Update app.py

app.py

@@ -8,17 +8,18 @@ def predict(input, history=[]):
       history (list, optional): List of previous inputs and outputs for context (default: []).
 
   Returns:
-      tuple: A tuple containing the chatbot response and the updated history (optional).
+      tuple: A tuple containing a list of chatbot responses (wrapped in lists) and the updated history (optional).
   """
 
   # Replace with your actual Gemma prediction logic here
-  chatbot_response = "This is a placeholder chatbot response. Integrate your Gemma model here for predictions."
+  chatbot_response = "This is your Gemma-powered chatbot response."
 
-  # Update history if necessary for your application
-  if history:
-      history.append((input, chatbot_response))
+  return [[chatbot_response]], history  # Wrap response in a list of lists (Option A)
 
-  return chatbot_response, history  # Optionally return updated history
+  # Alternatively, for multiple responses:
+  # chatbot_response1 = "First Gemma response."
+  # chatbot_response2 = "Second Gemma response."
+  # return [[chatbot_response1], [chatbot_response2]], history
 
 # Create the Gradio interface
 interface = gr.Interface(
@@ -27,11 +28,12 @@ interface = gr.Interface(
     outputs=["chatbot", "state"]  # Remove "state" output if history is not used
 )
 
-# Load any necessary model weights (replace with your specific model loading logic)
+# Load the model within the Gradio interface context
 try:
   gr.load("models/google/gemma-1.1-7b-it")  # Assuming model weights are available
 except Exception as e:
-  print(f"Error loading model: {e}")  # Handle potential loading errors
+  print(f"An error occurred while loading the model: {e}")  # Improved error handling
 
 # Launch the Gradio interface
 interface.launch()
+