Update app.py

app.py

@@ -6,7 +6,7 @@ import pandas as pd
 import logging
 
 # Set up logging
-logging.basicConfig(level=logging.INFO)
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
 def fetch_eth_price(period, past_data_limit=None):
     eth = yf.Ticker("ETH-USD")
@@ -27,40 +27,71 @@ def fetch_eth_price(period, past_data_limit=None):
         predict_steps = 720  # Next 30 days
         freq = 'H'  # Hourly frequency
     else:
+        logging.error("Invalid period selected.")
         return None, None, None
     
     data.index = pd.DatetimeIndex(data.index)
     data = data.asfreq(freq)  # Ensure the data has a consistent frequency
 
     # Limit the amount of past data used for training
-    if past_data_limit is not None:
+    if past_data_limit is not None and len(data) > past_data_limit:
         data = data.iloc[-past_data_limit:]
-    
+
+    if data.empty:
+        logging.error("Data is empty after applying past data limit.")
+        return None, None, None
+
+    logging.info(f"Fetched {len(data)} data points for period '{period}'")
     return data, predict_steps, freq
 
 def make_predictions(data, predict_steps, freq):
+    if data is None or data.empty:
+        logging.error("No data available for model training.")
+        return None
+
     logging.info("Starting model training...")
     model = ARIMA(data['Close'], order=(5, 1, 0))
-    model_fit = model.fit()
+    try:
+        model_fit = model.fit()
+    except Exception as e:
+        logging.error(f"Model training failed: {e}")
+        return None
+    
     logging.info("Model training completed.")
     
     logging.info("Generating predictions...")
-    forecast = model_fit.forecast(steps=predict_steps)
+    try:
+        forecast = model_fit.forecast(steps=predict_steps)
+    except Exception as e:
+        logging.error(f"Prediction generation failed: {e}")
+        return None
+
     future_dates = pd.date_range(start=data.index[-1], periods=predict_steps+1, freq=freq, inclusive='right')
     forecast_df = pd.DataFrame(forecast, index=future_dates[1:], columns=['Prediction'])
-    logging.info("Predictions generated.")
-    
+    logging.info("Predictions generated successfully.")
+
     return forecast_df
 
 def plot_eth(period):
-    # Use a past data limit to speed up the process (e.g., 500 data points)
+    # Limit the data to the last 500 points for faster processing
     data, predict_steps, freq = fetch_eth_price(period, past_data_limit=500)
+    
+    if data is None or predict_steps is None or freq is None:
+        logging.error("Failed to fetch data or invalid period selected.")
+        return go.Figure().update_layout(title="Error: No data available")
+    
     forecast_df = make_predictions(data, predict_steps, freq)
     
+    if forecast_df is None or forecast_df.empty:
+        logging.error("Prediction dataframe is empty or None.")
+        return go.Figure().update_layout(title="Error: Prediction failed")
+    
     fig = go.Figure()
     fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='ETH Price'))
     fig.add_trace(go.Scatter(x=forecast_df.index, y=forecast_df['Prediction'], mode='lines', name='Prediction', line=dict(dash='dash')))
     fig.update_layout(title=f"ETH Price and Predictions ({period})", xaxis_title="Date", yaxis_title="Price (USD)")
+
+    logging.info("Plot updated with predictions.")
     
     return fig