import os
import shutil
import subprocess
import signal
import hashlib

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

HOME = os.environ.get("HOME")
token = os.environ.get("HF_TOKEN")
library_username = os.environ.get("OLLAMA_USERNAME").lower()
ollama_pubkey = None
ollama_model_name = None
download_gguf_link = None
# model.num_parameters()
def regenerate_pubkey(pubkey, oauth_token: gr.OAuthToken | None):
    if oauth_token.token is None:
        raise ValueError("You must be logged in to use Ollamafy")
        
    hash_oauth = hashlib.sha256(b"{oauth_token.token}").hexdigest()
    generate_ollama_host_file = f"echo $(ss -natp | grep (cat ollama.pid) | awk '{{print $4}}') > ollama.host"
    generate_ollama_pid_file = f"echo $! > ollama.pid"
    ollama_pubkey = f"cat {HOME}/{hash_oauth}/.ollama/id_ed25519.pub"  
    ollama_start = f"HOME={HOME}/{hash_oauth} ollama serve & {generate_ollama_pid_file} & sleep 5"    
    ollama_pid = f"cat {HOME}/{hash_oauth}/ollama.pid"
    ollama_stop = f"kill -9 {ollama_pid}"
    delete_home = f"rm -Rf {HOME}/{hash_oauth}/.ollama"
    
    try:
        result = subprocess.run(ollama_pid, shell=True, capture_output=True)
        print(result)
        if result.returncode != 0:
            raise Exception(f"Error retrieving Ollama PID {result.stderr}")
        print(f"Ollama PID Retrived: {ollama_pid}")

        result = subprocess.run(ollama_stop, shell=True, capture_output=True)
        print(result)
        if result.returncode != 0:
            raise Exception(f"Error stoppping Ollama {result.stderr}")
        print("Ollama stopped successfully!")
    
        result = subprocess.run(delete_home, shell=True, capture_output=True)
        print(result)
        if result.returncode != 0:
            raise Exception(f"Error removing Ollama HOME folder {result.stderr}")
        print("Ollama HOME folder removed successfully!")
    
        result = subprocess.run(ollama_start, shell=True, capture_output=True)
        print(result)
        if result.returncode != 0:
            raise Exception(f"Error starting Ollama {result.stderr}")
        print("Ollama started successfully!")

        result = subprocess.run(ollama_pubkey, shell=True, capture_output=True)
        print(result)
        if result.returncode != 0:
            raise Exception(f"Error starting Ollama {result.stderr}")
        print(f"echo $(ss -natp | grep (cat ollama.pid) | awk '{{print $4}}')")
        print("Ollama Pubkey Obtained!")

        result = subprocess.run(generate_ollama_host_file, shell=True, capture_output=True)
        print(result)
        if result.returncode != 0:
            raise Exception(f"Error generating Ollama Host File {result.stderr}")
        print("Ollama Host File Created!")

    except Exception as e:
        return (f"Error: {e}", "error.png")
    finally:
        # shutil.rmtree(model_name, ignore_errors=True)
        print("Ollama Pubkey Generated! Copy to your user profile in the Ollama Library.")

def ollamafy_model(login, model_id, ollama_library_username , ollama_q_method, latest, download_gguf_link, maintainer, oauth_token: gr.OAuthToken | None, ollama_model_name):
    ollama_library_username: library_username | None 
    
    if oauth_token.token is None:
        raise ValueError("You must be logged in to use Ollamafy")
    hash_oauth = hashlib.sha256(b"{oauth_token.token}").hexdigest()

    # username = whoami(oauth_token.token)["name"]
    model_name = model_id.split('/')[-1]
    fp16 = f"{model_name}-fp16.gguf"
    ollama_pid = f"cat {HOME}/{hash_oauth}/ollama.pid"
    ollama_stop = f"kill -9 {ollama_pid}"
    delete_home = f"rm -Rf {HOME}/{hash_oauth}/.ollama"
    download_gguf = f"wget download_gguf_link"

    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) and download_gguf_link is None:         
            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)
        else:
            if urlparse.urlparse(download_gguf_link).scheme and download_gguf_link.file.path.endswith("*.gguf"):
                result = subprocess.run(download_gguf, shell=True, capture_output=True)
                print(result)
                if result.returncode != 0:
                    raise Exception(f"Error downloading GGUF: {result.stderr}")
                print("Downloaded GGUF")
            else:
                print("Invalid GGUF Download Link")
                

        ### Ollamafy ###
        model_maintainer = model_id.split('/')[-2]
        if ollama_model_name is None:
            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)

        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_HOST={ollama_host} ollama {library_username}/{model_name}:{q_method.lower()}"
            ollama_rm =  f"ollama rm  {library_username}/{model_name}:{q_method.lower()}"
        else: 
            ollama_push =  f"OLLAMA_HOST={ollama_host} 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_HOST={ollama_host} ollama push {library_username}/{model_name}:latest"
                ollama_rm_latest =  f"ollama rm  {library_username}/{model_name}:latest"
            else:    
                ollama_push_latest =  f"OLLAMA_HOST={ollama_host} 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!")
        
        result = subprocess.run(ollama_pid, shell=True, capture_output=True)
        print(result)
        if result.returncode != 0:
            raise Exception(f"Error retrieving Ollama PID {result.stderr}")
        print(f"Ollama PID Retrived: {ollama_pid}")
        
        result = subprocess.run(ollama_stop, shell=True, capture_output=True)
        print(result)
        if result.returncode != 0:
            raise Exception(f"Error stoppping Ollama {result.stderr}")
        print("Ollama stopped successfully!")
        
        result = subprocess.run(delete_home, shell=True, capture_output=True)
        print(result)
        if result.returncode != 0:
            raise Exception(f"Error removing Ollama HOME folder {result.stderr}")
        print("Ollama HOME fodler removed 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,
    )

    generate_pubkey = gr.Button (
        value="Generate Pubkey",
        min_width=250,
    )
    
    model_id = HuggingfaceHubSearch(
        label="Hugging Face Hub Model ID",
        placeholder="Search for model id on Huggingface",
        search_type="model",
    )
    
    download_gguf_link = gr.Textbox(
        label="Download Link",
        info="If you have access to the GGUF, you can insert the downlaod link here.",
    )
    
    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"
    )

    pubkey = gr.Code (
        ollama_pubkey,
        label="Copy this and paste this into your Ollama profile.",
    )
    
    ollama_model_name = gr.Textbox(
        label="Ollama Model Name",
        info="Input a Custom Model Name.",
    )

    ollama_library_username = gr.Textbox(
        label="Ollama Library Username",
        info="Input your username from Ollama to Push this model to their Library.",
    )
    
    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."
    )
            
    generate_pubkey.click(
        fn=regenerate_pubkey,
        inputs=[
            generate_pubkey
        ],
        outputs=[
        pubkey,
        ],
    )
    
    iface = gr.Interface(
        fn=ollamafy_model,
        # additional_inputs=[
        #     generate_pubkey,
        # ],
        inputs=[
            login,
            generate_pubkey,
            model_id,
            ollama_model_name,
            download_gguf_link,
            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():
    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)