Scale the data before learning to normalize the output

WebServer/AIPython/ai-predictor.py

@@ -1,5 +1,6 @@
 import os
 import json
+import joblib
 import numpy as np
 os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
 import yfinance as yf
@@ -31,11 +32,21 @@ def Predict():
     # Verify it loaded correctly
     reconstructed_model.summary()
 
+    # Load the scalers
+    feature_scaler = joblib.load(os.path.join(DATA_DIR, "feature_scaler.pkl"))
+    target_scaler = joblib.load(os.path.join(DATA_DIR, "target_scaler.pkl"))
+
+    # Scale the data
+    scaled_data = feature_scaler.transform(df)
+
     # Predict
-    predictions = reconstructed_model.predict(df)
+    scaled_predictions = reconstructed_model.predict(scaled_data)
+
+    # Use the loaded target scaler to get back to % change
+    actual_prediction = target_scaler.inverse_transform(scaled_predictions)
 
     # 'predictions' will be a 2D array, flatten it if you want a simple list
-    flat_predictions = predictions.flatten().tolist()
+    flat_predictions = actual_prediction.flatten().tolist()
 
     print(f"Predicted Target_Close: {flat_predictions}")