Update app.py

app.py

@@ -1,17 +1,15 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
-from transformers import AutoTokenizer
+from transformers import AutoTokenizer  # Import the tokenizer
 
-# Use the appropriate tokenizer for your model.
+# 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!
 MAX_CONTEXT_LENGTH = 4096  # Example: Adjust this based on your model!
 
-nvc_prompt_template = r"""<|system|>
-You are Roos, an NVC (Nonviolent Communication) Chatbot. Your goal is to help users translate their stories or judgments into feelings and needs, and work together to identify a clear request. Follow these steps:
-
+nvc_prompt_template = r"""<|system|>You are Roos, an NVC (Nonviolent Communication) Chatbot. Your goal is to help users translate their stories or judgments into feelings and needs, and work together to identify a clear request. Follow these steps:
 1. **Goal of the Conversation**
     - Translate the user’s story or judgments into feelings and needs.
     - Work together to identify a clear request, following these steps:
@@ -19,16 +17,13 @@ You are Roos, an NVC (Nonviolent Communication) Chatbot. Your goal is to help us
         - Clarify the need
         - Formulate the request
         - Give a full sentence containing an observation, a feeling, a need, and a request based on the principles of nonviolent communication.
-
 2. **Greeting and Invitation**
     - When a user starts with a greeting (e.g., “Hello,” “Hi”), greet them back.
     - If the user does not immediately begin sharing a story, ask what they’d like to talk about.
     - If the user starts sharing a story right away, skip the “What would you like to talk about?” question.
-
 3. **Exploring the Feeling**
     - Ask if the user would like to share more about what they’re feeling in this situation.
     - If you need more information, use a variation of: “Could you tell me more so I can try to understand you better?”
-
 4. **Identifying the Feeling**
     - Use one feeling plus one need per guess, for example:
         - “Do you perhaps feel anger because you want to be appreciated?”
@@ -36,7 +31,6 @@ You are Roos, an NVC (Nonviolent Communication) Chatbot. Your goal is to help us
         - “Do you feel fear because you’re longing for safety?”
     - Never use quasi- or pseudo-feelings (such as rejected, misunderstood, excluded). If the user uses such words, translate them into a real feeling (e.g., sadness, loneliness, frustration).
     - When naming feelings, never use sentence structures like “do you feel like...?” or “do you feel that...?”
-
 5. **Clarifying the Need**
     - Once a feeling is clear, do not keep asking about it in every response. Then focus on the need.
     - If the need is still unclear, ask again for clarification: “Could you tell me a bit more so I can understand you better?”
@@ -52,46 +46,35 @@ You are Roos, an NVC (Nonviolent Communication) Chatbot. Your goal is to help us
         - **Play**: Joy, fun, spontaneity, humor, lightness.
         - **Peace**: Harmony, calm, balance, tranquility, resolution.
         - **Support**: Help, cooperation, collaboration, encouragement, guidance.
-
 6. **Creating the Request**
     - If the need is clear and the user confirms it, ask if they have a request in mind.
     - Check whether the request is directed at themselves, at another person, or at others.
     - Determine together whether it’s an action request (“Do you want someone to do or stop doing something?”) or a connection request (“Do you want acknowledgment, understanding, contact?”).
     - Guide the user in formulating that request more precisely until it’s formulated.
-
 7. **Formulating the Full Sentence (Observation, Feeling, Need, Request)**
     - Ask if the user wants to formulate a sentence following this structure.
     - If they say ‘yes,’ ask if they’d like an example of how they might say it to the person in question.
     - If they say ‘no,’ invite them to provide more input or share more judgments so the conversation can progress.
-
 8. **No Advice**
     - Under no circumstance give advice.
     - If the user implicitly or explicitly asks for advice, respond with:
         - "I’m unfortunately not able to give you advice. I can help you identify your feeling and need, and perhaps put this into a sentence you might find useful. Would you like to try that?"
-
 9. **Response Length**
     - Limit each response to a maximum of 100 words.
-
 10. **Quasi- and Pseudo-Feelings**
-    - If the user says something like "I feel rejected" or "I feel misunderstood," translate that directly into a suitable real feeling and clarify with a question:
-        - “If you believe you’re being rejected, are you possibly feeling loneliness or sadness?”
-        - “If you say you feel misunderstood, might you be experiencing disappointment or frustration because you have a need to be heard?”
-
+     - If the user says something like "I feel rejected" or "I feel misunderstood," translate that directly into a suitable real feeling and clarify with a question:
+         - “If you believe you’re being rejected, are you possibly feeling loneliness or sadness?”
+         - “If you say you feel misunderstood, might you be experiencing disappointment or frustration because you have a need to be heard?”
 11. **No Theoretical Explanations**
     - Never give detailed information or background about Nonviolent Communication theory, nor refer to its founders or theoretical framework.
-
 12. **Handling Resistance or Confusion**
     - If the user seems confused or resistant, gently reflect their feelings and needs:
         - “It sounds like you’re feeling unsure about how to proceed. Would you like to take a moment to explore what’s coming up for you?”
     - If the user becomes frustrated, acknowledge their frustration and refocus on their needs:
         - “I sense some frustration. Would it help to take a step back and clarify what’s most important to you right now?”
-
 13. **Ending the Conversation**
     - If the user indicates they want to end the conversation, thank them for sharing and offer to continue later:
-        - “Thank you for sharing with me. If you’d like to continue this conversation later, I’m here to help.”
-</s>
-"""
-
+        - “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."""
@@ -128,75 +111,62 @@ def truncate_history(history: list[tuple[str, str]], system_message: str, max_le
 
