import os
import gradio as gr
from llama_cpp import Llama
from llama_cpp_agent import LlamaCppAgent, MessagesFormatterType
from llama_cpp_agent.providers import LlamaCppPythonProvider
from llama_cpp_agent.chat_history import BasicChatHistory
from llama_cpp_agent.chat_history.messages import Roles
from huggingface_hub import hf_hub_download

# Set environment variable for GPU usage
os.environ['CUDA_VISIBLE_DEVICES'] = '0'

huggingface_token = os.getenv("HF_TOKEN")

# Download the Meta-Llama-3.1-8B-Instruct model
hf_hub_download(
    repo_id="itaudit/2024-08-14_V3_394_sample",
    filename="V3_394_sample_ISO_unsloth.Q4_K_M.gguf",
    local_dir="./models",
    token=huggingface_token
)

llm = None
llm_model = None

def respond(
    message,
    history: list[tuple[str, str]],
    model,
    system_message,
    max_tokens,
    temperature,
    top_p,
    top_k,
    repeat_penalty,
):
    chat_template = MessagesFormatterType.ALPACA

    global llm
    global llm_model
    
    # Load model only if it's not already loaded or if a new model is selected
    if llm is None or llm_model != model:
        try:
            llm = Llama(
                model_path=f"models/{model}",
                n_ctx=2048,
                n_gpu_layers=-1,  # Use all GPU layers
                main_gpu=0,  # Use the first GPU
                tensor_split=None,  # Auto split tensors across GPUs
            )
            llm_model = model
        except Exception as e:
            return f"Error loading model: {str(e)}"

    provider = LlamaCppPythonProvider(llm)

    agent = LlamaCppAgent(
        provider,
        system_prompt=f"{system_message}",
        predefined_messages_formatter_type=chat_template,
        debug_output=True
    )
    
    settings = provider.get_provider_default_settings()
    settings.temperature = temperature
    settings.top_k = top_k
    settings.top_p = top_p
    settings.max_tokens = max_tokens
    settings.repeat_penalty = repeat_penalty
    settings.stream = True

    messages = BasicChatHistory()

    # Add user and assistant messages to the history
    for msn in history:
        user = {'role': Roles.user, 'content': msn[0]}
        assistant = {'role': Roles.assistant, 'content': msn[1]}
        messages.add_message(user)
        messages.add_message(assistant)
    
    # Stream the response
    try:
        stream = agent.get_chat_response(
            message,
            llm_sampling_settings=settings,
            chat_history=messages,
            returns_streaming_generator=True,
            print_output=False
        )
        
        outputs = ""
        for output in stream:
            outputs += output
            yield outputs
    except Exception as e:
        yield f"Error during response generation: {str(e)}"

description = """<p align="center">Using the Meta-Llama-3.1-8B-Instruct Model (GPU Version) with ISO</p>"""

demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Dropdown([
                'V3_394_sample_ISO_unsloth.Q4_K_M.gguf'
            ],
            value="V3_394_sample_ISO_unsloth.Q4_K_M.gguf",
            label="Model"
        ),
        gr.Textbox(value="You are a helpful assistant.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=1024, step=1, label="Max tokens"),
        gr.Slider(minimum=0.1, maximum=2.0, 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",
        ),
        gr.Slider(
            minimum=0,
            maximum=100,
            value=50,
            step=1,
            label="Top-k",
        ),
        gr.Slider(
            minimum=0.0,
            maximum=2.0,
            value=1.1,
            step=0.1,
            label="Repetition penalty",
        ),
    ],
    retry_btn="Retry",
    undo_btn="Undo",
    clear_btn="Clear",
    submit_btn="Send",
    title="Chat with Llama-3.1-8B using llama.cpp custom dataset V3 (GPU Version)", 
    description=description,
    chatbot=gr.Chatbot(
        scale=1, 
        likeable=False,
        show_copy_button=True
    )
)

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