Add monitoring

src/monitoring.py

@@ -43,11 +43,6 @@ def retrain_on_youtube_sample():
 
 
 
-
-    
-
-
-
 def main():
     print("Caricamento del modello")
     model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH)

tests/integration/test_monitoring.py

@@ -1,24 +1,36 @@
 import os
 import json
 import pytest
-from src.monitoring import monitor_model
+from src.monitoring import main, REPORTS_DIR
+
+METRICS_PATH = os.path.join(REPORTS_DIR, "metrics.json")
 
-METRICS_PATH = "reports/metrics.json"
 
 @pytest.fixture(autouse=True)
 def cleanup_metrics():
-    """Pulisce file metrics prima del test."""
+    """Rimuove il file metrics.json prima e dopo i test."""
     if os.path.exists(METRICS_PATH):
         os.remove(METRICS_PATH)
     yield
     if os.path.exists(METRICS_PATH):
         os.remove(METRICS_PATH)
 
+
 def test_monitoring_creates_metrics():
-    """Verifica che il monitoring crei il file metrics.json."""
-    monitor_model()
+    """Verifica che il monitoring crei correttamente il file metrics.json e contenga i dati previsti."""
+    main() 
+
     assert os.path.exists(METRICS_PATH), "metrics.json non è stato generato"
 
+
     with open(METRICS_PATH, "r") as f:
         metrics = json.load(f)
-    assert "accuracy" in metrics and "f1" in metrics, "Metriche principali mancanti"
+
+    assert "TweetEval" in metrics, "Mancano metriche TweetEval"
+    assert "YouTube" in metrics, "Mancano metriche YouTube"
+
+
+    for dataset_name, data in metrics.items():
+        assert "accuracy" in data, f"Manca accuracy per {dataset_name}"
+        assert "f1" in data, f"Manca F1 per {dataset_name}"
+