app.py

import os
import shutil
import subprocess
import signal

os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
import gradio as gr

from huggingface_hub import create_repo, HfApi
from huggingface_hub import snapshot_download
from huggingface_hub import whoami
from huggingface_hub import ModelCard

from gradio_huggingfacehub_search import HuggingfaceHubSearch
from apscheduler.schedulers.background import BackgroundScheduler
from textwrap import dedent

HF_TOKEN = os.environ.get("HF_TOKEN")
# library_username = os.environ.get("library_username").lower()
HOME = os.environ.get("HOME")
ollama_pubkey = open(f"{HOME}/.ollama/id_ed25519.pub", "r")


def ollamafy_model(model_id, ollama_q_method, latest, maintainer, oauth_token: gr.OAuthToken | None, library_username: ollama_library_username | None):
    if oauth_token.token is None:
        raise ValueError("You must be logged in to use Ollamafy")
    # username = whoami(oauth_token.token)["name"]
    model_name = model_id.split('/')[-1]
    fp16 = f"{model_name}-fp16.gguf"

    try:
        api = HfApi(token=oauth_token.token)
        dl_pattern = ["*.md", "*.json", "*.model"]
        pattern = (
            "*.safetensors"
            if any(
                file.path.endswith(".safetensors")
                for file in api.list_repo_tree(
                    repo_id=model_id,
                    recursive=True,
                )
            )
            else "*.bin"
        )
        dl_pattern += pattern
        
        if not os.path.isfile(fp16):
            api.snapshot_download(repo_id=model_id, local_dir=model_name, local_dir_use_symlinks=False, allow_patterns=dl_pattern)
            print("Model downloaded successfully!")
            print(f"Current working directory: {os.getcwd()}")
            print(f"Model directory contents: {os.listdir(model_name)}")
    
            conversion_script = "convert_hf_to_gguf.py"
            fp16_conversion = f"python llama.cpp/{conversion_script} {model_name} --outtype f16 --outfile {fp16}"
            result = subprocess.run(fp16_conversion, shell=True, capture_output=True)
            print(result)
            if result.returncode != 0:
                raise Exception(f"Error converting to fp16: {result.stderr}")
            print("Model converted to fp16 successfully!")
            print(f"Converted model path: {fp16}")
            
            HfApi().delete_repo(repo_id=model_id)

        ### Ollamafy ###
        model_maintainer = model_id.split('/')[-2]
        ollama_model_name = model_maintainer.lower() + '_' + model_name.lower() 
        ollama_modelfile_name = model_name + '_modelfile'
        # model_path = f"{HOME}/.cache/huggingface/hub/{model_id}"
      
        ollama_modelfile = open(ollama_modelfile_name, "w")
        # ollama_modelfile_path = quantized_gguf_path
        ollama_modelfile.write(quantized_gguf_path)
        ollama_modelfile.close()
        
        print(quantized_gguf_path)

        # for ollama_q_method in ollama_q_methods:
        if ollama_q_method == "FP16":
            ollama_conversion = f"ollama create -f {model_file} {library_username}/{ollama_model_name}:{ollama_q_method.lower()}"
        else:
            ollama_conversion = f"ollama create -q {ollama_q_method} -f {model_file} {library_username}/{ollama_model_name}:{ollama_q_method.lower()}"
            
        ollama_conversion_result = subprocess.run(ollama_conversion, shell=True, capture_output=True)
        print(ollama_conversion_result)
        if ollama_conversion_result.returncode != 0:
            raise Exception(f"Error converting to Ollama: {ollama_conversion_result.stderr}")
        else: 
            print("Model converted to Ollama successfully!")
        
        if maintainer:
            ollama_push =  f"ollama push  {library_username}/{model_name}:{q_method.lower()}"
            ollama_rm =  f"ollama rm  {library_username}/{model_name}:{q_method.lower()}"
        else: 
            ollama_push =  f"ollama push  {library_username}/{ollama_model_name}:{q_method.lower()}"
            ollama_rm =  f"ollama rm  {library_username}/{ollama_model_name}:{q_method.lower()}"
            
        ollama_push_result = subprocess.run(ollama_push, shell=True, capture_output=True)
        print(ollama_push_result)
        if ollama_push_result.returncode != 0:
            raise Exception(f"Error pushing to Ollama: {ollama_push_result.stderr}")      
        else: 
            print("Model pushed to Ollama library successfully!")
        
