Update app.py

app.py

@@ -1,139 +1,53 @@
 import streamlit as st
-import websocket
-import threading
-import json
+from transformers import pipeline
+import yfinance as yf
 import requests
-
-# Replace with your Finnhub API key
-FINNHUB_API_KEY = 'your_finnhub_api_key'
-
-# Global variable to store real-time data
-real_time_data = {}
-
-# Function to fetch top 5 stock tickers based on Finnhub's trending API (or another data source)
-def fetch_top_tickers():
-    url = 'https://finnhub.io/api/v1/stock/symbol'
-    params = {'exchange': 'US', 'token': FINNHUB_API_KEY}
-    response = requests.get(url, params=params)
-    if response.status_code == 200:
-        data = response.json()
-        # Select top 5 trending tickers (based on symbol)
-        top_tickers = [item['symbol'] for item in data[:5]]  # Top 5 from response data
-        return top_tickers
+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.error("Failed to fetch trending tickers.")
-        return []
-
-# Function to handle real-time WebSocket messages
-def on_message(ws, message):
-    data = json.loads(message)
-    if 'data' in data:
-        symbol = data['data'][0]['symbol']
-        price = data['data'][0]['p']
-        real_time_data[symbol] = price
-
-# WebSocket setup to fetch real-time data
-def on_error(ws, error):
-    print(f"Error: {error}")
-
-def on_close(ws):
-    print("### closed ###")
-
-def on_open(ws):
-    # Subscribe to tickers dynamically fetched
-    for ticker in tickers:
-        ws.send(json.dumps({"type": "subscribe", "symbol": ticker}))
-
-# WebSocket thread function
-def start_websocket():
-    websocket.enableTrace(True)
-    ws = websocket.WebSocketApp(f"wss://ws.finnhub.io?token={FINNHUB_API_KEY}",
-                                on_message=on_message,
-                                on_error=on_error,
-                                on_close=on_close)
-    ws.on_open = on_open
-    ws.run_forever()
-
-# Function to start WebSocket in a separate thread
-def run_websocket_thread():
-    websocket_thread = threading.Thread(target=start_websocket)
-    websocket_thread.daemon = True
-    websocket_thread.start()
-
-# Function to fetch stock data for a given ticker (HTTP request)
-def get_stock_data(tickers):
-    stock_data = []
-    for ticker in tickers:
-        url = f'https://finnhub.io/api/v1/quote'
-        params = {'symbol': ticker, 'token': FINNHUB_API_KEY}
-        response = requests.get(url, params=params)
-        if response.status_code == 200:
-            data = response.json()
-            stock_data.append({
-                'Ticker': ticker,
-                'Current Price': data.get('c', 'N/A'),
-                'High Price': data.get('h', 'N/A'),
-                'Low Price': data.get('l', 'N/A'),
-                'Open Price': data.get('o', 'N/A'),
-                'Previous Close Price': data.get('pc', 'N/A')
-            })
-    return stock_data
-
-# Investment advice logic
-def investment_advice(stock_data):
-    recommendations = []
-    for stock in stock_data:
-        ticker = stock['Ticker']
-        current_price = stock['Current Price']
-        if current_price == 'N/A':
-            recommendations.append(f"Data not available for {ticker}.")
-        elif float(current_price) > 1000:  # Example threshold for high-priced stock
-            recommendations.append(f"Stock {ticker} has a high current price ({current_price}). Caution: Potential overvaluation.")
-        elif float(current_price) < 50:  # Example threshold for low-priced stock
-            recommendations.append(f"Stock {ticker} has a low current price ({current_price}). Caution: Potential undervaluation.")
-        else:
-            recommendations.append(f"Stock {ticker} has a moderate current price ({current_price}). Consider for investment.")
-    return recommendations
-
-# Streamlit UI setup
-st.title("AI-Powered Investment Advisory App")
-
-# User input for investment amount and desired return
-investment_amount = st.number_input("Enter investment amount ($):", min_value=100, value=1000)
-desired_return = st.number_input("Enter desired return (%):", min_value=0.0, value=5.0)
-
-# Fetch top 5 tickers dynamically
-st.subheader("Fetching top 5 trending stock tickers...")
-tickers = fetch_top_tickers()
-
-if tickers:
-    # Start WebSocket thread to listen to real-time data
-    run_websocket_thread()
-
-    # Fetch initial stock data (for a snapshot of stock details)
-    st.subheader("Fetching stock data for top 5 tickers...")
-    stock_data = get_stock_data(tickers)
-
-    # Display fetched stock data
-    st.subheader("Top 5 Stocks Based on Current Market Data")
-    for stock in stock_data:
-        st.write(stock)
-
-    # Display real-time stock prices
-    st.subheader("Real-Time Stock Prices")
-    for ticker in tickers:
-        if ticker in real_time_data:
-            st.write(f"Real-time price of {ticker}: {real_time_data[ticker]}")
-
-    # Provide investment advice based on stock data
-    st.subheader("Investment Advice")
-    recommendations = investment_advice(stock_data)
-    for recommendation in recommendations:
-        st.write(recommendation)
-
-    # Portfolio allocation suggestion
-    st.subheader("Portfolio Allocation Recommendation")
-    allocation = investment_amount / len(tickers)  # Equal allocation for simplicity
-    st.write(f"Recommended Portfolio Allocation (per stock): ${allocation:.2f}")
+        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.warning("No tickers found. Please try again later.")
+    st.write("Could not retrieve stock data at this time.")
+
+# Other components like fetching news, NER, and sentiment analysis would remain the same