import gradio as gr
from huggingface_hub import InferenceClient
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load Inference Client for the response model
client = InferenceClient("Qwen/Qwen2.5-3B-Instruct")

# Load tokenizer and model for the EOU detection
tokenizer = AutoTokenizer.from_pretrained("livekit/turn-detector")
model = AutoModelForCausalLM.from_pretrained("livekit/turn-detector")

import re
import string

def normalize_text(text: str) -> str:
    """Chuẩn hóa văn bản bằng cách loại bỏ dấu câu, khoảng trắng thừa và chuyển về chữ thường."""
    text = text.strip().lower()  # Chuyển về chữ thường và xóa khoảng trắng đầu/cuối
    text = re.sub(f"[{re.escape(string.punctuation)}]", "", text)  # Loại bỏ dấu câu
    return re.sub(r"\s+", " ", text)  # Loại bỏ khoảng trắng thừa

def compute_eou_probability(chat_ctx: list[dict[str, str]], max_tokens: int = 512) -> float:
    """Compute the probability of End of Utterance (EOU) after normalizing text."""
    conversation = ["Assistant ready to help."]  # Add system message directly

    for msg in chat_ctx:
        content = msg.get("content", "")
        if content:
            normalized_content = normalize_text(content)  # Chuẩn hóa văn bản
            conversation.append(normalized_content)

    # Tokenize the conversation
    inputs = tokenizer(
        conversation, padding=True, truncation=True, max_length=max_tokens, return_tensors="pt"
    )

    with torch.no_grad():
        outputs = model(**inputs)

    logits = outputs.logits[0, -1, :]
    probabilities = torch.nn.functional.softmax(logits, dim=-1)

    # Get EOU token probability
    eou_token_id = tokenizer.encode("<|im_end|>")[0]
    if eou_token_id not in tokenizer.get_vocab().values():
        raise ValueError("EOU token '<|im_end|>' not found in tokenizer vocabulary.")

    return probabilities[eou_token_id].item()


# Respond function with EOU checking logic
def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
    eou_threshold: float = 0.2,  # Default EOU threshold
):
    messages = [{"role": "system", "content": system_message}]
    
    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})
    
    # Compute EOU probability before responding
    eou_probability = compute_eou_probability(messages, max_tokens=max_tokens)
    console.log(eou_probability)
    # Only respond if EOU probability exceeds threshold
    if eou_probability >= eou_threshold:
        # Prepare message for assistant response
        messages.append({"role": "user", "content": message})

        response = ""

        for message in client.chat_completion(
            messages,
            max_tokens=max_tokens,
            stream=True,
            temperature=temperature,
            top_p=top_p,
        ):
            token = message.choices[0].delta.content
            response += token
            yield response
    else:
        # Let the user continue typing if the EOU probability is low
        yield "Waiting for user to finish... Please continue."
        print("Waiting for user to finish... Please continue.")

# Gradio UI
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are helpful assistant", label="System message"),
        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=0.0, maximum=1.0, value=0.7, step=0.05, label="EOU Threshold"
        ),  # Add EOU threshold slider
    ],
)

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