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adjusted paths (this is the deployment setting)

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Michael Weig 2026-02-16 20:11:07 +00:00
parent 2b01085a9e
commit 4eab3c9876
1 changed files with 253 additions and 235 deletions
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34
predict_pipeline/predict_sample.py
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@ -6,8 +6,8 @@ import numpy as np
import sys import sys
import yaml import yaml
import pickle import pickle
#sys.path.append('/home/edgekit/MSY_FS/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") # sys.path.append(r"c:\\repo\\Fahrsimulator_MSY2526_AI\\tools")
import db_helpers import db_helpers
import joblib import joblib
@ -52,11 +52,11 @@ def callModel(sample, model_path):
suffix = model_path.suffix.lower() suffix = model_path.suffix.lower()
if suffix in {".pkl", ".joblib"}: if suffix in {".pkl", ".joblib"}:
model = _load_serialized(model_path) model = _load_serialized(model_path)
elif suffix == ".keras": # elif suffix == ".keras":
import tensorflow as tf # import tensorflow as tf
model = tf.keras.models.load_model(model_path) # model = tf.keras.models.load_model(model_path)
else: # else:
raise ValueError(f"Unsupported model format: {suffix}. Use .pkl, .joblib, or .keras.") # raise ValueError(f"Unsupported model format: {suffix}. Use .pkl, .joblib, or .keras.")
x = np.asarray(sample, dtype=np.float32) x = np.asarray(sample, dtype=np.float32)
if x.ndim == 1: if x.ndim == 1:
@ -101,13 +101,29 @@ def buildMessage(valid, result: np.int32, config_file_path, sample=None):
} }
return message 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): def sendMessage(config_file_path, message):
# Load the configuration
with Path(config_file_path).open("r", encoding="utf-8") as f: with Path(config_file_path).open("r", encoding="utf-8") as f:
cfg = yaml.safe_load(f) cfg = yaml.safe_load(f)
# Get MQTT configuration
mqtt_cfg = cfg.get("mqtt", {}) mqtt_cfg = cfg.get("mqtt", {})
topic = mqtt_cfg.get("topic", "ml/predictions") 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) payload = json.dumps(message, ensure_ascii=False)
print(payload) 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 return df.iloc[0] if isinstance(sample, pd.Series) else df
def main(): 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: with config_file_path.open("r", encoding="utf-8") as f:
cfg = yaml.safe_load(f) cfg = yaml.safe_load(f)