Update app.py

app.py

@@ -2,6 +2,7 @@ import torch
 from transformers import pipeline
 import gradio as gr
 import threading
+import time
 
 # Global variable to store the model pipeline
 model_pipeline = None
@@ -76,6 +77,13 @@ def reload_model_button():
     load_model()
     return "Model reloaded successfully."
 
+# Function to periodically update the status text
+def update_status_periodically(status_text):
+    while True:
+        time.sleep(5)  # Update every 5 seconds
+        status = "Model is loaded and ready." if model_loaded else "Model is not loaded."
+        status_text.update(value=status)
+
 # Gradio Interface
 with gr.Blocks() as demo:
     gr.Markdown("# DeepSeek-R1 Chatbot")
@@ -92,13 +100,6 @@ with gr.Blocks() as demo:
     
     status_text = gr.Textbox(label="Model Status", value="Model not loaded yet.", interactive=False)
     
-    def update_status():
-        """Update the model status text."""
-        if model_loaded:
-            return "Model is loaded and ready."
-        else:
-            return "Model is not loaded."
-    
     def respond(message, chat_history):
         bot_message = chat(message, chat_history)
         chat_history.append((message, bot_message))
@@ -108,10 +109,10 @@ with gr.Blocks() as demo:
     clear_button.click(lambda: [], None, chatbot)
     reload_button.click(reload_model_button, None, status_text)
     
-    # Periodically check and update model status
-    demo.load(update_status, None, status_text, every=5)
+    # Start a background thread to update the status text periodically
+    threading.Thread(target=update_status_periodically, args=(status_text,), daemon=True).start()
 
 # Load the model when the server starts
 if __name__ == "__main__":
     load_model()  # Pre-load the model
-    demo.launch(server_name="0.0.0.0")
+    demo.launch(server_name="0.0.0.0")