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") OLLAMA_USERNAME = os.environ.get("OLLAMA_USERNAME").lower() ollama_pubkey = open("/home/user/.ollama/id_ed25519.pub", "r") ollama_q_methods = ["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"] def process_model(model_id, q_method, use_imatrix, imatrix_q_method, private_repo, train_data_file, split_model, split_max_tensors, split_max_size, ollamafy, latest, maintainer, oauth_token: gr.OAuthToken | None): if oauth_token.token is None: raise ValueError("You must be logged in to use GGUF-my-repo") 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 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}") ### Ollamafy ### if ollama_model: 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 -q {ollama_q_method} -f {model_file} {OLLAMA_USERNAME}/{ollama_model_name}:{ollama_q_method.lower()}" else: ollama_conversion = f"ollama create -f {model_file} {OLLAMA_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}") print("Model converted to Ollama successfully!") if maintainer: ollama_push = f"ollama push {OLLAMA_USERNAME}/{model_name}:{q_method.lower()}" else: ollama_push = f"ollama push {OLLAMA_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 converting to Ollama: {ollama_push_result.stderr}") print("Model pushed to Ollama library successfully!") if latest == True: ollama_copy = f"ollama cp {OLLAMA_USERNAME}/{model_id.lower()}:{q_method.lower()} {OLLAMA_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 == True: llama_push_latest = f"ollama push {OLLAMA_USERNAME}/{model_name}:latest" else: ollama_push_latest = f"ollama push {OLLAMA_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 converting to Ollama: {ollama_push_result.stderr}") 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: gr.LoginButton(min_width=250) gr.Markdown("You must be logged in to use Ollamafy.") gr.Markdown(ollama_pubkey.read().rstrip()) ollama_pubkey.close() model_id = HuggingfaceHubSearch( label="Hub Model ID", placeholder="Search for model id on Huggingface", search_type="model", ) ollama_q_method latest = gr.Dropdown( ollama_q_methods, label="Ollama Lastest Quantization Method", info="Chose which quantization will be labled with the latest tag in the Ollama Library", value="FP16", filterable=False, visible=False ) latest = gr.Checkbox( value=False, label="Latest", info="Copy Model to Ollama Library with the :latest tag" ) maintainer = gr.Checkbox( value=False, label="Maintainer", info="This is your original repository on both Hugging Face and Ollama. (DO NOT USE!!!)" ) iface = gr.Interface( fn=process_model, inputs=[ model_id, ollama_q_method, latest, maintainer ], outputs=[ gr.Markdown(label="output"), gr.Image(show_label=False), ], title="Create your own Ollama Models and Push them to the Ollama Library, blazingly fast ⚡!", description="Sampled from https://huggingface.co/spaces/ggml-org/gguf-my-repo and https://huggingface.co/spaces/gingdev/ollama-server", api_name=False ) #username = whoami(oauth_token.token)["name"] def restart_space(): HfApi().restart_space(repo_id="unclemusclez/ollamafy", 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)