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import gradio
import argparse
import os
import boto3
from datetime import datetime
from utils import generate, send_to_s3
from models import get_tiny_llama, response_tiny_llama
from constants import css, js_code, js_light
MERA_table = None
TINY_LLAMA = None
S3_SESSION = None
def giga_gen(content):
res = generate(content,'auth_token.json')
send_to_s3(res, f'giga_{str(datetime.now()).replace(" ", "_")}.json', S3_SESSION)
return res
def tiny_gen(content):
res = response_tiny_llama(TINY_LLAMA, content)
send_to_s3(res, f'tiny_{str(datetime.now()).replace(" ", "_")}.json', S3_SESSION)
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"]
TINY_LLAMA = get_tiny_llama()
try:
session = boto3.session.Session()
S3_SESSION = session.client(
service_name='s3',
endpoint_url=os.getenv('S3_ENDPOINT'),
aws_access_key_id=os.getenv('S3_ENDPOINT'),
aws_secret_access_key=os.getenv('S3_ENDPOINT'),
)
except:
print('Failed to start s3 session')
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()
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