Update app.py

app.py

@@ -3,8 +3,12 @@ import yfinance as yf
 import plotly.graph_objects as go
 from statsmodels.tsa.arima.model import ARIMA
 import pandas as pd
+import logging
 
-def fetch_eth_price(period):
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+
+def fetch_eth_price(period, past_data_limit=None):
     eth = yf.Ticker("ETH-USD")
     if period == '1d':
         data = eth.history(period="1d", interval="1m")
@@ -27,20 +31,30 @@ def fetch_eth_price(period):
     
     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:
+        data = data.iloc[-past_data_limit:]
+    
     return data, predict_steps, freq
 
 def make_predictions(data, predict_steps, freq):
+    logging.info("Starting model training...")
     model = ARIMA(data['Close'], order=(5, 1, 0))
     model_fit = model.fit()
-    forecast = model_fit.forecast(steps=predict_steps)
+    logging.info("Model training completed.")
     
+    logging.info("Generating predictions...")
+    forecast = model_fit.forecast(steps=predict_steps)
     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.")
     
     return forecast_df
 
 def plot_eth(period):
-    data, predict_steps, freq = fetch_eth_price(period)
+    # Use a past data limit to speed up the process (e.g., 500 data points)
+    data, predict_steps, freq = fetch_eth_price(period, past_data_limit=500)
     forecast_df = make_predictions(data, predict_steps, freq)
     
     fig = go.Figure()