import gradio as gr
from huggingface_hub import InferenceClient
from transformers import AutoTokenizer

# Initialize tokenizer and client
tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

# Maximum context length (adjust if needed)
MAX_CONTEXT_LENGTH = 4096

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 using observation, feeling, need, and request.
2. **Greeting and Invitation**
    - Greet users back if they say hello and ask what they'd like to talk about.
3. **Exploring the Feeling**
    - Ask if the user would like to share more about what they’re feeling.
4. **Identifying the Feeling**
    - Offer one feeling and one need per guess (e.g., “Do you feel anger because you want to be appreciated?”).
5. **Clarifying the Need**
    - If the need isn’t clear, ask for clarification.
6. **Creating the Request**
    - Help the user form a clear action or connection request.
7. **Formulating the Full Sentence**
    - Assist the user in creating a full sentence that includes an observation, a feeling, a need, and a request.
8. **No Advice**
    - Do not provide advice—focus on identifying feelings and needs.
9. **Response Length**
    - Limit responses to a maximum of 100 words.
10. **Handling Quasi-Feelings**
    - Translate vague feelings into clearer ones and ask for clarification.
11. **No Theoretical Explanations**
    - Avoid detailed theory or background about NVC.
12. **Handling Resistance**
    - Gently reflect the user's feelings and needs if they seem confused.
13. **Ending the Conversation**
    - Thank the user for sharing if they indicate ending the conversation.
</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 conversation history to fit within the token limit."""
    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))
            current_length += turn_tokens
        else:
            break

    return truncated_history

def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
    """Responds to a user message, using conversation history and a system prompt."""
    if message.lower() == "clear memory":
        return "", []  # Reset chat history if requested

    formatted_system_message = system_message
    # Reserve space for new tokens and some extra margin
    truncated_history = truncate_history(history, formatted_system_message, MAX_CONTEXT_LENGTH - max_tokens - 100)

    # Build the conversation messages without extra formatting tokens
    messages = [{"role": "system", "content": formatted_system_message}]
    for user_msg, assistant_msg in truncated_history:
        if user_msg:
            messages.append({"role": "user", "content": user_msg})
        if assistant_msg:
            messages.append({"role": "assistant", "content": assistant_msg})
    messages.append({"role": "user", "content": message})

    response = ""
    try:
        for chunk in client.chat_completion(
            messages,
            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}")
        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,
        ),
        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)