Update app.py

app.py

@@ -1,13 +1,19 @@
 import streamlit as st
-import yfinance as yf
+import requests
 import pandas as pd
+from datetime import datetime
 
-def fetch_yfinance_data(symbol):
-    data = yf.download(symbol, start="2024-02-04", end="2024-02-04", interval="5m")
-    return data
+# Hard-coded API key for demonstration purposes
+API_KEY = "QR8F9B7T6R2SWTAT"
+
+def fetch_alpha_vantage_data(api_key, symbol):
+    url = f'https://www.alphavantage.co/query?function=GLOBAL_QUOTE&symbol={symbol}&apikey={api_key}'
+    response = requests.get(url)
+    alpha_vantage_data = response.json()
+    return alpha_vantage_data
 
 def main():
-    st.title("Stock Trend Predictor")
+    st.title("Real-Time Stock Data")
 
     # User input for stock symbol
     symbol = st.text_input("Enter Stock Symbol (e.g., IBM):")
@@ -16,12 +22,26 @@ def main():
         st.warning("Please enter a valid stock symbol.")
         st.stop()
 
-    # Fetch yfinance data
-    stock_data = fetch_yfinance_data(symbol)
+    # Use the hard-coded API key
+    api_key = API_KEY
+
+    # Continuously fetch and display real-time data
+    while True:
+        # Fetch Alpha Vantage data
+        alpha_vantage_data = fetch_alpha_vantage_data(api_key, symbol)
+
+        # Extract relevant data from Alpha Vantage response
+        alpha_vantage_quote = alpha_vantage_data.get('Global Quote', {})
+        df = pd.DataFrame([alpha_vantage_quote])
+        df.index = [datetime.now()]  # Use the current timestamp as the index
+        df = df.dropna(axis=0)
+
+        # Display the real-time data
+        st.subheader("Real-Time Data:")
+        st.write(df)
 
-    # Display the raw data
-    st.subheader("Raw Data:")
-    st.write(stock_data)
+        # Add a delay to avoid exceeding API rate limits
+        st.experimental_sleep(60)  # Sleep for 60 seconds (adjust as needed)
 
 if __name__ == "__main__":
     main()