Update app.py

app.py

@@ -101,11 +101,23 @@ def auto_train_pipeline():
 def initialize_autonomous_trainer():
     training_thread = threading.Thread(target=auto_train_pipeline, daemon=True)
     training_thread.start()
+    
+    # Start the status update process
+    return training_status
+
+# --- Status update function ---
+def update_status():
+    # Return the current status and trigger the next update
+    time.sleep(0.5)  # Small delay to prevent CPU overload
+    return training_status, gr.update(autoscroll=True)  # Also autoscroll chat window
 
 # --- Gradio UI (Chat-Only) ---
 with gr.Blocks(title="🥥 COCONUT-VLM Autonomous Trainer") as demo:
     gr.Markdown("# 🥥 COCONUT-VLM: Autonomous Vision-Language Trainer")
     gr.Markdown("Model is training itself in 3 stages automatically. **You can only chat.** Training is backend-only.")
+    
+    # We'll create a hidden component to trigger status updates
+    hidden_dummy = gr.State()
 
     with gr.Row():
         with gr.Column(scale=1):
@@ -126,14 +138,24 @@ with gr.Blocks(title="🥥 COCONUT-VLM Autonomous Trainer") as demo:
             msg = gr.Textbox(label="Ask a question about the image")
             clear = gr.Button("Clear Chat")
 
-    # Chat logic - FIXED: Changed output to chatbot instead of chat_history
+    # Chat logic - FIXED: Changed output to chatbot
     msg.submit(chat_with_image, [image_input, msg, chatbot], [msg, chatbot])
-    clear.click(lambda: None, None, chatbot, queue=False)
-
-    # ✅ FIXED: Use Gradio 4.x compatible .load()
-    demo.load(initialize_autonomous_trainer, inputs=None, outputs=None)
-    
-    # ✅ Updated: Use the correct syntax for Timer
-    gr.Timer(3, lambda: training_status, outputs=status)
+    clear.click(lambda: None, inputs=None, outputs=chatbot, queue=False)
+
+    # ✅ FIXED: Combined initialization and status updates
+    demo.load(
+        fn=initialize_autonomous_trainer, 
+        inputs=None, 
+        outputs=status,
+        then=[
+            fn=update_status, 
+            outputs=[status, chatbot],
+            every=1.5,  # Update every 1.5 seconds
+            # We'll chain updates to create a continuous loop:
+            then=update_status, 
+            outputs=[status, chatbot], 
+            every=1.5
+        ]
+    )
 
 demo.queue(max_size=20).launch()