import pandas as pd
import requests
from tqdm.auto import tqdm
from huggingface_hub import HfApi, hf_hub_download
from huggingface_hub.repocard import metadata_load


# Based on Omar Sanseviero work
# Make model clickable link
def make_clickable_model(model_name):
    # remove user from model name
    model_name_show = ' '.join(model_name.split('/')[1:])

    link = "https://huggingface.co/" + model_name
    return f'<a target="_blank" href="{link}">{model_name_show}</a>'

# Make user clickable link
def make_clickable_user(user_id):
    link = "https://huggingface.co/" + user_id
    return f'<a  target="_blank" href="{link}">{user_id}</a>'
    


def get_model_ids(rl_env):
    api = HfApi()
    models = api.list_models(filter=rl_env)
    model_ids = [x.modelId for x in models]
    return model_ids
    
def get_metadata(model_id):
    try:
        readme_path = hf_hub_download(model_id, filename="README.md")
        return metadata_load(readme_path)
    except requests.exceptions.HTTPError:
        # 404 README.md not found
        return None
        
def parse_metrics_accuracy(meta):
    if "model-index" not in meta:
        return None
    result = meta["model-index"][0]["results"]
    metrics = result[0]["metrics"]
    accuracy = metrics[0]["value"]
    return accuracy

# We keep the worst case episode
def parse_rewards(accuracy):
    default_std = -1000
    default_reward=-1000
    if accuracy !=  None:
        accuracy = str(accuracy)
        parsed =  accuracy.split(' +/- ')
        if len(parsed)>1:
            mean_reward = float(parsed[0])
            std_reward =  float(parsed[1])
        elif len(parsed)==1: #only mean reward   
            mean_reward = float(parsed[0])
            std_reward =  float(0)
             
        else: 
            mean_reward = float(default_std)
            std_reward = float(default_reward)

    else:
        mean_reward = float(default_std)
        std_reward = float(default_reward)
    return mean_reward, std_reward