Update app.py

app.py

@@ -117,8 +117,7 @@ with gr.Blocks(theme=gr.themes.Default(primary_hue="blue", secondary_hue="neutra
 demo.launch(server_name="0.0.0.0", server_port=7860)
 
 """
-# below edition can run but chat history not OK:
-
+# Backup, OK: history, user sys prompt, cpu.:
 
 import gradio as gr
 import ollama
@@ -127,29 +126,27 @@ import ollama
 MODEL_NAME = 'hf.co/unsloth/gemma-3-4b-it-qat-GGUF:Q4_K_M'
 
 # Default System Prompt
-DEFAULT_SYSTEM_PROMPT = "You are a helpful and respectful assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature."
+DEFAULT_SYSTEM_PROMPT = "You must response in zh-TW. Answer everything in simple, smart, relevant and accurate style. No chatty!"
 
 # --- Gradio Interface ---
 with gr.Blocks(theme=gr.themes.Default(primary_hue="blue", secondary_hue="neutral")) as demo:
-    gr.Markdown(f"# LLM GGUF Chat with `{MODEL_NAME}`")
+    gr.Markdown(f"## LLM GGUF Chat with `{MODEL_NAME}`")
     gr.Markdown("Chat with the model, customize its behavior with a system prompt, and toggle streaming output.")
 
-    # --- FIX: Use the modern 'messages' type for the Chatbot component ---
-    # This resolves the UserWarning and simplifies history management.
+    # Use the modern 'messages' type for the Chatbot component
     chatbot = gr.Chatbot(
         label="Conversation",
         height=500,
-        type='messages',  # Use the recommended OpenAI-style message format
+        type='messages',
         layout="bubble"
     )
     
     with gr.Row():
         msg = gr.Textbox(
-            label="Your Message",
+            show_label=False,
             placeholder="Type your message here and press Enter...",
             lines=1,
             scale=4,
-            show_label=False,
             container=False
         )
 
@@ -174,7 +171,7 @@ with gr.Blocks(theme=gr.themes.Default(primary_hue="blue", secondary_hue="neutra
             interactive=False 
         )
 
-    # Function to handle the logic for showing/hiding the custom system prompt textbox
+    # Function to toggle the interactivity of the system prompt textbox
     def toggle_system_prompt(use_custom):
         return gr.update(interactive=use_custom)
 
@@ -185,58 +182,58 @@ with gr.Blocks(theme=gr.themes.Default(primary_hue="blue", secondary_hue="neutra
         queue=False
     )
     
-    # --- FIX: Use a two-step process with .then() to solve the ValueError ---
-    # This is the robust way to handle multi-part responses in Gradio.
-
-    # Step 1: Add the user's message to the chat history and clear the input box.
-    # This function runs instantly on submission.
-    def add_user_message(history, user_message):
-        # The history is now a list of dictionaries, no conversion needed.
-        history.append({"role": "user", "content": user_message})
-        # Return the updated history for the chatbot and an empty string for the textbox.
-        return history, gr.update(value="")
-
-    # Step 2: Get the bot's response.
-    # This function runs after the user's message has been added.
-    def get_bot_response(history, system_prompt, stream_output):
-        # Prepend the system prompt to the conversation history for the API call.
+    # --- Core Chat Logic ---
+    # This function is the heart of the application.
+    def respond(history, system_prompt, stream_output):
+        
+        #This is the single function that handles the entire chat process.
+        #It takes the history, prepends the system prompt, calls the Ollama API,
+        #and streams the response back to the chatbot.
+                
+        # --- FINAL FIX: Construct the API payload correctly ---
+        # The 'history' variable from Gradio contains the entire conversation.
+        # We prepend the system prompt to this history to form the final payload.
         messages = [{"role": "system", "content": system_prompt}] + history
 
-        # Add a placeholder for the assistant's response.
+        # Add a placeholder for the assistant's response to the UI history.
+        # This creates the space where the streamed response will be displayed.
         history.append({"role": "assistant", "content": ""})
 
-        if stream_output:
-            response_stream = ollama.chat(
-                model=MODEL_NAME,
-                messages=messages,
-                stream=True
-            )
-            # Stream the response, updating the last message in the history
-            for chunk in response_stream:
-                if chunk['message']['content']:
-                    history[-1]['content'] += chunk['message']['content']
-                    yield history
-        else:
-            response = ollama.chat(
-                model=MODEL_NAME,
-                messages=messages,
-                stream=False
-            )
-            history[-1]['content'] = response['message']['content']
-            yield history
+        # Stream the response from the Ollama API
+        response_stream = ollama.chat(
+            model=MODEL_NAME,
+            messages=messages,
+            stream=True
+        )
+        
+        # Iterate through the stream, updating the placeholder with each new chunk.
+        for chunk in response_stream:
+            if chunk['message']['content']:
+                history[-1]['content'] += chunk['message']['content']
+                # Yield the updated history to the chatbot for a real-time effect.
+                yield history
 
-    # Wire up the event listeners using the .then() method.
+    # This function handles the user's submission.
+    def user_submit(history, user_message):
+        
+        #Adds the user's message to the chat history and clears the input box.
+        #This prepares the state for the main 'respond' function.
+        
+        return history + [{"role": "user", "content": user_message}], ""
+
+    # Gradio Event Wiring
     msg.submit(
-        add_user_message,
+        user_submit,
         inputs=[chatbot, msg],
         outputs=[chatbot, msg],
-        queue=False  # Run instantly
+        queue=False
     ).then(
-        get_bot_response,
+        respond,
         inputs=[chatbot, system_prompt_textbox, stream_checkbox],
         outputs=[chatbot]
     )
 
 # Launch the Gradio interface
 demo.launch(server_name="0.0.0.0", server_port=7860)
+
 """