Update app.py

app.py

@@ -147,7 +147,6 @@ def prepare_dataset(tokenizer):
         all_examples = []
         for i, example in enumerate(tokenized_dataset):
             all_examples.append(example)
-            # Progress every 5000 (IMPROVED)
             if i % 5000 == 0:
                 training_status["message"] = f"📥 Loaded {i:,}/{EXAMPLES_PER_SESSION:,} examples..."
             if i >= EXAMPLES_PER_SESSION - 1:
@@ -250,7 +249,6 @@ def run_training_session():
         traceback.print_exc()
         return False
     finally:
-        # MEMORY CLEANUP (IMPROVED)
         del model, trainer
         cleanup_memory()
 
@@ -266,7 +264,6 @@ def start_training():
     training_status["start_time"] = datetime.now()
     stop_requested = False
     
-    # CONTINUOUS TRAINING LOOP (IMPROVED)
     while not stop_requested:
         training_status["message"] = f"🚀 Starting session {training_status['session_count'] + 1}..."
         
@@ -300,13 +297,11 @@ def get_status():
         minutes, seconds = divmod(remainder, 60)
         elapsed = f"{hours}h {minutes}m {seconds}s"
     
-    # Pre-compute formatted loss string to avoid f-string format specifier issues
     if training_status["total_loss"]:
         loss_str = f"{training_status['total_loss']:.4f}"
     else:
         loss_str = "N/A"
     
-    # Pre-compute other conditional strings
     state_str = "🟢 Training" if training_status["is_training"] else "🔴 Stopped"
     continuous_str = "✅ Enabled" if CONTINUOUS_TRAINING else "❌ Disabled"
     elapsed_str = elapsed if elapsed else "N/A"
@@ -386,7 +381,5 @@ with gr.Blocks(title="AI Python Trainer") as demo:
     start_btn.click(start_training_async, outputs=output)
     stop_btn.click(stop_training, outputs=output)
     refresh_btn.click(get_status, outputs=status_display)
-    
-    demo.load(get_status, outputs=status_display, every=30)
 
 demo.launch()