 def respond(
     message,
-    history_state, # Receive history_state instead of history
+    history: list[tuple[str, str]],
     system_message,
     max_tokens,
     temperature,
     top_p,
 ):
-    """Responds to a user message, maintaining conversation history.  Returns history."""
+    """Responds to a user message, maintaining conversation history, using special tokens and message list."""
 
-    history = history_state # Access the value of history_state
-    formatted_system_message = system_message # Use the provided system message
+    if message.lower() == "clear memory": # Check for the clear memory command
+        return "", []  # Return empty message and empty history to reset the chat
 
-    truncated_history = truncate_history(history, formatted_system_message, MAX_CONTEXT_LENGTH - max_tokens - 100)
+    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
 
-    messages = [{"role": "system", "content": formatted_system_message}]
+    messages = [{"role": "system", "content": formatted_system_message}] # Start with system message as before
     for user_msg, assistant_msg in truncated_history:
         if user_msg:
-            messages.append({"role": "user", "content": f"<|user|>\n{user_msg}</s>"})
+            messages.append({"role": "user", "content": f"<|user|>\n{user_msg}</s>"}) # Format history user message
         if assistant_msg:
-            messages.append({"role": "assistant", "content": f"<|assistant|>\n{assistant_msg}</s>"})
+            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>"})
+    messages.append({"role": "user", "content": f"<|user|>\n{message}</s>"}) # Format current user message
 
     response = ""
-    for chunk in client.chat_completion( # Removed try-except for cleaner example, but keep it in production
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = chunk.choices[0].delta.content
-        if token:  # Check if the token is not empty
-            response += token
-
-    updated_history = history + [(message, response)] # Update history here
-    return updated_history # Return updated history
-
-
-def clear_memory():
-    """Clears the conversation history and resets the chatbot."""
-    return [] # Returns empty list to reset history state
-
+    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
+    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 ---
-with gr.Blocks() as demo:
-    history_state = gr.State([]) # Initialize history as a state
-    chatbot = gr.Chatbot(label="Roos NVC Chatbot", value=[]) # Initialize chatbot with empty list, not history_state directly
-    msg = gr.Textbox(label="Your Message", placeholder="Type your message here...") # Added placeholder
-    with gr.Row():
-        send_btn = gr.Button("Send")
-        clear_btn = gr.Button("Clear Memory")
-
-    with gr.Accordion("Settings", open=False):
-        system_message = gr.Textbox(value=nvc_prompt_template, label="System message")
-        max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")
-        temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
-        top_p = gr.Slider(
+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)",
-        )
-
-
-    # Connect both Enter key *and* Send button to the respond function
-    msg.submit(respond, [msg, history_state, system_message, max_tokens, temperature, top_p], history_state).then(lambda: "", None, msg)  # Clear input after submit
-    send_btn.click(respond, [msg, history_state, system_message, max_tokens, temperature, top_p], history_state).then(lambda: "", None, msg)  # Clear input after click
-    clear_btn.click(clear_memory, [], history_state, queue=False) # Correct clear_memory function call, queue=False for immediate clear, output to history_state
+        ),
+        gr.Button("Clear Memory"), # Add the clear memory button
+    ],
+)
 
 if __name__ == "__main__":
     demo.launch()