Create app.py

app.py

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+import gradio as gr
+from gradio_leaderboard import Leaderboard
+import plotly.express as px
+from pathlib import Path
+import pandas as pd
+import numpy as np
+abs_path = Path(__file__).parent
+
+def parse_model_args(model_args):
+    if "deltazip" in model_args:
+        model_args = model_args.split("deltazip")[1]
+        model_args = model_args.split(",")[0]
+        model_args = model_args.strip(".")
+        model_args = model_args.replace(".", "/")
+    if "espressor/" in model_args:
+        model_args = model_args.split("espressor/")[1]
+        model_args = model_args.split(",")[0]
+        model_args = model_args.strip(".")
+        model_args = model_args.replace(".", "/",1)
+        model_args = model_args.split("_")[0]
+    else:
+        model_args = model_args.split(",")[0]
+        model_args = model_args.replace("pretrained=", "")
+    return model_args
+
+def parse_model_precision(model_args):
+    if "espressor" in model_args:
+        if 'W8A8_int8' in model_args:
+            precision = 'W8A8_int8'
+        else:
+            precision = model_args.split("_")[-1]
+    else:
+        precision = "Default"
+    return precision
+
+# Any pandas-compatible data
+df = pd.read_csv(str(abs_path / "eval_results.csv"))
+# take acc only
+df = df[df['metric'] == 'acc']
+# dedup
+df = df.drop_duplicates(subset=['model', 'task'])
+# pivot df, such that the column names are model,task,efficiency
+# but keep precision in its original place
+df = df.pivot(index='model', columns='task', values='value').reset_index()
+
+df['precision'] = df['model'].apply(lambda x: x.split(":")[-1])
+df['model'] = df['model'].apply(lambda x: x.split(":")[0])
+
+# average over all columns starting with 'task_'
+df['avg_acc'] = df.filter(like='task_').mean(axis=1)
+# keep 2 decimal points for avg_acc, and all tasks_
+# rename columns starting with 'task_' by removing 'task_'
+df = df.rename(columns=lambda x: x.replace('task_', ''))
+numeric_columns = df.select_dtypes(include=[np.number]).columns
+df[numeric_columns] = (df[numeric_columns]*100).round(2)
+
+with gr.Blocks() as demo:
+    gr.Markdown("""
+    # 🥇 Efficient LLM Leaderboard
+    """)
+    task_options = [col for col in df.columns if col not in ['model', 'precision']]
+
+    with gr.Row():
+        selected_tasks = gr.CheckboxGroup(choices=task_options, label="Select Tasks")
+    with gr.Row():
+        accuracy_plot = gr.Plot(label="Accuracy Plot")
+        data_table = gr.Dataframe(value=df, label="Result Table")
+
+    def update_outputs(selected_tasks):
+        if not selected_tasks:
+            return df[['model', 'precision']], None
+        filtered_df = df[['model', 'precision'] + selected_tasks]
+        melted_df = filtered_df.melt(id_vars=['model', 'precision'], var_name='task', value_name='accuracy')
+        fig = px.bar(melted_df, x='model', y='accuracy', color='precision', barmode='group', facet_col='task')
+        return filtered_df, fig
+
+    selected_tasks.change(fn=update_outputs, inputs=selected_tasks, outputs=[data_table, accuracy_plot])
+
+
+if __name__ == "__main__":
+    demo.launch()