Move away from Python Interop for better stability

WebServer/AIPython/aipredictor.py

@@ -0,0 +1,72 @@
+import os
+import sys
+import joblib
+import numpy as np
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+import yfinance as yf
+import features
+import matplotlib
+matplotlib.use("Agg")
+
+def Predict():
+
+    # Get the Symbol from ARGV
+    Symbol = sys.argv[1]
+
+    # 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(Symbol, period="2mo", auto_adjust=True)
+    if not df.empty:
+        # Remove the ticker column
+        df.columns = df.columns.get_level_values(0)
+        # Make the feature set
+        df = features.MakeFeatures(df)
+
+    # Drop our predictor
+    df.drop('Target_Close', 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()
+
+    # 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
+    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 = actual_prediction.flatten().tolist()
+
+    # Set the movement indicator
+    movement_indicator = 0
+    if (np.mean(flat_predictions) > 0.01):
+        movement_indicator = 1
+    elif (np.mean(flat_predictions) < -0.01):
+        movement_indicator = -1
+    else:
+        movement_indicator = 0
+
+    # Return to C# via stdout
+    print(f"---RESULT_START---")
+    print(movement_indicator)
+    print(f"---RESULT_END---")
+
+if __name__ == "__main__":
+    Predict()