Update app.py

app.py

@@ -152,7 +152,7 @@ def get_data_predict(
 
     btc_data_ = remove_outliers(btc_data_, epsilon)
     bch_data_ = remove_outliers(bch_data_, epsilon)
-    print(btc_data_)
+    # print(btc_data_.head)
 
     if normalized:
         # merge with ori if you still want to include historical yf data
@@ -202,17 +202,18 @@ with open('model_n4h_cat.pkl','rb') as f:
 def predict_and_plot(timeframe, limit, epsilon, n_steps, ma):
     period = f'{limit}d'
     # original “ori” series now also from yfinance
-    btc_ori = fetch_yfinance_data('BTC/USDT', period, timeframe)
-    bch_ori = fetch_yfinance_data('BCH/USDT', period, timeframe)
-
-    btc_data, bch_data, label = get_data_predict(
-        btc_ori, bch_ori,
-        symbol='BCH/USDT',
-        timeframe=timeframe,
-        epsilon=epsilon,
-        normalized=True,
-        limit=limit
-    )
+    btc_data = fetch_yfinance_data('BTC/USDT', period, timeframe)
+    bch_data = fetch_yfinance_data('BCH/USDT', period, timeframe)
+    btc_data, _ = normalize(btc_data)
+    bch_data, _ = normalize(bch_data)
+    # btc_data, bch_data, label = get_data_predict(
+    #     btc_ori, bch_ori,
+    #     symbol='BCH/USDT',
+    #     timeframe=timeframe,
+    #     epsilon=epsilon,
+    #     normalized=True,
+    #     limit=limit
+    # )
     model = model_n1d_cat if timeframe=='1d' else model_n4h_cat
     preds = predictions(model, btc_data, bch_data, name=timeframe, n_steps=n_steps)
     fig = plot(preds, label=label, timeframe=timeframe, ma=ma, n_steps=n_steps)