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="baconnier/Finance_dolphin-2.9.1-yi-1.5-34b_GGUF", filename="Finance_dolphin-2.9.1-yi-1.5-34b-Q8_0.gguf",  local_dir = "./models")
hf_hub_download(repo_id="baconnier/Finance_dolphin-2.9.1-yi-1.5-9b_GGUF", filename="Finance_dolphin-2.9.1-yi-1.5-9b_Q8_0.gguf",  local_dir = "./models")
#hf_hub_download(repo_id="baconnier/finance_dolphin_orpo_llama3_8B_r64_51K_GGUF", filename="finance_dolphin_orpo_llama3_8B_r64_51K_GGUF-unsloth.Q8_0.gguf",  local_dir = "./models")
#hf_hub_download(repo_id="crusoeai/dolphin-2.9.1-llama-3-8b-GGUF", filename="dolphin-2.9.1-llama-3-8b.Q6_K.gguf",  local_dir = "./models")

css = """
.message-row {
    justify-content: space-evenly !important;
}
.message-bubble-border {
    border-radius: 6px !important;
}
.dark.message-bubble-border {
    border-color: #21293b !important;
}
.dark.user {
    background: #0a1120 !important;
}
.dark.assistant {
    background: transparent !important;
}
"""

PLACEHOLDER = """
<div class="message-bubble-border" style="display:flex; max-width: 600px; border-radius: 8px; box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); backdrop-filter: blur(10px);">
    <figure style="margin: 0;">
        <img src="https://huggingface.co/spaces/baconnier/Finance/resolve/main/banker.jpg" style="width: 100%; height: 100%; border-radius: 8px;">
    </figure>
    <div style="padding: .5rem 1.5rem;">
        <img src="https://huggingface.co/spaces/baconnier/Finance/resolve/main/banker_plus.jpg" style="width: 100%; height: 10%; border-radius: 8px;">    
        <h2 style="text-align: left; font-size: 1.5rem; font-weight: 700; margin-bottom: 0.5rem;"> </h2>
        <p style="text-align: left; font-size: 16px; line-height: 1.5; margin-bottom: 15px;">Banker++ is trained to act like a Senior Banker. Use this template for learning purposes only.</p>
    </div>    
</div>
"""

@spaces.GPU(duration=120)
def respond(
    message,
    history: list[tuple[str, str]],
    max_tokens,
    temperature,
    top_p,
    top_k,
    repeat_penalty,
    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
    print(message)
    print(history)
    
    llm = Llama(
        model_path=f"models/{model}",
        flash_attn=True,
        n_threads=40,
        n_gpu_layers=81,
        n_batch=1024,
        n_ctx=8192,
    )
    provider = LlamaCppPythonProvider(llm)

    agent = LlamaCppAgent(
        provider,
        system_prompt="You are Alan, a financial analyst.",
        predefined_messages_formatter_type=MessagesFormatterType.CHATML,
        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

demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Slider(minimum=1, maximum=8192, value=8192, 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",
        ),
        gr.Dropdown(["Finance_dolphin-2.9.1-yi-1.5-9b_Q8_0.gguf",'Finance_dolphin-2.9.1-yi-1.5-34b-Q8_0.gguf'], value="Finance_dolphin-2.9.1-yi-1.5-34b-Q8_0.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_border_width="1px",
        block_title_background_fill_dark="#070d1b",
        #input_background_fill_dark="#0c1425",
        button_secondary_background_fill_dark="#070d1b",
        border_color_primary_dark="#21293b",
        background_fill_secondary_dark="#0f172a",
        color_accent_soft_dark="transparent"
    ),
    css=css,
    retry_btn="Retry",
    undo_btn="Undo",
    clear_btn="Clear",
    submit_btn="Send",
    description="BANKER++ is fine-tuned on Cognitive Computation: Chat Dolphin 🐬 2.9.1-yi-1.5-34b",
    chatbot=gr.Chatbot(scale=1, placeholder=PLACEHOLDER)
)

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