app.py

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

subprocess.run('pip install llama-cpp-python==0.2.75 --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu124', shell=True)
subprocess.run('pip install llama-cpp-agent==0.2.10', shell=True)

hf_hub_download(repo_id="bartowski/dolphin-2.9.1-yi-1.5-34b-GGUF", filename="dolphin-2.9.1-yi-1.5-34b-Q6_K.gguf",  local_dir = "./models")
hf_hub_download(repo_id="bartowski/dolphin-2.9.1-yi-1.5-9b-GGUF", filename="dolphin-2.9.1-yi-1.5-9b-f32.gguf",  local_dir = "./models")

css = """
.message-row {
    justify-content: space-evenly !important;
}
.message-bubble-border {
    border-radius: 6px !important;
    border-color: #21293b !important;
}
.user {
    background: #1e293b !important;
}
.assistant, .pending {
    background: #0f172a !important;
}
"""

@spaces.GPU(duration=120)
def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
    model,
):
    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

    llm = Llama(
        model_path=f"models/{model}",
        n_gpu_layers=81,
    )
    provider = LlamaCppPythonProvider(llm)

    agent = LlamaCppAgent(
        provider,
        system_prompt="You are a helpful assistant.",
        predefined_messages_formatter_type=MessagesFormatterType.CHATML,
        debug_output=True
    )
    
    settings = provider.get_provider_default_settings()
    settings.max_tokens = max_tokens
    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

demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Slider(minimum=1, maximum=8192, value=8192, 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.Dropdown(['dolphin-2.9.1-yi-1.5-34b-Q6_K.gguf', 'dolphin-2.9.1-yi-1.5-9b-f32.gguf'], value="dolphin-2.9.1-yi-1.5-34b-Q6_K.gguf", label="Model"),
    ],
    theme=gr.themes.Soft(primary_hue="indigo", secondary_hue="blue", neutral_hue="gray",font=[gr.themes.GoogleFont("Exo"), "ui-sans-serif", "system-ui", "sans-serif"]).set(
        body_background_fill_dark="#0f172a",
        block_background_fill_dark="#0f172a",
        block_title_background_fill_dark="#0c1425",
        input_background_fill_dark="#0c1425",
        button_secondary_background_fill_dark="#0c1425",
        border_color_primary_dark="#21293b",
        background_fill_secondary_dark="#0f172a"
    ),
    css=css,
    retry_btn="Retry",
    undo_btn="Undo",
    clear_btn="Clear",
    submit_btn="Send",
    description="Cognitive Computation: 🐬 Chat multi llm"
)

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