Use threading for python. and make singals from python

WebServer/AIPython/ai-predictor.py

@@ -8,18 +8,20 @@ import features
 import matplotlib
 matplotlib.use("Agg")
 
-def Predict():
+def Predict(Symbol):
     # 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("NVDA", period="2mo", auto_adjust=True)
+    df = yf.download(Symbol, period="2mo", auto_adjust=True)
     if not df.empty:
         df = features.MakeFeatures(df, 1)
         df = features.CleanDF(df)
 
+    print(Symbol)
+
     # Drop our predictor
     df.drop('Target_Close', axis=1, inplace=True)
 
@@ -50,7 +52,15 @@ def Predict():
 
     print(f"Predicted Target_Close: {flat_predictions}")
 
-    return json.dumps(flat_predictions)
+    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 movement_indicator
 
 if __name__ == "__main__":
     Predict()