Update app.py

app.py

@@ -3,6 +3,9 @@ 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):
     url = f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol=IBM&interval=5min&apikey={api_key}'
     response = requests.get(url)
@@ -12,34 +15,33 @@ def fetch_alpha_vantage_data(api_key):
 def main():
     st.title("Stock Trend Predictor")
 
-    # Get Alpha Vantage API key from user input
-    api_key = st.text_input("Enter your Alpha Vantage API key:")
+    # Use the hard-coded API key
+    api_key = API_KEY
 
     # Fetch Alpha Vantage data
-    if api_key:
-        alpha_vantage_data = fetch_alpha_vantage_data(api_key)
-
-        # 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)
-
-        # Create predictor
-        my_market_predictor = Pandas_Market_Predictor(df)
-
-        # Predict Trend
-        indicators = ["Indicator1", "Indicator2"]
-        trend = my_market_predictor.Trend_Detection(indicators, 10)
-
-        # Display results
-        st.subheader("Predicted Trend:")
-        st.write("Buy Trend :", trend['BUY'])
-        st.write("Sell Trend :", trend['SELL'])
-        st.write(f"Standard Deviation Percentage: {my_market_predictor.PERCENT_STD}%")
-
-        # Delete the DataFrame to release memory
-        del df
+    alpha_vantage_data = fetch_alpha_vantage_data(api_key)
+
+    # 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)
+
+    # Create predictor
+    my_market_predictor = Pandas_Market_Predictor(df)
+
+    # Predict Trend
+    indicators = ["Indicator1", "Indicator2"]
+    trend = my_market_predictor.Trend_Detection(indicators, 10)
+
+    # Display results
+    st.subheader("Predicted Trend:")
+    st.write("Buy Trend :", trend['BUY'])
+    st.write("Sell Trend :", trend['SELL'])
+    st.write(f"Standard Deviation Percentage: {my_market_predictor.PERCENT_STD}%")
+
+    # Delete the DataFrame to release memory
+    del df
 
 if __name__ == "__main__":
     main()