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| 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() | |