app.py

import gradio as gr

from gradio_client import Client
from huggingface_hub import get_token, InferenceClient
from llama_cpp import Llama


llm = Llama.from_pretrained(
    repo_id="prithivMLmods/SmolLM2-135M-Instruct-GGUF",
	filename="SmolLM2-135M-Instruct.Q5_K_M.gguf",
    verbose=False,
)


def generate(
    user_prompt: str,
    system_prompt: str = "You are a helpful assistant.",
    max_tokens: int = 4000,
    temperature: float = 0.2,
    top_p: float = 0.95,
    top_k: int = 40,
):
    messages = [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": user_prompt},
    ]
    return llm.create_chat_completion(
        messages,
        max_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
        top_k=top_k,
    )

with gr.Blocks() as demo:
    gr.Markdown("""# RAG - generate
                
                Generate a response to a query using a [HuggingFaceTB/SmolLM2-360M-Instruct and llama-cpp-python](https://huggingface.co./HuggingFaceTB/SmolLM2-360M-Instruct-GGUF?library=llama-cpp-python).
                
                Part of [ai-blueprint](https://github.com/huggingface/ai-blueprint) - a blueprint for AI development, focusing on applied examples of RAG, information extraction, analysis and fine-tuning in the age of LLMs and agents.""")

    with gr.Row():
        system_prompt = gr.Textbox(label="System prompt", lines=3, value="You are a helpful assistant.")
        user_prompt = gr.Textbox(label="Query", lines=3)

    with gr.Accordion("kwargs"):
        with gr.Row(variant="panel"):
            max_tokens = gr.Number(label="Max tokens", value=512)
            temperature = gr.Number(label="Temperature", value=0.2)
            top_p = gr.Number(label="Top p", value=0.95)
            top_k = gr.Number(label="Top k", value=40)

    submit_btn = gr.Button("Submit")
    response_output = gr.Textbox(label="Response", lines=10)
    documents_output = gr.Dataframe(
        label="Documents", headers=["chunk", "url", "distance", "rank"], wrap=True
    )

    submit_btn.click(
        fn=generate,
        inputs=[
            user_prompt,
            system_prompt,
            max_tokens,
            temperature,
            top_p,
            top_k,
        ],
        outputs=[response_output],
    )

demo.launch()