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

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(assignment):
    api = HfApi()
    models = api.list_models(author="Classroom-workshop", search=assignment)
    model_ids = [x.modelId for x in models]
    print(model_ids)
    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:
        parsed =  accuracy.split(' +/- ')
        if len(parsed)>1:
            mean_reward = float(parsed[0])
            std_reward =  float(parsed[1])
        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



class Leaderboard:
    def __init__(self) -> None:
        self.leaderboard= {} 

    def add_leaderboard(self,id=None, title=None):
        if id is not None and title is not None:
            id = id.strip()
            title = title.strip()
            self.leaderboard.update({id:{'title':title,'data':get_data_per_env(id)}})
    def get_data(self):
        return self.leaderboard

    def get_ids(self):
        return list(self.leaderboard.keys())

          

# CSS file for the
with open('app.css','r') as f:
    BLOCK_CSS = f.read() 


LOADED_MODEL_IDS = {}

def get_data(rl_env):
    global LOADED_MODEL_IDS 
    data = []
    model_ids = get_model_ids(rl_env)
    LOADED_MODEL_IDS[rl_env]=model_ids

    for model_id in tqdm(model_ids):
        meta = get_metadata(model_id)
        if meta is None:
            continue
        user_id = model_id.split('/')[0]
        row = {}
        row["User"] = user_id
        row["Model"] = model_id
        metric = parse_metrics_accuracy(meta)
        row["Result"] = metric
        data.append(row)
    return pd.DataFrame.from_records(data)

def get_data_per_env(assignment):
    dataframe = get_data(assignment)
    dataframe = dataframe.fillna("")

    if not dataframe.empty:
        # turn the model ids into clickable links
        dataframe["User"] = dataframe["User"].apply(make_clickable_user)
        dataframe["Model"] = dataframe["Model"].apply(make_clickable_model)
        print(dataframe)
        dataframe = dataframe.sort_values(by=['Result'], ascending=False)
        if not 'Ranking' in dataframe.columns:
            dataframe.insert(0, 'Ranking', [i for i in range(1,len(dataframe)+1)])
        else:
           dataframe['Ranking'] =   [i for i in range(1,len(dataframe)+1)]   
        table_html = dataframe.to_html(escape=False, index=False,justify = 'left')
        return table_html,dataframe,dataframe.empty
    else: 
        html = """<div style="color: green">
                <p> ⌛ Please wait. Results will be out soon... </p>
                </div>
               """
        return html,dataframe,dataframe.empty   



leaderboard = Leaderboard()
leaderboard.add_leaderboard('assignment1'," Automatic Speech Recognition")
leaderboard.add_leaderboard('assignment2',"RL Agent for Moon landing")

ASSIGNMENTS = leaderboard.get_ids()
DETAILS = leaderboard.get_data()




def update_data(rl_env):
    global LOADED_MODEL_IDS
    data = []
    model_ids = [x for x in get_model_ids(rl_env) if x not in LOADED_MODEL_IDS[rl_env]]
    LOADED_MODEL_IDS[rl_env]+=model_ids

    for model_id in tqdm(model_ids):
        meta = get_metadata(model_id)
        if meta is None:
            continue
        user_id = model_id.split('/')[0]
        row = {}
        row["User"] = user_id
        row["Model"] = model_id
        accuracy = parse_metrics_accuracy(meta)
        row["Accuracy"] = accuracy
        data.append(row)
    return pd.DataFrame.from_records(data)


def update_data_per_env(rl_env):
    global DETAILS

    _,old_dataframe,_ = DETAILS[rl_env]['data']
    new_dataframe = update_data(rl_env)

    new_dataframe = new_dataframe.fillna("")
    if not new_dataframe.empty:
        new_dataframe["User"] = new_dataframe["User"].apply(make_clickable_user)
        new_dataframe["Model"] = new_dataframe["Model"].apply(make_clickable_model)

    dataframe = pd.concat([old_dataframe,new_dataframe])

    if not dataframe.empty:
       
        dataframe = dataframe.sort_values(by=['Results'], ascending=False)
        if not 'Ranking' in dataframe.columns:
            dataframe.insert(0, 'Ranking', [i for i in range(1,len(dataframe)+1)])
        else:
           dataframe['Ranking'] =   [i for i in range(1,len(dataframe)+1)]   
        table_html = dataframe.to_html(escape=False, index=False,justify = 'left')
        return table_html,dataframe,dataframe.empty
    else: 
        html = """<div style="color: green">
                <p> ⌛ Please wait. Results will be out soon... </p>
                </div>
               """
        return html,dataframe,dataframe.empty   






def get_info_display(len_dataframe,env_name,name_leaderboard,is_empty):
    if not is_empty:
        markdown = """
        <div class='infoPoint'>
        <h1> {name_leaderboard} </h1>
        <br>
        <p> This is a leaderboard of <b>{len_dataframe}</b> assignments for assignment {env_name} 👩‍🚀. </p>
        <br>
        </div>
        """.format(len_dataframe = len_dataframe,env_name = env_name,name_leaderboard = name_leaderboard)

    else:
        markdown = """
        <div class='infoPoint'>
        <h1> {name_leaderboard} </h1>
        <br>
        </div>                  
        """.format(name_leaderboard =  name_leaderboard)
    return markdown    

def reload_all_data():

    global DETAILS, ASSIGNMENTS

    for assignment in ASSIGNMENTS:
        DETAILS[assignment]['data'] = update_data_per_env(assignment)

    html = """<div style="color: green">
                <p> ✅ Leaderboard updated! Click `Reload Leaderboard` to see the current leaderboard.</p>
                </div>
               """    
    return html            


def reload_leaderboard(rl_env):
    global DETAILS
 
    data_html,data_dataframe,is_empty = DETAILS[rl_env]['data'] 

    markdown = get_info_display(len(data_dataframe),rl_env, DETAILS[rl_env]['title'],is_empty)            
    
    return markdown,data_html 
    
block = gr.Blocks(css=BLOCK_CSS)
with block:
    notification = gr.HTML("""<div style="color: green">
                <p> ⌛ Updating leaderboard... </p>
                </div>
               """)
    block.load(reload_all_data,[],[notification])
    
    with gr.Tabs():
        for rl_env in ASSIGNMENTS:
            with gr.TabItem(rl_env) as rl_tab:
                data_html,data_dataframe,is_empty = DETAILS[rl_env]['data'] 
                markdown = get_info_display(len(data_dataframe),rl_env,DETAILS[rl_env]['title'],is_empty)            
                env_state =gr.Variable(default_value=rl_env)  
                output_markdown = gr.HTML(markdown)
                reload = gr.Button('Reload Leaderboard')

                output_html = gr.HTML(data_html)

                reload.click(reload_leaderboard,inputs=[env_state],outputs=[output_markdown,output_html])
                rl_tab.select(reload_leaderboard,inputs=[env_state],outputs=[output_markdown,output_html])

block.launch()