app.py

import spaces
import json
import subprocess
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
import gradio as gr
from huggingface_hub import hf_hub_download

llm = None
llm_model = None

hf_hub_download(
    repo_id="unsloth/Reflection-Llama-3.1-70B-GGUF",
    filename="Reflection-Llama-3.1-70B.Q3_K_L.gguf",
    local_dir = "./models"
)

hf_hub_download(
    repo_id="jhofseth/Reflection-Llama-3.1-70B-GGUF",
    filename="Reflection-Llama-3.1-70B-IQ3_XXS.gguf",
    local_dir = "./models"
)

hf_hub_download(
    repo_id="bartowski/Reflection-Llama-3.1-70B-GGUF",
    filename="Reflection-Llama-3.1-70B.imatrix",
    local_dir = "./random"
)

def get_messages_formatter_type(model_name):
    if "Llama" in model_name:
        return MessagesFormatterType.LLAMA_3
    else:
        raise ValueError(f"Unsupported model: {model_name}")


@spaces.GPU
def respond(
    message,
    history: list[tuple[str, str]],
    model,
    system_message,
    max_tokens,
    temperature,
    top_p,
    top_k,
    repeat_penalty,
):
    global llm
    global llm_model
    
    chat_template = get_messages_formatter_type(model)
    
    if llm is None or llm_model != model:
        llm = Llama(
            model_path=f"models/{model}",
            flash_attn=True,
            n_gpu_layers=81,
            n_batch=1024,
            n_ctx=8192,
        )
        llm_model = model
    
    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()

    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 = 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

description = """<p><center>
<a href="https://huggingface.co/mattshumer/ref_70_e3" target="_blank">[Reflection Llama 3.1 70B Correct Weights]</a>
<a href="https://huggingface.co/mattshumer/Reflection-Llama-3.1-70B" target="_blank">[Old Repo]</a>
<a href="https://huggingface.co/unsloth/Reflection-Llama-3.1-70B-GGUF" target="_blank">[Reflection-Llama-3.1-70B-GGUF]</a>

</center></p>
"""

demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Dropdown([
                "Reflection-Llama-3.1-70B.Q3_K_L.gguf",
                "Reflection-Llama-3.1-70B-IQ3_XXS.gguf"
            ],
            value="Reflection-Llama-3.1-70B.Q3_K_L.gguf",
            label="Model"
        ),
        gr.Textbox(value="You are a world-class AI system, capable of complex reasoning and reflection. Reason through the query inside <thinking> tags, and then provide your final response inside <output> tags. If you detect that you made a mistake in your reasoning at any point, correct yourself inside <reflection> tags.", label="System message"),
        gr.Slider(minimum=1, maximum=8192, value=2048, step=1, label="Max 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",
        ),
        gr.Slider(
            minimum=0,
            maximum=100,
            value=40,
            step=1,
            label="Top-k",
        ),
        gr.Slider(
            minimum=0.0,
            maximum=2.0,
            value=1.1,
            step=0.1,
            label="Repetition penalty",
        ),
    ],
    theme=gr.themes.Soft(primary_hue="violet", secondary_hue="violet", neutral_hue="gray",font=[gr.themes.GoogleFont("Exo"), "ui-sans-serif", "system-ui", "sans-serif"]).set(
        body_background_fill_dark="#16141c",
        block_background_fill_dark="#16141c",
        block_border_width="1px",
        block_title_background_fill_dark="#1e1c26",
        input_background_fill_dark="#292733",
        button_secondary_background_fill_dark="#24212b",
        border_color_accent_dark="#343140",
        border_color_primary_dark="#343140",
        background_fill_secondary_dark="#16141c",
        color_accent_soft_dark="transparent",
        code_background_fill_dark="#292733",
    ),
    retry_btn="Retry",
    undo_btn="Undo",
    clear_btn="Clear",
    submit_btn="Send",
    title="Reflection Llama-3.1 70B",
    description=description,
    chatbot=gr.Chatbot(
        scale=1, 
        likeable=False,
        show_copy_button=True
    )
)

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