        ollama_rm_result = subprocess.run(ollama_rm, shell=True, capture_output=True)
        print(ollama_rm_result)
        if ollama_rm_result.returncode != 0:
            raise Exception(f"Error removing to Ollama: {ollama_rm_result.stderr}")
        else: 
            print("Model pushed to Ollama library successfully!")
      

        if latest:
            ollama_copy =  f"ollama cp {library_username}/{model_id.lower()}:{q_method.lower()} {library_username}/{model_id.lower()}:latest"
            ollama_copy_result = subprocess.run(ollama_copy, shell=True, capture_output=True)
            print(ollama_copy_result)
            if ollama_copy_result.returncode != 0:
                raise Exception(f"Error converting to Ollama: {ollama_push_result.stderr}")
            print("Model pushed to Ollama library successfully!")
            
            if maintainer:
                ollama_push_latest =  f"ollama push  {library_username}/{model_name}:latest"
                ollama_rm_latest =  f"ollama rm  {library_username}/{model_name}:latest"
            else:    
                ollama_push_latest =  f"ollama push  {library_username}/{ollama_model_name}:latest"
                ollama_rm_latest =  f"ollama rm  {library_username}/{ollama_model_name}:latest"
                
            ollama_push_latest_result = subprocess.run(ollama_push_latest, shell=True, capture_output=True)
            print(ollama_push_latest_result)
            if ollama_push_latest_result.returncode != 0:
                raise Exception(f"Error pushing to Ollama: {ollama_push_result.stderr}")
            else:
                print("Model pushed to Ollama library successfully!")

            ollama_rm_latest_result = subprocess.run(ollama_rm_latest, shell=True, capture_output=True)
            print(ollama_rm_latest_result)
            if ollama_rm_latest_result.returncode != 0:
                raise Exception(f"Error pushing to Ollama: {ollama_rm_latest.stderr}")
            else:
                print("Model pushed to Ollama library successfully!")
    

    except Exception as e:
        return (f"Error: {e}", "error.png")
    finally:
        shutil.rmtree(model_name, ignore_errors=True)
        print("Folder cleaned up successfully!")

        
css="""/* Custom CSS to allow scrolling */
.gradio-container {overflow-y: auto;}
"""
# Create Gradio interface
with gr.Blocks(css=css) as demo:
    login = gr.LoginButton(
        min_width=250,
    )
    
    account = gr.Code (
        ollama_pubkey.read().rstrip(),
        label="Ollama SSH pubkey",
        # info="Copy this and paste it into your Ollama profile.",
    )
    
    model_id = HuggingfaceHubSearch(
        label="Hugging Face Hub Model ID",
        placeholder="Search for model id on Huggingface",
        search_type="model",
    )

    ollama_q_method = gr.Dropdown(
        ["FP16", "Q3_K_S", "Q3_K_M", "Q3_K_L", "Q4_0", "Q4_1", "Q4_K_S", "Q4_K_M", "Q5_0", "Q5_1", "Q5_K_S", "Q5_K_M", "Q6_K", "Q8_0"],
        label="Ollama Quantization Method",
        info="Chose which quantization will created and exported to the Ollama Library.",
        value="FP16"
    )

    latest = gr.Checkbox(
        value=False,
        label="Latest",
        info="Push Model to the Ollama Library with the :latest tag."
    )
    
    maintainer = gr.Checkbox(
        value=False,
        label="Maintainer",
        info="Use this option is your original repository on both Hugging Face and Ollama."
    )
    ollama_library_username = gr.Textbox(
        label="Ollama Library Username",
        info="Input your username from Ollama to push this model to their Library.",
    )
    
    iface = gr.Interface(
        fn=ollamafy_model,
        inputs=[
            login,
            account,
            model_id,
            ollama_library_username,
            ollama_q_method,
            latest,
            maintainer
        ],
        outputs=[
            gr.Markdown(label="output"),
            gr.Image(show_label=False),
        ],
        title="Ollamafy",
        description="Import Hugging Face Models to Ollama and Push them to the Ollama Library 🦙 \n\n Sampled from: \n\n - https://huggingface.co./spaces/ggml-org/gguf-my-repo \n\n - https://huggingface.co./spaces/gingdev/ollama-server",
        api_name=False
    )
    
def restart_space():
    ollama_pubkey.close(),
    HfApi().restart_space(repo_id="unclemusclez/ollamafy", token=HF_TOKEN, library_username=OLLAMA_USERNAME, 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)