Create app-v1.py

app-v1.py

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+import gradio as gr
+from huggingface_hub import InferenceClient
+from transformers import AutoTokenizer  # Import the tokenizer
+
+# 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!
+
+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.”</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.
+    """
+    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_history
+
+def 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": f"<|user|>\n{user_msg}</s>"}) # Format history user message
+        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
+
+    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
+    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)