import gradio
import argparse
import os

from utils import generate
from models import get_tiny_llama, response_tiny_llama
from constants import css, js_code, js_light

MERA_table = None
TINY_LLAMA = get_tiny_llama()

def giga_gen(content):
    res = generate(content,'auth_token.json')
    return res

def tiny_gen(content):
    res = response_tiny_llama(TINY_LLAMA, content)
    return res

def tab_arena():
    with gradio.Row():
        with gradio.Column():
            gradio.Interface(fn=giga_gen, inputs="text", outputs="text", allow_flagging=False, title='Giga') # arena = 
        with gradio.Column():
            gradio.Interface(fn=tiny_gen, inputs="text", outputs="text", allow_flagging=False, title='TinyLlama') # arena = 
        
    # arena.launch()

with open("test.md", "r") as f:
    TEST_MD = f.read()

available_models = ["GigaChat", ""] # list(model_info.keys()) 

def build_demo():
    # global original_dfs, available_models, gpt4t_dfs, haiku_dfs, llama_dfs

    with gradio.Blocks(theme=gradio.themes.Base(), css=css, js=js_light) as demo:
        # gradio.HTML(BANNER, elem_id="banner")
        # gradio.Markdown(HEADER_MD.replace("{model_num}", str(len(original_dfs["-1"]))), elem_classes="markdown-text")
        
        with gradio.Tabs(elem_classes="tab-buttons") as tabs:
            with gradio.TabItem("🐼 MERA leaderboard", elem_id="od-benchmark-tab-table", id=0):
                gradio.Markdown(TEST_MD, elem_classes="markdown-text-details")
                # _tab_leaderboard()

            with gradio.TabItem("🆚 SBS by categories and criteria", elem_id="od-benchmark-tab-table", id=1):
                gradio.Markdown(TEST_MD, elem_classes="markdown-text-details")

            with gradio.TabItem("🥊 Model arena", elem_id="od-benchmark-tab-table", id=2):
                tab_arena()
                # _tab_explore()

            with gradio.TabItem("💪 About MERA", elem_id="od-benchmark-tab-table", id=3):
                gradio.Markdown(TEST_MD, elem_classes="markdown-text")
        # gr.Markdown(f"Last updated on **{LAST_UPDATED}** | [Link to V1-legacy](https://huggingface.co/spaces/allenai/WildBench-V1-legacy)", elem_classes="markdown-text-small")
        
        # with gr.Row():
        #     with gr.Accordion("📙 Citation", open=False, elem_classes="accordion-label"):
        #         gr.Textbox(
        #             value=CITATION_TEXT, 
        #             lines=7,
        #             label="Copy the BibTeX snippet to cite this source",
        #             elem_id="citation-button",
        #             show_copy_button=True)
                # ).style(show_copy_button=True)

    return demo

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--share", action="store_true")
    # parser.add_argument("--bench_table", help="Path to MERA table", default="data_dir/MERA_jun2024.jsonl")
    args = parser.parse_args()
    # data_load(args.result_file)    
    # TYPES = ["number", "markdown", "number"]

    demo = build_demo()
    demo.launch(share=args.share, height=3000, width="110%") # share=args.share

    # demo = gradio.Interface(fn=gen, inputs="text", outputs="text")
    # demo.launch()