Update src/components/model_timeseries_risk.py

src/components/model_timeseries_risk.py

@@ -4,10 +4,7 @@ import tensorflow as tf
 import joblib
 import requests
 from io import BytesIO
-import logging
 
-logger = logging.getLogger(__name__)
-logging.basicConfig(level=logging.INFO)
 
 # ----------------------------
 # Load model and scaler from HF Hub
@@ -41,7 +38,7 @@ region_name = "Shanghai"
 
 df_region = df[df[region_col] == region_name].copy()
 if len(df_region) < 100:
-    logger.warning("Region sample is small, upsampling/cropping to 200 rows from full dataset.")
+    
     df_region = df.sample(200, random_state=42) if len(df) >= 200 else df
 
 feature_cols = [
@@ -62,7 +59,7 @@ for i in range(len(X_scaled) - seq_length):
     y_seq.append(y_all[i+seq_length])
 X_seq = np.array(X_seq)
 y_seq = np.array(y_seq)
-logger.info(f"Sequence shape: {X_seq.shape}; Labels: {y_seq.shape}")
+
 
 # ----------------------------
 # Train/test split (optional)
@@ -75,7 +72,7 @@ y_train, y_test = y_seq[:-test_size], y_seq[-test_size:]
 # Evaluate using loaded model
 # ----------------------------
 test_loss, test_acc = model.evaluate(X_test, y_test)
-logger.info(f"Test Accuracy: {test_acc:.4f}")
+
 
 # ----------------------------
 # Prediction function
@@ -85,7 +82,7 @@ def predict_risk_for_next_day(sequence, threshold=0.5):
     seq_window = np.expand_dims(seq_scaled, axis=0)
     pred_prob = model.predict(seq_window)[0][0]
     pred_label = int(pred_prob > threshold)
-    logger.info(f"Predicted next-day risk score: {pred_prob:.3f} (region: {region_name}), label: {pred_label}")
+    
     return pred_prob, pred_label
 
 # Example usage