Update app.py

app.py

@@ -1,18 +1,20 @@
 import streamlit as st
-from thronetrader import StrategicSignals
 import requests
-from Pandas_Market_Predictor import Pandas_Market_Predictor
 import pandas as pd
 
-
 # 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=TIME_SERIES_INTRADAY&symbol={symbol}&interval=5min&apikey={api_key}'
-    response = requests.get(url)
-    alpha_vantage_data = response.json()
-    return alpha_vantage_data
+    try:
+        url = f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol={symbol}&interval=5min&apikey={api_key}'
+        response = requests.get(url)
+        response.raise_for_status()  # Raise an error for bad responses
+        alpha_vantage_data = response.json()
+        return alpha_vantage_data
+    except requests.RequestException as e:
+        st.error(f"Error fetching data: {e}")
+        return None
 
 def main():
     st.title("Stock Trend Predictor")
@@ -30,15 +32,16 @@ def main():
     # Fetch Alpha Vantage data
     alpha_vantage_data = fetch_alpha_vantage_data(api_key, symbol)
 
-    # Extract relevant data from Alpha Vantage response
-    alpha_vantage_time_series = alpha_vantage_data.get('Time Series (5min)', {})
-    df = pd.DataFrame(alpha_vantage_time_series).T
-    df.index = pd.to_datetime(df.index)
-    df = df.dropna(axis=0)
+    if alpha_vantage_data:
+        # Extract relevant data from Alpha Vantage response
+        alpha_vantage_time_series = alpha_vantage_data.get('Time Series (5min)', {})
+        df = pd.DataFrame(alpha_vantage_time_series).T
+        df.index = pd.to_datetime(df.index)
+        df = df.dropna(axis=0)
 
-    # Display the raw data
-    st.subheader("Raw Data:")
-    st.write(df)
+        # Display the raw data
+        st.subheader("Raw Data:")
+        st.write(df)
 
 if __name__ == "__main__":
-    main()
+    main()