Update app.py

app.py

@@ -1,152 +1,59 @@
 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.
+# Load model & 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!
+# Define a maximum context length (tokens). Adjust this based on your model!
+MAX_CONTEXT_LENGTH = 4096
 
 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:
-        - Recognize the feeling
-        - 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?”
-        - “Are you feeling sadness because connection is important to you?”
-        - “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?”
-    - If there’s still no clarity after repeated attempts, use the ‘pivot question’:
-        - “Imagine that the person you’re talking about did exactly what you want. What would that give you?”
-    - **Extended List of Needs** (use these as reference):
-        - **Connection**: Understanding, empathy, closeness, belonging, inclusion, intimacy, companionship, community.
-        - **Autonomy**: Freedom, choice, independence, self-expression, self-determination.
-        - **Safety**: Security, stability, trust, predictability, protection.
-        - **Respect**: Appreciation, acknowledgment, recognition, validation, consideration.
-        - **Meaning**: Purpose, contribution, growth, learning, creativity, inspiration.
-        - **Physical Well-being**: Rest, nourishment, health, comfort, ease.
-        - **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?”
-
-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."""
     return len(tokenizer.encode(text))
 
 def truncate_history(history: list[tuple[str, str]], system_message: str, max_length: int) -> list[tuple[str, str]]:
-    """Truncates the conversation history to fit within the maximum token limit.
-
-    Args:
-        history: The conversation history (list of user/assistant tuples).
-        system_message: The system message.
-        max_length: The maximum number of tokens allowed.
-
-    Returns:
-        The truncated history.
-    """
+    """Truncates the conversation history to fit within the maximum token limit."""
     truncated_history = []
     system_message_tokens = count_tokens(system_message)
     current_length = system_message_tokens
 
-    # Iterate backwards through the history (newest to oldest)
+    # Iterate backwards (newest first) and build up history until the limit is reached
     for user_msg, assistant_msg in reversed(history):
         user_tokens = count_tokens(user_msg) if user_msg else 0
         assistant_tokens = count_tokens(assistant_msg) if assistant_msg else 0
         turn_tokens = user_tokens + assistant_tokens
 
         if current_length + turn_tokens <= max_length:
-            truncated_history.insert(0, (user_msg, assistant_msg))  # Add to the beginning
+            truncated_history.insert(0, (user_msg, assistant_msg))
             current_length += turn_tokens
         else:
-            break  # Stop adding turns if we exceed the limit
-
+            break
     return truncated_history
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    """Responds to a user message, maintaining conversation history.  Returns history."""
-
-    formatted_system_message = system_message # Use the provided system message
-
+def respond(message, history, system_message, max_tokens, temperature, top_p):
+    """
+    Calls the LLM with the current message and history.
+    This function is a generator that streams tokens.
+    """
+    # Use the provided system_message (from our prompt display)
+    formatted_system_message = system_message
     truncated_history = truncate_history(history, formatted_system_message, MAX_CONTEXT_LENGTH - max_tokens - 100)
 
+    # Build the message list (system, then history, then new message)
     messages = [{"role": "system", "content": formatted_system_message}]
     for user_msg, assistant_msg in truncated_history:
         if user_msg:
             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>"})
-
     messages.append({"role": "user", "content": f"<|user|>\n{message}</s>"})
 
     response = ""
@@ -159,50 +66,67 @@ def respond(
             top_p=top_p,
         ):
             token = chunk.choices[0].delta.content
-            if token:  # Check if the token is not empty
-                response += token
-                # Yield the *updated* history, including the *current* partial response.
-                yield history + [(message, response)]
-
+            response += token
+            yield response
     except Exception as e:
         print(f"An error occurred: {e}")
-        error_message = "I'm sorry, I encountered an error. Please try again."
-        yield history + [(message, error_message)]
+        yield "I'm sorry, I encountered an error. Please try again."
 
+def generate_response(message, history, system_message, max_tokens, temperature, top_p):
+    """
+    Wrapper function that collects the final response (from the generator)
+    and updates the conversation history.
+    """
+    final_response = ""
+    for r in respond(message, history, system_message, max_tokens, temperature, top_p):
+        final_response = r  # (In a streaming setup, you might update incrementally.)
+    updated_history = history + [(message, final_response)]
+    # Return both the updated chat (for the Chatbot component) and updated state.
+    return updated_history, updated_history
+
+def clear_memory():
+    """Resets the conversation history."""
+    return [], []
+
+# --- Build the Gradio Interface using Blocks ---
+with gr.Blocks() as demo:
+    # Hidden settings in an Accordion (closed by default)
+    with gr.Accordion("Settings", open=False):
+        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)")
 
-def clear_memory(history, chat_history):
-  """Clears the conversation history and resets the chatbot."""
-  history.clear()
-  chat_history.clear()
-  return [], []
+    # Display the prompt below the settings
+    prompt_display = gr.Textbox(
+        value=nvc_prompt_template,
+        label="Prompt",
+        interactive=False,
+        lines=10
+    )
 
+    # Chatbot component to display conversation history (using "messages" type)
+    chatbot = gr.Chatbot(type="messages")
 
+    # Hidden state to store conversation history (list of tuples)
+    state = gr.State([])
 
-# --- Gradio Interface ---
-with gr.Blocks() as demo:
-    chatbot = gr.Chatbot(label="Roos NVC Chatbot")
-    msg = gr.Textbox(label="Your Message", placeholder="Type your message here...") # Added placeholder
+    # Row for user input and a clear memory button
     with gr.Row():
-        send_btn = gr.Button("Send")
+        txt = gr.Textbox(show_label=False, placeholder="Enter your message")
         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(
-                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, chatbot, system_message, max_tokens, temperature, top_p], chatbot).then(lambda: "", None, msg)  # Clear input after submit
-    send_btn.click(respond, [msg, chatbot, system_message, max_tokens, temperature, top_p], chatbot).then(lambda: "", None, msg)  # Clear input after click
-    clear_btn.click(clear_memory, [chatbot, chatbot], [msg, chatbot])
+    # When a message is submitted, call generate_response and update both the chatbot and state.
+    txt.submit(
+        generate_response,
+        inputs=[txt, state, prompt_display, max_tokens_slider, temperature_slider, top_p_slider],
+        outputs=[chatbot, state]
+    )
+
+    # Clear memory button resets the chatbot display and state.
+    clear_btn.click(
+        clear_memory,
+        outputs=[chatbot, state]
+    )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()