import gradio as gr
from huggingface_hub import InferenceClient
from transformers import LlamaTokenizer  # Use LlamaTokenizer instead of AutoTokenizer

# Load the correct tokenizer
tokenizer = LlamaTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

# Define max context length (tokens)
MAX_CONTEXT_LENGTH = 4096  

default_nvc_prompt_template = """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..."""

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."""
    truncated_history = []
    system_message_tokens = count_tokens(system_message)
    current_length = system_message_tokens

    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 if limit exceeded

    return truncated_history

def respond(message, history, system_message, max_tokens, temperature, top_p):
    """Handles user message and generates a response."""
    
    if message.lower() == "clear memory":
        return "", []  # Reset chat history

    formatted_system_message = system_message
    truncated_history = truncate_history(history, formatted_system_message, MAX_CONTEXT_LENGTH - max_tokens - 100)

    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"Error: {e}")
        yield "I'm sorry, I encountered an error. Please try again."

# Build Gradio UI
demo = gr.ChatInterface(
    fn=respond,
    additional_inputs=[
        gr.Textbox(value=default_nvc_prompt_template, label="System message", 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)