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# Add this at the top of your script
import warnings
warnings.filterwarnings("ignore")
import gradio as gr
import promptquality as pq
from dotenv import load_dotenv
load_dotenv()
from data_loader import (
load_data,
CATEGORIES,
METHODOLOGY,
HEADER_CONTENT,
CARDS,
DATASETS,
SCORES,
)
from tabs.leaderboard import create_leaderboard_tab, filter_leaderboard
from tabs.model_comparison import create_model_comparison_tab, compare_models
from tabs.data_exploration import create_exploration_tab, filter_and_display
def create_app():
df = load_data()
MODELS = [x.strip() for x in df["Model"].unique().tolist()]
with gr.Blocks(
theme=gr.themes.Soft(font=[gr.themes.GoogleFont("sans-serif")])
) as app:
with gr.Tabs():
# Create tabs
lb_output, lb_plot1, lb_plot2 = create_leaderboard_tab(
df, CATEGORIES, METHODOLOGY, HEADER_CONTENT, CARDS
)
mc_info, mc_plot = create_model_comparison_tab(df, HEADER_CONTENT)
exp_outputs = create_exploration_tab(df)
# Initial loads
app.load(
fn=lambda: filter_leaderboard(
df, "All", list(CATEGORIES.keys())[0], "Performance"
),
outputs=[lb_output, lb_plot1, lb_plot2],
)
app.load(
fn=lambda: compare_models(
df, [df.sort_values("Model Avg", ascending=False).iloc[0]["Model"]]
),
outputs=[mc_info, mc_plot],
)
app.load(
fn=lambda: filter_and_display(
MODELS[0],
DATASETS[0],
min(SCORES),
max(SCORES),
0,
0,
0,
),
outputs=exp_outputs[:-1],
)
return app
demo = create_app()
demo.launch()
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