Update app.py

app.py

@@ -6,7 +6,7 @@ from transformers import AutoTokenizer  # Import the tokenizer
 tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
-# Define a maximum context length (tokens).  Check your model's documentation!
+# Define a maximum context length (tokens). Check your model's documentation!
 MAX_CONTEXT_LENGTH = 4096  # Example: Adjust this based on your model!
 
 nvc_prompt_template = r"""<|system|>
@@ -78,7 +78,6 @@ You are Roos, an NVC (Nonviolent Communication) Chatbot. Your goal is to help us
       - “Thank you for sharing with me. If you’d like to continue this conversation later, I’m here to help.”</s>
 """
 
-
 def count_tokens(text: str) -> int:
     """Counts the number of tokens in a given string."""
     return len(tokenizer.encode(text))
@@ -119,53 +118,92 @@ def respond(
     top_p,
 ):
     """Responds to a user message, maintaining conversation history, using special tokens and message list."""
+    # Allow a modifiable system prompt; if none is provided, fall back to the default.
+    formatted_system_message = system_message if system_message else nvc_prompt_template
 
-    formatted_system_message = nvc_prompt_template
-
-    truncated_history = truncate_history(history, formatted_system_message, MAX_CONTEXT_LENGTH - max_tokens - 100) # Reserve space for the new message and some generation
+    truncated_history = truncate_history(
+        history,
+        formatted_system_message,
+        MAX_CONTEXT_LENGTH - max_tokens - 100  # Reserve space for the new message and some generation
+    )
 
-    messages = [{"role": "system", "content": formatted_system_message}] # Start with system message as before
+    messages = [{"role": "system", "content": formatted_system_message}]  # Start with system message
     for user_msg, assistant_msg in truncated_history:
         if user_msg:
-            messages.append({"role": "user", "content": f"<|user|>\n{user_msg}</s>"}) # Format history user message
+            messages.append({"role": "user", "content": f"<|user|>\n{user_msg}</s>"})
         if assistant_msg:
-            messages.append({"role": "assistant", "content": f"<|assistant|>\n{assistant_msg}</s>"}) # Format history assistant message
-
-    messages.append({"role": "user", "content": f"<|user|>\n{message}</s>"}) # Format current user message
-
+            messages.append({"role": "assistant", "content": f"<|assistant|>\n{assistant_msg}</s>"})
+    messages.append({"role": "user", "content": f"<|user|>\n{message}</s>"})
 
     response = ""
     try:
-      for chunk in client.chat_completion(
-          messages, # Send the messages list again, but with formatted content
-          max_tokens=max_tokens,
-          stream=True,
-          temperature=temperature,
-          top_p=top_p,
-      ):
-          token = chunk.choices[0].delta.content
-          response += token
-          yield response
+        for chunk in client.chat_completion(
+            messages,  # Send the messages list with the formatted content
+            max_tokens=max_tokens,
+            stream=True,
+            temperature=temperature,
+            top_p=top_p,
+        ):
+            token = chunk.choices[0].delta.content
+            response += token
+            yield response
     except Exception as e:
-      print(f"An error occurred: {e}") # It's a good practice add a try-except block
-      yield "I'm sorry, I encountered an error. Please try again."
-
-# --- Gradio Interface ---
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value=nvc_prompt_template, label="System message", visible=False), # Set the NVC prompt as default and hide the system message box
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+        print(f"An error occurred: {e}")
+        yield "I'm sorry, I encountered an error. Please try again."
+
+# --- Gradio Interface using Blocks ---
+with gr.Blocks() as demo:
+    # State to hold conversation history
+    state = gr.State([])
+
+    # Chatbot display
+    chatbot = gr.Chatbot(label="NVC Chatbot")
+
+    # System prompt textbox (modifiable)
+    with gr.Row():
+        system_prompt = gr.Textbox(value=nvc_prompt_template, label="System Prompt (modifiable)")
+
+    # Controls for generation parameters
+    with gr.Row():
+        max_tokens_slider = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")
+        temperature_slider = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
+        top_p_slider = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
+
+    # Button to clear conversation memory
+    with gr.Row():
+        clear_button = gr.Button("Clear Memory")
+
+    # User input area and send button
+    with gr.Row():
+        user_input = gr.Textbox(label="Your Message", placeholder="Type your message here...")
+        send_button = gr.Button("Send")
+
+    def chat_step(message, history, system_message, max_tokens, temperature, top_p):
+        # Call the respond generator and accumulate the final response.
+        gen = respond(message, history, system_message, max_tokens, temperature, top_p)
+        response = ""
+        for r in gen:
+            response = r  # In a streaming scenario, you might update the UI incrementally.
+        history.append((message, response))
+        return history, history
+
+    # Trigger the chat step on button click or when submitting the textbox.
+    send_button.click(
+        chat_step,
+        inputs=[user_input, state, system_prompt, max_tokens_slider, temperature_slider, top_p_slider],
+        outputs=[chatbot, state],
+    )
+    user_input.submit(
+        chat_step,
+        inputs=[user_input, state, system_prompt, max_tokens_slider, temperature_slider, top_p_slider],
+        outputs=[chatbot, state],
+    )
+
+    # Clear memory: resets both the chatbot display and the state.
+    def clear_history():
+        return [], []
+
+    clear_button.click(clear_history, inputs=[], outputs=[chatbot, state])
 
 if __name__ == "__main__":
     demo.launch()