Add log_argument

Signed-off-by: András Schmelczer <andras@schmelczer.dev>

example/main_batch.py

@@ -7,13 +7,11 @@ from predict_domain import predict_domain
 from good_ai import process_batch, serve
 
 if __name__ == "__main__":
-    serve(predict_domain)
-    
-    with open(".cache/ss-data-0/s2-corpus-1583.json") as f:
+    with open(".cache/data-1/s2-corpus-323.json") as f:
         raw = json.load(f)
 
     shuffle(raw)
-    data = {f'{r["title"]} {r["abstract"]}': r["domain"] for r in raw[:5]}
+    data = {f'{r["title"]} {r["abstract"]}': r["domain"] for r in raw[:10]}
 
     results = process_batch(predict_domain, data.keys())
 

example/main_service.py

@@ -0,0 +1,10 @@
+import json
+from random import shuffle
+
+from devtools import debug
+from predict_domain import predict_domain
+
+from good_ai import process_batch, serve
+
+if __name__ == "__main__":
+    serve(predict_domain)

example/predict_domain.py

@@ -1,15 +1,18 @@
 import re
 from typing import Dict, Iterable, List
 
+
 from config import model_key
 from models import DomainPrediction
 from preprocess import preprocess
 from sklearn.pipeline import Pipeline
 
-from good_ai import use_model
+from good_ai import use_model, log_argument, log_metric
 from good_ai.utilities.clean import clean
 
 
+@log_metric('text_length', calculate=lambda text: len(text))
+@log_argument('text', expected_type=str, validator=lambda t: len(t) > 0)
 @use_model(model_key, version="latest")
 def predict_domain(
     text: str, model: Pipeline, cut_off_probability: float = 0.2