app.py

import os
import gradio as gr
import copy
from llama_cpp import Llama
from huggingface_hub import hf_hub_download  
from huggingface_hub.file_download import http_get

# huggingface-cli download microsoft/Phi-3-mini-4k-instruct-gguf Phi-3-mini-4k-instruct-q4.gguf --local-dir .
# huggingface-cli download LoneStriker/OpenBioLLM-Llama3-8B-GGUF --local-dir ./llama3-gguf
# Explicitly create the cache directory if it doesn't exist


def load_model(
    directory: str = ".",
    model_name: str = "OpenBioLLM-Llama3-8B-Q5_K_M.gguf",
    model_url: str = "https://huggingface.co/LoneStriker/OpenBioLLM-Llama3-8B-GGUF/resolve/main/OpenBioLLM-Llama3-8B-Q5_K_M.gguf"
):
    final_model_path = os.path.join(directory, model_name)
    
    print("Downloading all files...")
    if not os.path.exists(final_model_path):
        with open(final_model_path, "wb") as f:
            http_get(model_url, f)
    os.chmod(final_model_path, 0o777)
    print("Files downloaded!")
    
    model = Llama(
        model_path=final_model_path,
        n_ctx=2000,
        n_parts=1,
    )
    
    print("Model loaded!")
    return model

# llm = Llama(
#     # model_path="./Phi-3-mini-4k-instruct-q4.gguf",
#     # model_path="./llama3-gguf/OpenBioLLM-Llama3-8B-Q5_K_M.gguf",
#     model_path = hf_hub_download(
#         repo_id=os.environ.get("REPO_ID", "LoneStriker/OpenBioLLM-Llama3-8B-GGUF"),
#         filename=os.environ.get("MODEL_FILE", "Llama3-8B-Q5_K_M.gguf"),
#     ),
#     n_ctx=2048,
#     n_gpu_layers=50, # change n_gpu_layers if you have more or less VRAM 
# ) 

llm = load_model()

# print("here")
def generate_text(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    temp = ""
    input_prompt = f"[INST] <<SYS>>\n{system_message}\n<</SYS>>\n\n "
    for interaction in history:
        input_prompt = input_prompt + str(interaction[0]) + " [/INST] " + str(interaction[1]) + " </s><s> [INST] "

    input_prompt = input_prompt + str(message) + " [/INST] "

    output = llm(
        input_prompt,
        temperature=temperature,
        top_p=top_p,
        top_k=40,
        repeat_penalty=1.1,
        max_tokens=max_tokens,
        stop=[
            "<|prompter|>",
            "<|endoftext|>",
            "<|endoftext|> \n",
            "ASSISTANT:",
            "USER:",
            "SYSTEM:",
        ],
        stream=True,
    )
    for out in output:
        stream = copy.deepcopy(out)
        temp += stream["choices"][0]["text"]
        yield temp


demo = gr.ChatInterface(
    generate_text,
    title="llama-cpp-python on CPU",
    description="Running LLM with https://github.com/abetlen/llama-cpp-python",
    examples=[
        ['How to setup a human base on Mars? Give short answer.'],
        ['Explain theory of relativity to me like I’m 8 years old.'],
        ['What is 9,000 * 9,000?'],
        ['Write a pun-filled happy birthday message to my friend Alex.'],
        ['Justify why a penguin might make a good king of the jungle.']
    ],
    cache_examples=False,
    retry_btn=None,
    undo_btn="Delete Previous",
    clear_btn="Clear",
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, 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)",
        ),
    ],
)


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