import gradio as gr from huggingface_hub import InferenceClient from transformers import AutoTokenizer # Import the tokenizer # Import the tokenizer - No need to import twice, remove the second import 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! default_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.”"""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. """ truncated_history = [] system_message_tokens = count_tokens(system_message) current_length = system_message_tokens # Iterate backwards through the history (newest to oldest) 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 current_length += turn_tokens else: break # Stop adding turns if we exceed the limit return truncated_historydef respond( message, history: list[tuple[str, str]], system_message, # System message is now an argument max_tokens, temperature, top_p, ): """Responds to a user message, maintaining conversation history, using special tokens and message list.""" if message.lower() == "clear memory": # Check for the clear memory command return "", [] # Return empty message and empty history to reset the chat formatted_system_message = system_message # Use the system_message argument 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 for user_msg, assistant_msg in truncated_history: if user_msg: messages.append({"role": "user", "content": user_msg}) # Format history user message - Removed extra tags if assistant_msg: messages.append({"role": "assistant", "content": assistant_msg}) # Format history assistant message - Removed extra tags messages.append({"role": "user", "content": message}) # Format current user message - Removed extra tags 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 # Post-processing to remove prefixes (example - add to your existing yield) - Solution 3 (Fallback) processed_response = response.replace("User:", "").replace("Assistant:", "").replace("Roos:", "").lstrip() yield processed_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=default_nvc_prompt_template, label="System message", visible=True, lines=10, # Increased height for more space to read the prompt ), 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)", ), ], ) if __name__ == "__main__": demo.launch(share=True)