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

# Modeli indirme
hf_hub_download(
    repo_id="CerebrumTech/cere-gemma-2-9b-tr",
    filename="unsloth.F16.gguf",
    local_dir="./models"
)

# Yanıt üretme fonksiyonu
@spaces.GPU(duration=120)
def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    model,
    max_tokens,
    temperature,
    top_p,
    top_k,
    repetition_penalty,
):
    chat_template = MessagesFormatterType.VICUNA 

    llm = Llama(
        model_path=f"models/unsloth.F16.gguf",
        flash_attn=True,
        n_gpu_layers=81,
        n_batch=1024,
        n_ctx=8192,
    )
    provider = LlamaCppPythonProvider(llm)

    agent = LlamaCppAgent(
        provider,
        system_prompt=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 = repetition_penalty
    settings.stream = True

    messages = BasicChatHistory()

    for user_msg, assistant_msg in history:
        user = {
            'role': Roles.user,
            'content': user_msg
        }
        assistant = {
            'role': Roles.assistant,
            'content': assistant_msg
        }
        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

# Arayüz oluşturma fonksiyonu
def create_interface(model_name, description):
    return gr.ChatInterface(
        fn=respond,
        additional_inputs=[
            gr.Textbox(value="", label="System message"),
            gr.Textbox(value=model_name, label="Model", interactive=False),
            gr.Slider(minimum=1, maximum=4096, value=2048, step=1, label="Max tokens"),
            gr.Slider(minimum=0.1, maximum=4.0, value=0.1, 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"),
        ],
        title=model_name,
        description=description,
    )

# Açıklama ve arayüz oluşturma
description = """<p align="center">CerebrumTech/cere-gemma-2-9b-tr</p>"""
interface = create_interface('Cere-Gemma-2-9b', description)

# Gradio uygulamasını başlatma
if __name__ == "__main__":
    interface.launch()