app.py

import gradio as gr
import subprocess
import os

from huggingface_hub import HfApi, snapshot_download
from gradio_huggingfacehub_search import HuggingfaceHubSearch

from apscheduler.schedulers.background import BackgroundScheduler

HF_TOKEN = os.environ.get("HF_TOKEN")

api = HfApi()


def process_model(model_id: str, file_path: str, key: str, value: str, hf_token):

    MODEL_NAME = model_id.split("/")[-1]

    FILE_NAME = file_path.split("/")[-1]

    api.snapshot_download(
        repo_id=model_id,
        allow_patterns=file_path,
        local_dir=f"{MODEL_NAME}",
    )
    print("Model downloaded successully!")

    metadata_update = f"python llama.cpp/gguf-py/scripts/gguf_set_metadata.py {MODEL_NAME}/{file_path} {key} {value}"
    subprocess.run(metadata_update, shell=True)
    print(f"Model metadata {key} updated to {value} successully!")

    # Upload gguf files
    api.upload_folder(
        folder_path=MODEL_NAME,
        repo_id=model_id,
        allow_patterns=["*.gguf", "$.md"],
        token=hf_token,
    )
    print("Uploaded successfully!")

    return "Processing complete."


# Create Gradio interface
iface = gr.Interface(
    fn=process_model,
    inputs=[
        gr.Textbox(lines=1, label="Model ID"),
        gr.Textbox(lines=1, label="File path"),
        gr.Textbox(lines=1, label="Key"),
        gr.Textbox(lines=1, label="Value"),
        gr.Textbox(lines=1, label="Token"),
    ],
    outputs="text",
)

# Launch the interface
iface.launch(debug=True)


def restart_space():
    HfApi().restart_space(
        repo_id="bartowski/gguf-metadata-updated", token=HF_TOKEN, factory_reboot=True
    )


scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=21600)
scheduler.start()

# Launch the interface
demo.queue(default_concurrency_limit=1, max_size=5).launch(debug=True, show_api=False)