"""Python Application Script for AI chatbot using LLAMA CPP."""
import logging

import gradio as gr
from llama_cpp import Llama

# Setting up enviornment
log_level = os.environ.get("LOG_LEVEL", "WARNING")
logging.basicConfig(encoding='utf-8', level=log_level)
# Default System Prompt
DEFAULT_SYSTEM_PROMPT = os.environ.get("DEFAULT_SYSTEM", "You are Dolphin, a helpful AI assistant.")
# Model Path
model_path = "model.gguf"
logging.debug("Model Path: %s", model_path)

logging.info("Loading Moddel")
llm = Llama(model_path=model_path, n_ctx=4000, n_threads=2, chat_format="chatml")


def generate(
    message: str,
    history: list[tuple[str, str]],
    system_prompt: str,
    temperature: float = 0.1,
    max_tokens: int = 512,
    top_p: float = 0.95,
    repetition_penalty: float = 1.0,
):
    """Function to generate text.

    :param message: The new user prompt.
    :param history: The history of the chat session.
    :param system: The system prompt of the model.
    :param temperature: The temperature parameter for the model.
    :param max_tokens: The maximum amount of tokens to use for the model.
    :param top_p: The top p value for the model.
    :param repetition_penalty: The repetition penalty for the model.
    """
    logging.info("Generating Text")
    logging.debug("message: %s", message)
    logging.debug("history: %s", history)
    logging.debug("system: %s", system)
    logging.debug("temperature: %s", temperature)
    logging.debug("max_tokens: %s", max_tokens)
    logging.debug("top_p: %s", top_p)
    logging.debug("repetion_penalty: %s", repetition_penalty)

    # Formatting Prompt
    logging.info("Formatting Prompt")
    formatted_prompt = [{"role": "system", "content": system_prompt}]
    for user_prompt, bot_response in history:
        formatted_prompt.append({"role": "user", "content": user_prompt})
        formatted_prompt.append({"role": "assistant", "content": bot_response})
    formatted_prompt.append({"role": "user", "content": message})
    logging.debug("Formatted Prompt: %s", formatted_prompt)

    # Generating Response
    logging.info("Generating Response")
    stream_response = llm.create_chat_completion(
        messages=formatted_prompt,
        temperature=temperature,
        max_tokens=max_tokens,
        top_p=top_p,
        repeat_penalty=repetition_penalty,
        stream=True,
    )

    # Parsing Response
    logging.info("Parsing Response")
    response = ""
    for chunk in stream_response:
        if (
            len(chunk["choices"][0]["delta"]) != 0
            and "content" in chunk["choices"][0]["delta"]
        ):
            response += chunk["choices"][0]["delta"]["content"]
        logging.debug("Response: %s", response)
        yield response


additional_inputs = [
    gr.Textbox(
        label="System Prompt",
        max_lines=1,
        interactive=True,
        value=DEFAULT_SYSTEM_PROMPT,
    ),
    gr.Slider(
        label="Temperature",
        value=0.9,
        minimum=0.0,
        maximum=1.0,
        step=0.05,
        interactive=True,
        info="Higher values produce more diverse outputs",
    ),
    gr.Slider(
        label="Max new tokens",
        value=256,
        minimum=0,
        maximum=1048,
        step=64,
        interactive=True,
        info="The maximum numbers of new tokens",
    ),
    gr.Slider(
        label="Top-p (nucleus sampling)",
        value=0.90,
        minimum=0.0,
        maximum=1,
        step=0.05,
        interactive=True,
        info="Higher values sample more low-probability tokens",
    ),
    gr.Slider(
        label="Repetition penalty",
        value=1.2,
        minimum=1.0,
        maximum=2.0,
        step=0.05,
        interactive=True,
        info="Penalize repeated tokens",
    )
]

examples = []

logging.info("Creating Chatbot")
mychatbot = gr.Chatbot(avatar_images=["user.png", "botsc.png"], bubble_full_width=False, show_label=False, show_copy_button=True, likeable=True,)

logging.info("Creating Chat Interface")
iface = gr.ChatInterface(
    fn=generate,
    chatbot=mychatbot,
    additional_inputs=additional_inputs,
    examples=examples,
    concurrency_limit=20,
    title="LLAMA CPP Template"
)

logging.info("Starting Application")
iface.launch(show_api=False, server_name="0.0.0.0")