Update app.py

app.py

@@ -1,19 +1,19 @@
 import streamlit as st
 import requests
 import pandas as pd
-from datetime import datetime, timedelta
+from datetime import datetime
 
 # Hard-coded API key for demonstration purposes
 API_KEY = "QR8F9B7T6R2SWTAT"
 
-def fetch_alpha_vantage_intraday(api_key, symbol):
-    url = f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol={symbol}&interval=1min&apikey={api_key}'
+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("Latest Traded Data (Last Hour)")
+    st.title("Real-Time Stock Data")
 
     # User input for stock symbol
     symbol = st.text_input("Enter Stock Symbol (e.g., IBM):")
@@ -25,28 +25,23 @@ def main():
     # Use the hard-coded API key
     api_key = API_KEY
 
-    # Set the time interval for fetching historical intraday data
-    interval = 1  # 1-minute intervals
+    # Continuously fetch and display real-time data
+    while True:
+        # Fetch Alpha Vantage data
+        alpha_vantage_data = fetch_alpha_vantage_data(api_key, symbol)
 
-    # Fetch Alpha Vantage intraday data
-    alpha_vantage_data = fetch_alpha_vantage_intraday(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)
 
-    # Extract relevant data from Alpha Vantage response
-    alpha_vantage_time_series = alpha_vantage_data.get(f'Time Series ({interval}min)', {})
-    df = pd.DataFrame(alpha_vantage_time_series).T
-    df.index = pd.to_datetime(df.index)
+        # Display the real-time data
+        st.subheader("Real-Time Data:")
+        st.write(df)
 
-    # Add a new column for the trading day
-    df['Trading Day'] = df.index.date
-
-    # Filter data for the last hour
-    current_time = datetime.now()
-    one_hour_ago = current_time - timedelta(hours=1)
-    filtered_df = df[df.index >= one_hour_ago]
-
-    # Display the latest traded data for the last hour including the trading day
-    st.subheader("Latest Traded Data (Last Hour):")
-    st.write(filtered_df[['Trading Day', 'open', 'high', 'low', 'close', 'volume']].tail(1))
+        # Add a delay to avoid exceeding API rate limits
+        st.experimental_sleep(60)  # Sleep for 60 seconds (adjust as needed)
 
 if __name__ == "__main__":
     main()