import os
import pandas as pd
import requests
import huggingface_hub
import gradio as gr

data = pd.read_csv("data.csv")
webhook_url = os.environ.get("WEBHOOK_URL")

def filter_table(name, type, arch, license):
    tmp = data
    
    if name:
        tmp = tmp[tmp["Name"].str.contains(name)]
    if type:
        tmp = tmp[tmp["Type"].isin(type)]
    if arch:
        tmp = tmp[tmp["Architecture"].isin(arch)]
    if license:
        tmp = tmp[tmp["License"].isin(license)]

    tmp["Name"] = tmp["Name"].apply(lambda x: f'<a target="_blank" href="https://huggingface.co/{x}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{x}</a>')
    return tmp

def submit_model(name):
    try:
        huggingface_hub.hf_hub_download(repo_id=name, filename="config.json") # sanity check input
    except huggingface_hub.utils._errors.EntryNotFoundError:
        return "# ERROR: Model does not have a config.json file!"
    except huggingface_hub.utils._errors.RepositoryNotFoundError:
        return "# ERROR: Model could not be found on the Hugging Face Hub!"
    except requests.exceptions.HTTPError:
        return "# ERROR: Network error while validating model. Please try again later."
    except Exception as e:
        print(e)
        return "ERROR: Unexpected error. Please try again later."
    
    try:
        result = requests.post(webhook_url, json={"content":name})
    except requests.exceptions.HTTPError:
        return "# ERROR: Network error while contacting queue. Please try again in a few minutes."
    except Exception as e:
        print(e)
        return "ERROR: Unexpected error. Please try again later."
    
    return "# SUCCESS: Please wait up to 24 hours for your model to be added to the queue."

with gr.Blocks() as demo:
    gr.HTML('<style>.tab-buttons button{font-size:1.3em}</style><h1 style="text-align:center">Subquadratic LLM Leaderboard</h1>')

    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        with gr.Tab("🏅 LLM Benchmark"):
            with gr.Row():
                namefilter = model_name = gr.Textbox(max_lines=1, label="Search by model name and hit Enter")
                typefilter = gr.CheckboxGroup(label="Filter by model type", choices=list(data["Type"].unique()), value=[n for n in data["Type"].unique() if n not in ["Pending"]])
                archfilter = gr.CheckboxGroup(label="Filter by model architecture", choices=list(data["Architecture"].unique()), value=list(data["Architecture"].unique()))
                lcnsfilter = gr.CheckboxGroup(label="Filter by model license", choices=list(data["License"].unique()), value=list(data["License"].unique()))
                
            table = gr.Dataframe(datatype="markdown")

            namefilter.submit(filter_table, [namefilter,typefilter,archfilter,lcnsfilter], table)
            
            for filter in [typefilter, archfilter, lcnsfilter]:
                filter.input(filter_table, [namefilter,typefilter,archfilter,lcnsfilter], table)

            demo.load(fn=filter_table, inputs=[namefilter,typefilter,archfilter,lcnsfilter], outputs=table)
        
        with gr.Tab("📝 About"):
            gr.Markdown("""
                The **Subquadratic LLM Leaderboard** evaluates LLMs with subquadratic architectures (ie RWKV & Mamba) with the goal of providing open evaluation results while the architectures themselves are pending inclusion in 🤗 Transformers.  
                The metrics are the same as the Open LLM Leaderboard: ARC 25-shot, HellaSwag 10-shot, MMLU 5-shot, TruthfulQA zeroshot, Winogrande 5-shot, and GSM8K 5-shot.  
                This leaderboard is maintained by Devin Gulliver and is still under construction, check back regularly for further improvements!
                """)
        
        with gr.Tab("🚀 Submit here!"):
            with gr.Group():
                with gr.Row():
                    model_name = gr.Textbox(max_lines=1, label="Model Name", scale=4)
                    submit = gr.Button("Submit", variant="primary", scale=0)
            
            output = gr.Markdown("Enter a public HF repo id, then hit Submit to add it to the evaluation queue.")
            
            submit.click(fn=submit_model, inputs=model_name, outputs=output)

demo.launch()