fix: ensure trained model is correctly detected using absolute path

train_abuse_model.py

@@ -41,7 +41,8 @@ from utils import (
     tune_thresholds,
     label_map,
     label_row_soft,
-    AbuseDataset
+    AbuseDataset,
+    save_and_yield_eval
 )
 
 # Create evaluation results directory if it doesn't exist
@@ -52,18 +53,6 @@ MODEL_DIR = PERSIST_DIR / "saved_model"
 LOG_FILE = PERSIST_DIR / "training.log"
 
 
-# Save and print evaluation results
-def save_and_yield_eval(report: str):
-    # Generate versioned filename using timestamp
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    
-    eval_filename = f"eval_report_{timestamp}.txt"
-    eval_filepath = Path("/home/user/app/results_eval") / eval_filename
-
-    with open(eval_filepath, "w") as f:
-        f.write(report)
-    yield f"📄 Evaluation saved to: {eval_filepath.name}"
-    yield report
 # configure logging 
 log_buffer = io.StringIO()
 logging.basicConfig(
@@ -135,7 +124,7 @@ def load_saved_model_and_tokenizer():
     return tokenizer, model
 
 def evaluate_saved_model(progress=gr.Progress(track_tqdm=True)):
-    if os.path.exists("saved_model/"):
+    if MODEL_DIR.exists():
         yield "✅ Trained model found! Skipping training...\n"
     else:
         yield "❌ No trained model found. Please train the model first.\n"
@@ -218,7 +207,7 @@ model_name = "microsoft/deberta-v3-base"
 
 def run_training(progress=gr.Progress(track_tqdm=True)):
     log_queue = queue.Queue()
-    if os.path.exists("saved_model/"):
+    if MODEL_DIR.exists():
         yield "✅ Trained model found! Skipping training...\n"
         for line in evaluate_saved_model():
             yield line