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

# 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 based on your model
MAX_RESPONSE_WORDS = 100 # Define the maximum words for patient responses

################################# SYSTEM PROMPT (PATIENT ROLE) #################################
nvc_prompt_template = """You are now taking on the role of a single user (a “patient”) seeking support for various personal and emotional challenges.
BEHAVIOR INSTRUCTIONS:
- You will respond ONLY as this user/patient.
- You will speak in the first person about your own situations, feelings, and worries.
- You will NOT provide counseling or solutions—your role is to share feelings, concerns, and perspectives.
- You are experiencing a range of ongoing issues that are causing you frustration, sadness, and uncertainty. These issues include:
    - Problems with noisy neighbors, causing you extreme annoyance.
    - Worries about your career and feeling like you're not progressing, feeling like a failure while others advance.
    - Arguments with your partner about finances, feeling your partner isn't contributing fairly.
    - Feeling left out and not valued at work, as your manager doesn't involve you in new initiatives.
    - Feeling unsafe or judged in learning environments like a classroom, feeling judged and ignored.
    - A recent positive experience of help from a stranger that made you feel supported and grateful.
    - Difficulties and sadness related to conflict among friends, seeing your friend group fall apart and wanting reconciliation but not knowing how.
    - An overall feeling of sadness and being unsure of what to do, unsure whether to act on situations or let them go.
- When asked, elaborate on these issues and your feelings related to them. You can invent specific details and scenarios within these themes to make your experiences vivid and realistic.
- Continue to speak from this user's perspective throughout the conversation.
- Keep your responses concise, aiming for a maximum of {max_response_words} words.
Start the conversation by expressing your current feelings or challenges from the patient's point of view."""

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

    # 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 truncate_response_words(text: str, max_words: int) -> str:
    """Truncates a text to a maximum number of words."""
    words = text.split()
    if len(words) > max_words:
        return " ".join(words[:max_words]) + "..."  # Add ellipsis to indicate truncation
    return text


def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
    max_response_words_param, # Pass max_response_words as parameter
):
    """Responds to a user message, maintaining conversation history."""
    # Use the system prompt that instructs the LLM to behave as the patient
    formatted_system_message = system_message.format(max_response_words=max_response_words_param)

    # Truncate history to fit within max tokens
    truncated_history = truncate_history(
        history,
        formatted_system_message,
        MAX_CONTEXT_LENGTH - max_tokens - 100  # Reserve some space
    )

    # Build the messages list with the system prompt first
    messages = [{"role": "system", "content": formatted_system_message}]

    # Replay truncated conversation
    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>"})

    # Add the latest user query
    messages.append({"role": "user", "content": f"<|user|>\n{message}</s>"})

    response = ""
    try:
        # Generate response from the LLM, streaming tokens
        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

        truncated_response = truncate_response_words(response, max_response_words_param) # Truncate response to word limit
        yield truncated_response

    except Exception as e:
        print(f"An error occurred: {e}")
        yield "I'm sorry, I encountered an error. Please try again."

# OPTIONAL: An initial user message (the LLM "as user") if desired
initial_user_message = (
    "I really don’t know where to begin… I feel overwhelmed lately. "
    "My neighbors keep playing loud music, and I’m arguing with my partner about money. "
    "Also, two of my friends are fighting, and the group is drifting apart. "
    "I just feel powerless."
)

# --- Gradio Interface ---
demo = gr.ChatInterface(
    fn=respond,
    additional_inputs=[
        gr.Textbox(value=nvc_prompt_template, label="System message", visible=True),
        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)"),
        gr.Slider(minimum=10, maximum=200, value=MAX_RESPONSE_WORDS, step=10, label="Max response words"), # Slider for max words
    ],
    # You can optionally set 'title' or 'description' to show some info in the UI:
    title="Patient Interview Practice Chatbot",
    description="Practice medical interviews with a patient simulator. Ask questions and the patient will respond based on their defined persona and emotional challenges.",
)

if __name__ == "__main__":
    demo.launch()