Update app.py

app.py

@@ -43,13 +43,20 @@ def main():
 if __name__ == "__main__":
     main()
 
+
 # Hard-coded API key for demonstration purposes
 API_KEY = "QR8F9B7T6R2SWTAT"
 
-def fetch_alpha_vantage_data(api_key, symbol):
+def fetch_alpha_vantage_data(api_key, symbol, progress_bar):
     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()
+    
+    # Simulate progress with a fake sleep
+    for percent_complete in range(0, 101, 10):
+        progress_bar.progress(percent_complete)
+        st.experimental_rerun()
+
     return alpha_vantage_data
 
 def calculate_indicators(data):
@@ -66,13 +73,14 @@ def display_signals(signal_type, signals):
     st.write(signals)
 
 def main():
-    st.title("Strategical Trend Predictor")
+    st.title("AI Stock Trend Predictor")
 
     # Input for stock symbol
     symbol = st.text_input("Enter stock symbol (e.g., AAPL):", "AAPL")
 
     # Fetch Alpha Vantage data
-    alpha_vantage_data = fetch_alpha_vantage_data(API_KEY, symbol)
+    progress_bar_fetch_data = st.progress(0)
+    alpha_vantage_data = fetch_alpha_vantage_data(API_KEY, symbol, progress_bar_fetch_data)
 
     # Extract relevant data from Alpha Vantage response
     alpha_vantage_time_series = alpha_vantage_data.get('Time Series (5min)', {})
@@ -83,18 +91,33 @@ def main():
     # Rename columns
     df = df.rename(columns={'1. open': 'Open', '2. high': 'High', '3. low': 'Low', '4. close': 'Close', '5. volume': 'Volume'})
 
+    # Simulate progress with a fake sleep
+    for percent_complete in range(0, 101, 10):
+        progress_bar_process_data = st.progress(percent_complete)
+        st.experimental_rerun()
+
     # Calculate indicators
     df = calculate_indicators(df)
 
     # Display stock trading signals
     strategic_signals = StrategicSignals(symbol=symbol)
 
+    # Simulate progress with a fake sleep
+    for percent_complete in range(0, 101, 10):
+        progress_bar_display_signals = st.progress(percent_complete)
+        st.experimental_rerun()
+
     display_signals("Bollinger Bands", strategic_signals.get_bollinger_bands_signals())
     display_signals("Breakout", strategic_signals.get_breakout_signals())
     display_signals("Crossover", strategic_signals.get_crossover_signals())
     display_signals("MACD", strategic_signals.get_macd_signals())
     display_signals("RSI", strategic_signals.get_rsi_signals())
 
+    # Simulate progress with a fake sleep
+    for percent_complete in range(0, 101, 10):
+        progress_bar_predict_trend = st.progress(percent_complete)
+        st.experimental_rerun()
+
     # Create predictor
     my_market_predictor = Pandas_Market_Predictor(df)
 
@@ -102,13 +125,16 @@ def main():
     indicators = ["Doji", "Inside"]
     trend = my_market_predictor.Trend_Detection(indicators, 10)
 
-    st.title("AI Stock Trend Predictor")
-    
     # Display trend predictions
     st.subheader("Predicted Trend:")
     st.write("Buy Trend :", trend['BUY'])
     st.write("Sell Trend :", trend['SELL'])
 
+    # Simulate progress with a fake sleep
+    for percent_complete in range(0, 101, 10):
+        progress_bar_complete = st.progress(percent_complete)
+        st.experimental_rerun()
+
     # Delete the DataFrame to release memory
     del df