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import gradio as gr
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import snapshot_download
import pandas as pd
import matplotlib.pyplot as plt
# Dataset paths
LEADERBOARD_PATH = "hf://datasets/alibayram/yapay_zeka_turkce_mmlu_liderlik_tablosu/data/train-00000-of-00001.parquet"
RESPONSES_PATH = "hf://datasets/alibayram/yapay_zeka_turkce_mmlu_model_cevaplari/data/train-00000-of-00001.parquet"
SECTION_RESULTS_PATH = "hf://datasets/alibayram/yapay_zeka_turkce_mmlu_bolum_sonuclari/data/train-00000-of-00001.parquet"
# Load datasets
try:
leaderboard_data = pd.read_parquet(LEADERBOARD_PATH)
model_responses_data = pd.read_parquet(RESPONSES_PATH)
section_results_data = pd.read_parquet(SECTION_RESULTS_PATH)
except Exception as e:
print(f"Error loading datasets: {e}")
raise
# Helper functions
def filter_leaderboard(family=None, quantization_level=None):
df = leaderboard_data.copy()
if family:
df = df[df["family"] == family]
if quantization_level:
df = df[df["quantization_level"] == quantization_level]
return df
def search_responses(query, model):
filtered = model_responses_data[model_responses_data["bolum"].str.contains(query, case=False)]
selected_columns = ["bolum", "soru", "cevap", model + "_cevap"]
return filtered[selected_columns]
def plot_section_results():
fig, ax = plt.subplots(figsize=(10, 6))
avg_scores = section_results_data.mean(numeric_only=True)
avg_scores.plot(kind="bar", ax=ax)
ax.set_title("Average Section-Wise Performance")
ax.set_ylabel("Accuracy (%)")
ax.set_xlabel("Sections")
return fig # Return the figure object
def add_new_model(model_name, base_model, revision, precision, weight_type, model_type):
# Simulated model submission logic
return f"Model '{model_name}' submitted successfully!"
# Gradio app structure
with gr.Blocks(css=".container { max-width: 1200px; margin: auto; }") as app:
gr.HTML("<h1>🏆 Turkish MMLU Leaderboard</h1>")
gr.Markdown("Explore, evaluate, and compare AI model performance.")
with gr.Tabs() as tabs:
# Leaderboard Tab
with gr.TabItem("Leaderboard"):
family_filter = gr.Dropdown(
choices=leaderboard_data["family"].unique().tolist(), label="Filter by Family", multiselect=False
)
quantization_filter = gr.Dropdown(
choices=leaderboard_data["quantization_level"].unique().tolist(), label="Filter by Quantization Level"
)
leaderboard_table = gr.DataFrame(leaderboard_data)
gr.Button("Apply Filters").click(
filter_leaderboard, inputs=[family_filter, quantization_filter], outputs=leaderboard_table
)
# Model Responses Tab
with gr.TabItem("Model Responses"):
model_dropdown = gr.Dropdown(
choices=leaderboard_data["model"].unique().tolist(), label="Select Model"
)
query_input = gr.Textbox(label="Search Query")
responses_table = gr.DataFrame()
gr.Button("Search").click(
search_responses, inputs=[query_input, model_dropdown], outputs=responses_table
)
# Section Results Tab
with gr.TabItem("Section Results"):
gr.Plot(plot_section_results)
gr.DataFrame(section_results_data)
# Submit Model Tab
with gr.TabItem("Submit Model"):
gr.Markdown("### Submit Your Model for Evaluation")
model_name = gr.Textbox(label="Model Name")
base_model = gr.Textbox(label="Base Model")
revision = gr.Textbox(label="Revision", placeholder="main")
precision = gr.Dropdown(
choices=["float16", "int8", "bfloat16", "float32"], label="Precision", value="float16"
)
weight_type = gr.Dropdown(
choices=["Original", "Delta", "Adapter"], label="Weight Type", value="Original"
)
model_type = gr.Dropdown(
choices=["Transformer", "RNN", "GPT", "Other"], label="Model Type", value="Transformer"
)
submit_button = gr.Button("Submit")
submission_output = gr.Markdown()
submit_button.click(
add_new_model,
inputs=[model_name, base_model, revision, precision, weight_type, model_type],
outputs=submission_output,
)
# Scheduler for refreshing datasets
scheduler = BackgroundScheduler()
scheduler.add_job(
lambda: snapshot_download(repo_id="alibayram", repo_type="dataset", local_dir="cache"),
"interval", seconds=1800
)
scheduler.start()
# Launch app
app.queue(default_concurrency_limit=40).launch() |