adjusted paths (this is the deployment setting)

predict_pipeline/predict_sample.py

@@ -6,8 +6,8 @@ import numpy as np
 import sys
 import yaml
 import pickle
-#sys.path.append('/home/edgekit/MSY_FS/fahrsimulator_msy2526_ai/tools')
-sys.path.append(r"c:\\repo\\Fahrsimulator_MSY2526_AI\\tools")
+sys.path.append('/home/edgekit/MSY_FS/fahrsimulator_msy2526_ai/tools')
+# sys.path.append(r"c:\\repo\\Fahrsimulator_MSY2526_AI\\tools")
 import db_helpers
 import joblib
 
@@ -52,11 +52,11 @@ def callModel(sample, model_path):
     suffix = model_path.suffix.lower()
     if suffix in {".pkl", ".joblib"}:
         model = _load_serialized(model_path)
-    elif suffix == ".keras":
-        import tensorflow as tf
-        model = tf.keras.models.load_model(model_path)
-    else:
-        raise ValueError(f"Unsupported model format: {suffix}. Use .pkl, .joblib, or .keras.")
+    # elif suffix == ".keras":
+       # import tensorflow as tf
+        # model = tf.keras.models.load_model(model_path)
+    # else:
+        # raise ValueError(f"Unsupported model format: {suffix}. Use .pkl, .joblib, or .keras.")
 
     x = np.asarray(sample, dtype=np.float32)
     if x.ndim == 1:
@@ -101,13 +101,29 @@ def buildMessage(valid, result: np.int32, config_file_path, sample=None):
     }
     return message
 
+def convert_int64(obj):
+    if isinstance(obj, np.int64):
+        return int(obj)
+    # If the object is a dictionary or list, recursively convert its values
+    elif isinstance(obj, dict):
+        return {key: convert_int64(value) for key, value in obj.items()}
+    elif isinstance(obj, list):
+        return [convert_int64(item) for item in obj]
+    return obj
+
 def sendMessage(config_file_path, message):
+    # Load the configuration
     with Path(config_file_path).open("r", encoding="utf-8") as f:
         cfg = yaml.safe_load(f)
 
+    # Get MQTT configuration
     mqtt_cfg = cfg.get("mqtt", {})
     topic = mqtt_cfg.get("topic", "ml/predictions")
 
+    # Convert message to ensure no np.int64 values remain
+    message = convert_int64(message)
+
+    # Serialize the message to JSON
     payload = json.dumps(message, ensure_ascii=False)
     print(payload)
 
@@ -197,7 +213,9 @@ def scale_sample(sample, use_scaling=False, scaler_path=None):
     return df.iloc[0] if isinstance(sample, pd.Series) else df
 
 def main():
-    config_file_path = Path("predict_pipeline/config.yaml")
+    pd.set_option('future.no_silent_downcasting', True) # kann ggf raus
+
+    config_file_path = Path("/home/edgekit/MSY_FS/fahrsimulator_msy2526_ai/predict_pipeline/config.yaml")
     with config_file_path.open("r", encoding="utf-8") as f:
         cfg = yaml.safe_load(f)