Update app.py

app.py

@@ -5,8 +5,7 @@ import os
 
 # --- Configuration (Read from Environment Variables) ---
 
-# Get the model path from an environment variable.  Default to a placeholder
-# if the environment variable is not set.  This is important for deployment.
+# Get the model path from an environment variable.
 model_path = os.environ.get("MODEL_PATH", "Athspi/Athspiv2new")
 deepseek_tokenizer_path = os.environ.get("TOKENIZER_PATH", "deepseek-ai/DeepSeek-R1")
 # Get the Hugging Face token from an environment variable (for gated models).
@@ -67,21 +66,26 @@ def chat_with_llm(prompt, history):
     except Exception as e:
         return f"Error during generation: {e}"
 
-
 # --- Gradio Interface ---
-
+# Use the 'messages' format for chatbot
 def predict(message, history):
     history = history or []
     response = chat_with_llm(message, history)
-    history.append((message, response))
+    # history.append((message, response)) # Old format
+    history.append({"role": "user", "content": message}) # Append user message in 'messages' format
+    history.append({"role": "assistant", "content": response})  # Append assistant (AI) message
     return "", history
 
 with gr.Blocks() as demo:
-    chatbot = gr.Chatbot(label="Athspi Chat", height=500, show_label=True, value=[[None, "Hi! I'm Athspi. How can I help you today?"]])
+    chatbot = gr.Chatbot(label="Athspi Chat", height=500, show_label=True,
+                         value=[{"role": "assistant", "content": "Hi! I'm Athspi. How can I help you today?"}],
+                         type="messages")  # Set type to "messages"
     msg = gr.Textbox(label="Your Message", placeholder="Type your message here...")
     clear = gr.Button("Clear")
 
+
     msg.submit(predict, [msg, chatbot], [msg, chatbot])
-    clear.click(lambda: None, None, chatbot, queue=False)
+     # Clear needs to be updated for the messages format
+    clear.click(lambda: [], [], chatbot, queue=False) # Return empty list for history
 
 demo.launch(share=True)