app.py

# Importing required libraries
import warnings
warnings.filterwarnings("ignore")

import json
import subprocess
from llama_cpp import Llama
from llama_cpp_agent import LlamaCppAgent
from llama_cpp_agent import 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
import gradio as gr
from huggingface_hub import hf_hub_download


# Download gguf model files
llm = None
llm_model = None

hf_hub_download(
    repo_id="bartowski/Qwen2.5-Coder-1.5B-Instruct-GGUF",
    filename="Qwen2.5-Coder-1.5B-Instruct-Q6_K.gguf",
    local_dir="./models",
)
hf_hub_download(
    repo_id="bartowski/Qwen2.5-Coder-3B-Instruct-GGUF",
    filename="Qwen2.5-Coder-3B-Instruct-Q5_K_S.gguf",
    local_dir="./models",
)

# Set the title and description
title = "Qwen-Coder Llama.cpp"
description = """Qwen2.5-Coder, a six-model family of LLMs, boasts enhanced code generation, reasoning, and debugging. Trained on 5.5 trillion tokens, its 32B parameter model rivals GPT-4o, offering versatile capabilities for coding and broader applications."""


def respond(
    message,
    history: list[tuple[str, str]],
    model,
    system_message,
    max_tokens,
    temperature,
    top_p,
    top_k,
    repeat_penalty,
):
    """
    Respond to a message using the Dolphin-3 model via Llama.cpp.

    Args:
        - message (str): The message to respond to.
        - history (list[tuple[str, str]]): The chat history.
        - model (str): The model to use.
        - system_message (str): The system message to use.
        - max_tokens (int): The maximum number of tokens to generate.
        - temperature (float): The temperature of the model.
        - top_p (float): The top-p of the model.
        - top_k (int): The top-k of the model.
        - repeat_penalty (float): The repetition penalty of the model.

    Returns:
        str: The response to the message.
    """
    # Load the global variables
    global llm
    global llm_model

    # Load the model
    if llm is None or llm_model != model:
        llm = Llama(
            model_path=f"models/{model}",
            flash_attn=False,
            n_gpu_layers=0,
            n_batch=32,
            n_ctx=8192,
        )
        llm_model = model
    provider = LlamaCppPythonProvider(llm)

    # Create the agent
    agent = LlamaCppAgent(
        provider,
        system_prompt=f"{system_message}",
        predefined_messages_formatter_type=MessagesFormatterType.CHATML,
        debug_output=True,
    )

    # Set the settings like temperature, top-k, top-p, max tokens, etc.
    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 the chat 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)

    # Get the response stream
    stream = agent.get_chat_response(
        message,
        llm_sampling_settings=settings,
        chat_history=messages,
        returns_streaming_generator=True,
        print_output=False,
    )

    # Generate the response
    outputs = ""
    for output in stream:
        outputs += output
        yield outputs


# Create a chat interface
demo = gr.ChatInterface(
    respond,
    additional_inputs_accordion=gr.Accordion(
        label="⚙️ Parameters", open=False, render=False
    ),
    additional_inputs=[
        gr.Dropdown(
            choices=[
                "Qwen2.5-Coder-1.5B-Instruct-Q6_K.gguf",
                "Qwen2.5-Coder-3B-Instruct-Q5_K_S.gguf",
            ],
            value="Qwen2.5-Coder-1.5B-Instruct-Q6_K.gguf",
            label="Model",
            info="Select the AI model to use for chat",
        ),
        gr.Textbox(
            value="You are Qwen, created by Alibaba Cloud. You are a helpful assistant.",
            label="System Prompt",
            info="Define the AI assistant's personality and behavior",
            lines=2,
        ),
        gr.Slider(
            minimum=512,
            maximum=4096,
            value=2048,
            step=512,
            label="Max Tokens",
            info="Maximum length of response (higher = longer replies)",
        ),
        gr.Slider(
            minimum=0.1,
            maximum=2.0,
            value=0.7,
            step=0.1,
            label="Temperature",
            info="Creativity level (higher = more creative, lower = more focused)",
        ),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p",
            info="Nucleus sampling threshold",
        ),
        gr.Slider(
            minimum=1,
            maximum=100,
            value=40,
            step=1,
            label="Top-k",
            info="Limit vocabulary choices to top K tokens",
        ),
        gr.Slider(
            minimum=1.0,
            maximum=2.0,
            value=1.1,
            step=0.1,
            label="Repetition Penalty",
            info="Penalize repeated words (higher = less repetition)",
        ),
    ],
    theme="Ocean",
    submit_btn="Send",
    stop_btn="Stop",
    title=title,
    description=description,
    chatbot=gr.Chatbot(scale=1, show_copy_button=True),
    flagging_mode="never",
)


# Launch the chat interface
if __name__ == "__main__":
    demo.launch(debug=False)