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)