Update app.py

app.py

@@ -35,7 +35,7 @@ class TrainingState:
             
     def add_log(self, message):
         self.logs.append(f"[{datetime.now().strftime('%H:%M:%S')}] {message}")
-        if len(self.logs) > 10:  # Keep only last 10 logs
+        if len(self.logs) > 10:
             self.logs.pop(0)
             
     def complete_process(self):
@@ -59,10 +59,8 @@ class TrainingState:
 state = TrainingState()
 
 def test_model(input_text):
-    """Enhanced test function with response variations"""
     if not input_text.strip():
         return "Please enter some text to test."
-    
     responses = [
         f"Processed: '{input_text}'",
         f"Model response to: {input_text}",
@@ -73,7 +71,6 @@ def test_model(input_text):
     return random.choice(responses)
 
 def simulate_process(duration, process_type, data_size):
-    """Simulate long-running training/fine-tuning process"""
     if process_type == "train":
         state.start_training(data_size)
     else:
@@ -84,8 +81,6 @@ def simulate_process(duration, process_type, data_size):
         time.sleep(duration / steps)
         progress = int((i / steps) * 100)
         state.update_progress(progress)
-        
-        # Add simulated log messages
         if i % 3 == 0:
             messages = [
                 f"Processing batch {i*5}/{steps*5}",
@@ -94,59 +89,53 @@ def simulate_process(duration, process_type, data_size):
                 f"Learning rate: {random.uniform(1e-5, 1e-3):.6f}"
             ]
             state.add_log(random.choice(messages))
-    
     state.complete_process()
 
 def train_model(dataset_text):
-    """Training function with simulated processing"""
     if not dataset_text.strip():
         return "Please provide training data.", ""
-    
     data_size = len(dataset_text)
-    if state.status != "idle":
+    if getattr(state, 'status', 'idle') != "idle":
         return "Another process is already running. Please wait.", ""
-    
-    # Start simulation in background thread
     threading.Thread(
         target=simulate_process,
         args=(15, "train", data_size),
         daemon=True
     ).start()
-    
     return "Training started successfully! Check status in the Status tab.", ""
 
 def finetune_model(dataset_text):
-    """Fine-tuning function with simulated processing"""
     if not dataset_text.strip():
         return "Please provide fine-tuning data.", ""
-    
     data_size = len(dataset_text)
-    if state.status != "idle":
+    if getattr(state, 'status', 'idle') != "idle":
         return "Another process is already running. Please wait.", ""
-    
-    # Start simulation in background thread
     threading.Thread(
         target=simulate_process,
         args=(10, "fine-tune", data_size),
         daemon=True
     ).start()
-    
     return "Fine-tuning started successfully! Check status in the Status tab.", ""
 
 def get_current_status():
-    """Get current system status"""
-    status_text = state.get_status()
-    
-    # Add progress information
-    if state.status != "idle":
-        status_text += f" - {state.progress}% complete"
-    
-    # Format logs
-    logs = "\n".join(state.logs) if state.logs else "No logs available"
+    try:
+        status_text = state.get_status()
+    except Exception as e:
+        status_text = f"❌ Error: {str(e)}"
+    
+    try:
+        progress = state.progress
+    except:
+        progress = 0
+
+    try:
+        logs = "\n".join(state.logs[-10:]) if hasattr(state, 'logs') else "No logs available"
+    except:
+        logs = "❌ Failed to retrieve logs."
     
     return {
         status_box: status_text,
-        progress_bar: state.progress,
+        progress_bar: progress,
         log_output: logs
     }
 
@@ -187,16 +176,12 @@ with gr.Blocks(title="Pashto-Base-Bloom Trainer", theme="soft") as demo:
         with gr.Row():
             with gr.Column():
                 status_box = gr.Textbox(label="Current Status", interactive=False)
-                # Replace ProgressBar with Slider for progress indication
                 progress_bar = gr.Slider(label="Progress", minimum=0, maximum=100, value=0, interactive=False)
                 refresh_btn = gr.Button("Refresh Status", variant="secondary")
                 auto_refresh = gr.Checkbox(label="Auto-refresh every 5 seconds", value=True)
             log_output = gr.Textbox(label="Process Logs", lines=8, interactive=False)
         
-        # Refresh actions
         refresh_btn.click(get_current_status, outputs=[status_box, progress_bar, log_output])
-        
-        # Initial status load
         demo.load(get_current_status, outputs=[status_box, progress_bar, log_output])
 
 if __name__ == "__main__":