import gradio as gr
import subprocess
import os
from huggingface_hub import list_models, model_info, run_job, inspect_job, whoami
from lighteval.tasks.registry import Registry
from lighteval.utils.utils import as_list

def get_models():
    """Get list of popular text generation models from Hugging Face"""
    try:
        models = list_models(
            filter="text-generation",
            sort="likes",
            direction=-1,
            limit=1000,
            expand="inferenceProviderMapping"
        )

        model_ids = [
            model.id 
            for model in models 
            if hasattr(model, 'inference_provider_mapping') and model.inference_provider_mapping
        ]
        return model_ids
    except Exception as e:
        print(f"Error fetching models: {e}")
        return []

def get_providers_for_model(model_id):
    """Get inference providers for a specific model"""
    if not model_id:
        return gr.update(choices=["NO PROVIDER"], value=None)

    try:
        info = model_info(model_id, expand="inferenceProviderMapping")
        if hasattr(info, 'inference_provider_mapping') and info.inference_provider_mapping:
            providers = [p.provider for p in info.inference_provider_mapping]
            return gr.update(choices=providers, value=providers[0] if providers else None)
        else:
            return gr.update(choices=[], value=None)
    except Exception as e:
        print(f"Error fetching providers for {model_id}: {e}")
        return gr.update(choices=["NO PROVIDER"], value=None)

# Cache tasks at module level to avoid reloading
_TASKS_CACHE = None

def get_available_tasks():
    """Get list of available lighteval tasks"""
    global _TASKS_CACHE
    if _TASKS_CACHE is not None:
        return _TASKS_CACHE

    try:
        print("Loading lighteval tasks...")
        registry = Registry()
        # We only want generative metrics as the others won't run with inference providers
        tasks_list = [
            f"{config.suite[0]}|{name}" 
            for name, config in registry.load_tasks().items() 
            if all(metric.category.value == "GENERATIVE" for metric in config.metrics)
        ]
        tasks = sorted(tasks_list)
        _TASKS_CACHE = tasks
        print(f"Loaded {len(tasks)} tasks")
        return tasks
    except Exception as e:
        print(f"Error fetching tasks: {e}")
        return []

def build_lighteval_command(
    model = "MODEL", 
    provider = "PROVIDER", 
    tasks = "TASKS", 
    results_org = "YOUR_ORG", 
    username="YOUR_USERNAME"
):
    """Build run_job command from selections"""
    tasks = as_list(tasks)
    model_provider_arg = f'model_name={model},provider={provider}'
    task_args = ','.join([f'{task}' for task in tasks])

    # Build the Python code string
    command = f'''from huggingface_hub import run_job, inspect_job
import os

# The token must be able to call inference providers and write to the org you selected on your behalf
# Test the command with `"--max-samples", "10",` first, and remove to run a full evaluation job

job = run_job(
    image="hf.co/spaces/OpenEvals/EvalsOnTheHub",
    command=[
        "lighteval", "endpoint", "inference-providers",
        "{model_provider_arg}",
        "{task_args}",
        "--push-to-hub", "--save-details",
        "--results-org", "{results_org}"
    ],
    namespace="{username}",
    secrets={{"HF_TOKEN": os.getenv("HF_TOKEN")}},
    token=os.getenv("HF_TOKEN")
)'''
    return command