Build the AI predictor off the features so they are the same as the training model

WebServer/AIPython/ai-predictor.py

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+import os
+import yfinance as yf
+import features
+import matplotlib
+matplotlib.use("Agg")
+
+def Predict():
+    # Define paths (consistent with your previous script)
+    SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
+    DATA_DIR = os.path.join(SCRIPT_DIR, "data")
+    MODEL_PATH = os.path.join(DATA_DIR, "model.keras")
+
+    # Pull 1 month of current data to make prediction against | for volatility 20
+    df = yf.download("SPY", period="2mo", auto_adjust=True)
+    if not df.empty:
+        df = features.MakeFeatures(df, 1)
+        df = features.CleanDF(df)
+
+    # Drop our predictor
+    df.drop('Volatility_5', axis=1, inplace=True)
+
+    # Lazy load this so it doesnt interfere with yfinance
+    from keras.models import load_model
+
+    # Load the model
+    reconstructed_model = load_model(MODEL_PATH)
+
+    # Verify it loaded correctly
+    reconstructed_model.summary()
+
+    # Predict
+    predictions = reconstructed_model.predict(df)
+
+    # 'predictions' will be a 2D array, flatten it if you want a simple list
+    flat_predictions = predictions.flatten()
+
+    print(f"Predicted Volatility: {flat_predictions}")
+    
+    return flat_predictions