created low code script for model deployment

predict_pipeline/predict_sample.py

@@ -0,0 +1,43 @@
+# Imports
+import pandas as pd
+import json
+from pathlib import Path
+import numpy as np
+
+def getLastEntryFromSQLite():
+
+    return 
+
+def callModel(sample):
+    prediction: np.int32 = sample # noch unklar ob jedes mal ein load oder z.B. mit Flask API
+    return prediction
+
+def getMessageConfig( config_file_path):
+
+    return dict()
+
+
+def buildMessage(result: np.int32, config: dict):
+    # message =json...
+    message = 5
+    return message
+
+
+def sendMessage(destination, message):
+    return 2
+
+def main():
+    config_file_path = Path("")
+    config = getMessageConfig(config_file_path=config_file_path)
+
+    sample = getLastEntryFromSQLite()
+
+    prediction = callModel(sample=sample)
+
+    message = buildMessage(result=prediction, config=config)
+    
+    sendMessage(config, message)
+
+
+if __name__ == "__main__":
+    main()