mini changes in predict pipeline

predict_pipeline/config.yaml

@@ -1,20 +1,20 @@
 database:
-  path: "C:\\repo\\Fahrsimulator_MSY2526_AI\\predict_pipeline\\database.sqlite"
+  path: "/home/edgekit/MSY_FS/databases/database.sqlite"
   table: feature_table
   key: _Id
 
 model:
-  path: "C:\\repo\\Fahrsimulator_MSY2526_AI\\files_for_testing\\xgb_model_3_groupK.joblib"
+  path: "/home/edgekit/MSY_FS/fahrsimulator_msy2526_ai/predict_pipeline/xgb_model_3_groupK.joblib"
 
 scaler:
-  use_scaling: True
-  path: "C:\\repo\\Fahrsimulator_MSY2526_AI\\predict_pipeline\\normalizer_min_max_global.pkl"
+  use_scaling: true
+  path: "/home/edgekit/MSY_FS/fahrsimulator_msy2526_ai/predict_pipeline/normalizer_min_max_global.pkl"
 
 mqtt:
   enabled: true
   host: "141.75.215.233"
   port: 1883
-  topic: "PREDICTIONS"
+  topic: "PREDICTION"
   client_id: "jetson-board"
   qos: 0
   retain: false
@@ -107,4 +107,4 @@ fallback:
   Blink_mean_dur: 0.38857142857142857
   Blink_median_dur: 0.2
   Pupil_mean: 3.2823675201416016
-  Pupil_IPA: 0.0036347377340156025
+  Pupil_IPA: 0.0036347377340156025

predict_pipeline/predict_sample.py

@@ -7,9 +7,10 @@ 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")
+
 import db_helpers
 import joblib
+import paho.mqtt.client as mqtt
 
 def _load_serialized(path: Path):
     suffix = path.suffix.lower()
@@ -52,11 +53,11 @@ 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:
@@ -125,44 +126,37 @@ def sendMessage(config_file_path, message):
 
     # Serialize the message to JSON
     payload = json.dumps(message, ensure_ascii=False)
-    print(payload)
+    print(payload) # for debugging purposes
 
-    # Later: publish via MQTT using config parameters above.
-    # Example (kept commented intentionally):
-    # import paho.mqtt.client as mqtt
-    # client = mqtt.Client(client_id=mqtt_cfg.get("client_id", "predictor-01"))
-    # if "username" in mqtt_cfg and mqtt_cfg.get("username"):
-    #     client.username_pw_set(mqtt_cfg["username"], mqtt_cfg.get("password"))
-    # client.connect(mqtt_cfg.get("host", "localhost"), int(mqtt_cfg.get("port", 1883)), 60)
-    # client.publish(
-    #     topic=topic,
-    #     payload=payload,
-    #     qos=int(mqtt_cfg.get("qos", 1)),
-    #     retain=bool(mqtt_cfg.get("retain", False)),
-    # )
-    # client.disconnect()
+    
+    client = mqtt.Client(client_id=mqtt_cfg.get("client_id", "predictor-01"))
+    if "username" in mqtt_cfg and mqtt_cfg.get("username"):
+        client.username_pw_set(mqtt_cfg["username"], mqtt_cfg.get("password"))
+    client.connect(mqtt_cfg.get("host", "localhost"), int(mqtt_cfg.get("port", 1883)), 60)
+    client.publish(
+        topic=topic,
+        payload=payload,
+        qos=int(mqtt_cfg.get("qos", 1)),
+        retain=bool(mqtt_cfg.get("retain", False)),
+    )
+    client.disconnect()
     return
 
 def replace_nan(sample, config_file_path: Path):
     with config_file_path.open("r", encoding="utf-8") as f:
         cfg = yaml.safe_load(f)
-
-    fallback_list = cfg.get("fallback", [])
-    fallback_map = {}
-    for item in fallback_list:
-        if isinstance(item, dict):
-            fallback_map.update(item)
-
+    
+    fallback_map = cfg.get("fallback", {})
+    
     if sample.empty:
         return False, sample
-
+    
     nan_ratio = sample.isna().mean()
     valid = nan_ratio <= 0.5
-
+    
     if valid and fallback_map:
         sample = sample.fillna(value=fallback_map)
-
-
+    
     return valid, sample
 
 def sample_to_numpy(sample, drop_cols=("_Id", "start_time")):
@@ -213,7 +207,7 @@ def scale_sample(sample, use_scaling=False, scaler_path=None):
     return df.iloc[0] if isinstance(sample, pd.Series) else df
 
 def main():
-    pd.set_option('future.no_silent_downcasting', True) # kann ggf raus
+    pd.set_option('future.no_silent_downcasting', True) 
 
     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: