current files from jetson board

model_training/DeepSVDD/deepSVDD.ipynb

@@ -895,18 +895,6 @@
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.10"
   }
  },
  "nbformat": 4,

model_training/OCSVM/ocsvm_with_AE.ipynb

@@ -705,18 +705,6 @@
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.10"
   }
  },
  "nbformat": 4,

predict_pipeline/config.yaml

@@ -4,15 +4,15 @@ database:
   key: _Id
 
 model:
-  path: "/home/edgekit/MSY_FS/fahrsimulator_msy2526_ai/predict_pipeline/xgb_model_3_groupK.joblib"
+  path: "/home/edgekit/MSY_FS/fahrsimulator_msy2526_ai/predict_pipeline/cnn_crossVal_EarlyFusion_V2_0103.keras"
 
 scaler:
   use_scaling: true
-  path: "/home/edgekit/MSY_FS/fahrsimulator_msy2526_ai/predict_pipeline/normalizer_min_max_global.pkl"
+  path: "/home/edgekit/MSY_FS/fahrsimulator_msy2526_ai/predict_pipeline/scaler_crossVal_EarlyFusion_V2_0103.joblib"
 
 mqtt:
   enabled: true
-  host: "141.75.215.233"
+  host: "141.75.223.13"
   port: 1883
   topic: "PREDICTION"
   client_id: "jetson-board"
@@ -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.service

@@ -5,7 +5,7 @@ StartLimitIntervalSec=0
 [Service]
 Type=oneshot
 User=edgekit
-ExecStart=~/anaconda3/envs/p310_FS/bin/python /home/edgekit/MSY_FS/fahrsimulator_msy2526_ai/predict_pipeline/predict_sample.py
+ExecStart=/home/edgekit/anaconda3/envs/p310_FS_TF/bin/python /home/edgekit/MSY_FS/fahrsimulator_msy2526_ai/predict_pipeline/predict_sample.py
 
 [Install]
-WantedBy=multi-user.target
+WantedBy=multi-user.target

predict_pipeline/predict.timer

@@ -2,10 +2,11 @@
 Description=Run predict sample every 5 seconds
 
 [Timer]
-OnBootSec=5
+OnActiveSec=60
 OnUnitActiveSec=5
 AccuracySec=1s
 Unit=predict.service
 
 [Install]
 WantedBy=timers.target
+

predict_pipeline/predict_sample.py

@@ -7,7 +7,6 @@ import sys
 import yaml
 import pickle
 sys.path.append('/home/edgekit/MSY_FS/fahrsimulator_msy2526_ai/tools')
-
 import db_helpers
 import joblib
 import paho.mqtt.client as mqtt
@@ -54,8 +53,9 @@ def callModel(sample, model_path):
     if suffix in {".pkl", ".joblib"}:
         model = _load_serialized(model_path)
     elif suffix == ".keras":
-        import tensorflow as tf
+        import tensorflow
-        model = tf.keras.models.load_model(model_path)
+        tensorflow.get_logger().setLevel("ERROR")
+        model = tensorflow.keras.models.load_model(model_path)
     else:
         raise ValueError(f"Unsupported model format: {suffix}. Use .pkl, .joblib, or .keras.")
 
@@ -65,9 +65,7 @@ def callModel(sample, model_path):
 
     if suffix == ".keras":
         x_full = x  
-        # Future model (35 features): keep this call when your new model is active.
+        prediction = (model.predict(x_full[:, :35], verbose=0) > 0.5).astype(int)
-        # prediction = model.predict(x_full[:, :35], verbose=0)
-        prediction = model.predict(x_full[:, :20], verbose=0)
 
     else:
         if hasattr(model, "predict"):
@@ -126,10 +124,12 @@ def sendMessage(config_file_path, message):
 
     # Serialize the message to JSON
     payload = json.dumps(message, ensure_ascii=False)
-    print(payload) # for debugging purposes
+    print(payload)
 
-    
+    # publish via MQTT using config parameters above.    
-    client = mqtt.Client(client_id=mqtt_cfg.get("client_id", "predictor-01"))
+    client = mqtt.Client(client_id=mqtt_cfg.get("client_id", "predictor-01"),
+                          callback_api_version=mqtt.CallbackAPIVersion.VERSION2)
+    callback_api_version=mqtt.CallbackAPIVersion.VERSION2
     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)
@@ -207,7 +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) 
+    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:

predict_pipeline/predict_service_timer_documentation.md

@@ -0,0 +1,216 @@
+# Predict Service and Timer Documentation
+
+## Overview
+
+This setup uses **systemd services and timers** to repeatedly execute a
+Python script that performs prediction on the latest sample and sends a
+message.
+
+The systemd unit files are typically stored in:
+
+    /etc/systemd/system/
+
+For this setup, the relevant files are:
+
+    /etc/systemd/system/predict.service
+    /etc/systemd/system/predict.timer
+
+These files define the service execution and the timer scheduling.
+
+-   `predict.service` -- defines how the Python script is executed
+-   `predict.timer` -- schedules the repeated execution of the service
+
+The timer triggers the service **every 5 seconds** after the first
+activation.
+
+------------------------------------------------------------------------
+
+# Systemd Timer
+
+File: `predict.timer`
+
+``` ini
+[Unit]
+Description=Run predict sample every 5 seconds
+
+[Timer]
+OnActiveSec=60
+OnUnitActiveSec=5
+AccuracySec=1s
+Unit=predict.service
+
+[Install]
+WantedBy=timers.target
+```
+
+## Behavior
+
+-   **OnActiveSec=60**\
+    The timer starts **60 seconds after it is activated**.
+
+-   **OnUnitActiveSec=5**\
+    After the service has run once, it will be triggered again **every 5
+    seconds**.
+
+-   **AccuracySec=1s**\
+    Allows systemd to schedule the timer with **1 second precision**.
+
+-   **Unit=predict.service**\
+    Defines which service should be triggered by the timer.
+
+------------------------------------------------------------------------
+
+# Systemd Service
+
+File: `predict.service`
+
+``` ini
+[Unit]
+Description=Predict latest sample and send message
+After=network.target
+StartLimitIntervalSec=0
+
+[Service]
+Type=oneshot
+User=edgekit
+ExecStart=/home/edgekit/anaconda3/envs/p310_FS_TF/bin/python /home/edgekit/MSY_FS/fahrsimulator_msy2526_ai/predict_pipeline/predict_sample.py
+
+[Install]
+WantedBy=multi-user.target
+```
+
+## Behavior
+
+-   **Type=oneshot**\
+    The service runs the script once and then exits.
+
+-   **User=edgekit**\
+    The script is executed under the `edgekit` user.
+
+-   **ExecStart**\
+    Executes the Python script using the specified conda environment.
+
+-   **After=network.target**\
+    Ensures the service only runs after the network is available.
+
+------------------------------------------------------------------------
+
+# Execution Flow
+
+1.  The **timer starts** after it is enabled.
+2.  After **60 seconds**, the first execution happens (this results from the duration of the camera processing initialization)
+3.  The timer triggers `predict.service`.
+4.  The service runs `predict_sample.py`.
+5.  Once the script finishes, the service exits.
+6.  The timer triggers the service again **every 5 seconds**.
+
+------------------------------------------------------------------------
+
+# Debugging and Monitoring
+
+## View Live Output
+
+All `print()` output from the Python script is written to the **systemd
+journal**.
+
+Follow the output live with:
+
+``` bash
+journalctl -u predict.service -f
+```
+
+This command is typically the most useful for debugging.
+
+------------------------------------------------------------------------
+
+# Common Systemd Commands
+
+## Check Service Status
+
+``` bash
+systemctl status predict.service
+```
+
+Shows the last execution result and recent log lines.
+
+------------------------------------------------------------------------
+
+## Check Timer Status
+
+``` bash
+systemctl status predict.timer
+```
+
+Shows when the timer last ran and when it will run next.
+
+------------------------------------------------------------------------
+
+## List All Timers
+
+``` bash
+systemctl list-timers
+```
+
+Displays all active timers and their next scheduled execution.
+
+------------------------------------------------------------------------
+
+# Manual Execution
+
+To run the service manually once:
+
+``` bash
+systemctl start predict.service
+```
+
+------------------------------------------------------------------------
+
+# Restarting the Systemd Units
+
+## Restart the Service
+
+``` bash
+systemctl restart predict.service
+```
+
+## Restart the Timer
+
+``` bash
+systemctl restart predict.timer
+```
+
+------------------------------------------------------------------------
+
+## Reload Systemd After Changes
+
+If `.service` or `.timer` files were modified:
+
+``` bash
+systemctl daemon-reload
+systemctl restart predict.timer
+systemctl restart predict.service
+```
+
+------------------------------------------------------------------------
+
+## Enabling the Timer
+
+To ensure the timer starts automatically on system boot:
+
+``` bash
+systemctl enable predict.timer
+systemctl start predict.timer
+```
+
+------------------------------------------------------------------------
+
+# Summary
+
+-   `predict.timer` schedules periodic execution.
+-   `predict.service` runs the Python prediction script.
+-   The script runs **every 5 seconds** after the initial delay.
+-   Logs and script output are available through:
+
+``` bash
+journalctl -u predict.service -f
+```