Update utils.py

utils.py

@@ -6,9 +6,7 @@ from datetime import datetime, timedelta
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots
 import spaces
-# Impor Chronos2Pipeline untuk model amazon/chronos-2
-from chronos import Chronos2Pipeline 
-
+from chronos import Chronos2Pipeline
 
 def get_indonesian_stocks():
     return {
@@ -159,27 +157,30 @@ def format_large_number(num):
 @spaces.GPU(duration=120)
 def predict_prices(data, model=None, tokenizer=None, prediction_days=30):
     try:
-        # PENGGANTIAN: Gunakan Chronos2Pipeline dan model amazon/chronos-2
         pipeline = Chronos2Pipeline.from_pretrained("amazon/chronos-2", device_map="auto")
         
-        # Chronos-2 membutuhkan input dalam format DataFrame (predict_df)
         context_df = data[['Close']].reset_index()
         context_df.columns = ['timestamp', 'target'] 
         context_df['id'] = 'stock_price' 
 
+        # Fix Error: Could not infer frequency (membuat data menjadi frekuensi harian)
+        context_df['timestamp'] = pd.to_datetime(context_df['timestamp'])
+        context_df = context_df.set_index('timestamp').asfreq('D').fillna(method='ffill').reset_index()
+        
+        context_df['id'] = 'stock_price' 
+        context_df.columns = ['timestamp', 'target', 'id']
+
+
         with torch.no_grad():
-            # Menggunakan predict_df()
             pred_df = pipeline.predict_df(
                 context_df,
                 prediction_length=prediction_days,
                 id_column="id",
                 timestamp_column="timestamp",
                 target="target",
-                # Kita ambil kuantil 0.5 (median) sebagai prediksi mean
                 quantile_levels=[0.5] 
             )
 
-        # Ekstraksi hasil prediksi dari DataFrame
         mean_forecast = pred_df['target_0.5'].values.astype(np.float32)
         predicted_dates = pred_df['timestamp']
         
@@ -189,14 +190,10 @@ def predict_prices(data, model=None, tokenizer=None, prediction_days=30):
         predicted_mean = float(np.mean(mean_forecast))
         change_pct = ((predicted_mean - last_price) / last_price) * 100 if last_price != 0 else 0
         
-        # Perbarui summary model
         return {'values': mean_forecast, 'dates': predicted_dates, 'high_30d': predicted_high, 'low_30d': predicted_low, 'mean_30d': predicted_mean, 'change_pct': change_pct, 'summary': f"AI Model: Amazon Chronos-2\nPredicted High: {predicted_high:.2f}\nPredicted Low: {predicted_low:.2f}\nExpected Change: {change_pct:.2f}%"}
         
     except Exception as e:
-        # Error handling yang lebih baik
         error_message = f'Model error: {e}'
-        if "context_tensor" in str(e) or "context" in str(e) or "pipeline" in str(e):
-             error_message = f"Prediction API Error (Chronos-2): Cek instalasi 'chronos-forecasting' dan argumen di predict_prices(). Detail: {e}"
         print(f"Error in prediction: {e}")
         return {'values': [], 'dates': [], 'high_30d': 0, 'low_30d': 0, 'mean_30d': 0, 'change_pct': 0, 'summary': error_message}
 
@@ -206,8 +203,6 @@ def create_prediction_chart(data, predictions):
     fig = go.Figure()
     fig.add_trace(go.Scatter(x=data.index[-60:], y=data['Close'].values[-60:], name='Historical Price', line=dict(color='blue', width=2)))
     fig.add_trace(go.Scatter(x=predictions['dates'], y=predictions['values'], name='AI Prediction', line=dict(color='red', width=2, dash='dash')))
-    # Perhitungan band harus menggunakan prediksi yang sudah dikuantil (jika ada) atau distandardisasi
-    # Karena kita hanya menggunakan satu kuantil (0.5), kita asumsikan pred_std kecil
     pred_std = np.std(predictions['values']) if len(predictions['values']) > 1 else 0.05 * predictions['values'][0]
     upper_band = predictions['values'] + (pred_std * 1.96)
     lower_band = predictions['values'] - (pred_std * 1.96)