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import gradio as gr
import pandas as pd
def load_and_process_data(file_path):
# Load the leaderboard data
df = pd.read_pickle(file_path)
# Group by 'lmm' and 'question' to calculate mean accuracy
accuracy_df = (
df.groupby(["lmm", "question"])["accepted_by_judge"].mean().reset_index()
)
accuracy_df = accuracy_df.rename(columns={"accepted_by_judge": "accuracy"})
accuracy_df["accuracy"] = (accuracy_df["accuracy"] * 100).round(1)
# Group by 'lmm' to calculate the count of images
image_count_df = df.groupby("lmm")["image"].nunique().reset_index()
image_count_df = image_count_df.rename(columns={"image": "Total Images"})
return accuracy_df, image_count_df
def expand_and_format_df(accuracy_df, image_count_df):
# Pivot and format the accuracy dataframe
expanded_df = accuracy_df.pivot(index="lmm", columns="question", values="accuracy")
expanded_df["Average"] = expanded_df.mean(axis=1).round(1)
expanded_df = expanded_df.sort_values(by="Average", ascending=False).reset_index()
expanded_df.columns.name = None
# Merge the 'total_images' column
final_df = pd.merge(expanded_df, image_count_df, on="lmm")
return final_df.rename(columns={"lmm": "Model"})
def map_model_names(df, name_dict):
# Map model names using the provided dictionary
df["Model"] = df["Model"].map(name_dict)
return df
# Dictionary for renaming models
name_dict = {
"gpt4v": "GPT-4V(ision)",
"llava": "LLaVA-1.5-13B",
"llava-7b": "LLaVA-1.5-7B",
"Long-SPHINX": "Long-SPHINX",
"SPHINX": "SPHINX",
"OtterHD": "OtterHD",
"minigpt4v2": "MiniGPT4v2",
"InstructBLIP-13B": "InstructBLIP-13B",
"InstructBLIP": "InstructBLIP-7B",
"qwen": "Qwen-VL-Chat",
"fuyu-8b": "Fuyu-8B",
}
# Processing steps
accuracy_df, image_count_df = load_and_process_data("raw_outputs.pkl")
final_df = expand_and_format_df(accuracy_df, image_count_df)
final_df = map_model_names(final_df, name_dict)
# Gradio interface
with gr.Blocks() as demo:
gr.Markdown("# GlitchBench Leaderboard")
with gr.Row():
gr.Dataframe(final_df)
demo.launch()
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