
from bs4 import BeautifulSoup
import xml.etree.ElementTree as ET
import datetime

# Set up the models for Named Entity Recognition, Sentiment Analysis, and Text Generation
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)

# App title
st.title("AI-Powered Financial Analysis App")

# Sidebar for selecting stock
st.sidebar.header("Select Stock for Analysis")
top_25_stocks = [
    "AAPL", "MSFT", "GOOGL", "AMZN", "TSLA", "META", "NVDA", "BRK-B", "JNJ", "JPM",
    "V", "PG", "UNH", "DIS", "MA", "HD", "BAC", "XOM", "VZ", "PFE",
    "KO", "PEP", "MRK", "WMT", "CSCO"
]
selected_stock = st.sidebar.selectbox("Choose a stock for analysis:", top_25_stocks)

# Function to fetch the latest stock data using yfinance with dynamic date handling
def get_stock_data(ticker):
    stock = yf.Ticker(ticker)
    today = datetime.datetime.now().date()
    
    # Try fetching the data for today, but fall back to the most recent available date if markets are closed
    stock_info = stock.history(period="5d")  # Fetch data for the last 5 days to account for weekends or holidays
    if not stock_info.empty:
        # Get the most recent available data
        latest_data = stock_info.iloc[-1]
        return latest_data
    else:
        st.warning("No recent stock data available. Please try again later.")
        return None

# Display stock data
st.header(f"Latest Stock Data for {selected_stock}")
stock_data = get_stock_data(selected_stock)
if stock_data is not None:
    st.write(stock_data)
else:
    st.write("Could not retrieve stock data at this time.")

# Other components like fetching news, NER, and sentiment analysis would remain the same