feat: Add Gradio and Pandas dependencies, and create a leaderboard for PyTorch Image Models

app.py

@@ -1,56 +1,63 @@
 import gradio as gr
 import pandas as pd
 
-imagenet_df = pd.read_csv('https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-imagenet.csv')
-imagenet_real_df = pd.read_csv('https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-imagenet-real.csv')
-imagenetv2_df = pd.read_csv('https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-imagenetv2-matched-frequency.csv')
-sketch_df = pd.read_csv('https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-sketch.csv')
-imagenet_a_df = pd.read_csv('https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-imagenet-a.csv')
-imagenet_r_df = pd.read_csv('https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-imagenet-r.csv')
-
-# columns to remove from each dataframe
-remove_column_names = ["top1_err", "top5_err", "top1_diff", "top5_diff", "rank_diff"]
-for remove_column_name in remove_column_names:
-    if remove_column_name in imagenet_df.columns:
-        imagenet_df = imagenet_df.drop(columns=remove_column_name)
-    if remove_column_name in imagenet_real_df.columns:
-        imagenet_real_df = imagenet_real_df.drop(columns=remove_column_name)
-    if remove_column_name in imagenetv2_df.columns:
-        imagenetv2_df = imagenetv2_df.drop(columns=remove_column_name)
-    if remove_column_name in sketch_df.columns:
-        sketch_df = sketch_df.drop(columns=remove_column_name)
-    if remove_column_name in imagenet_a_df.columns:
-        imagenet_a_df = imagenet_a_df.drop(columns=remove_column_name)
-    if remove_column_name in imagenet_r_df.columns:
-        imagenet_r_df = imagenet_r_df.drop(columns=remove_column_name)
-
-# Rename top1 and top5 columns to the name of the dataframe+top1/top5
-imagenet_df = imagenet_df.rename(columns={"top1": "imagenet_top1", "top5": "imagenet_top5"})
-imagenet_real_df = imagenet_real_df.rename(columns={"top1": "imagenet_real_top1", "top5": "imagenet_real_top5"})
-imagenetv2_df = imagenetv2_df.rename(columns={"top1": "imagenetv2_top1", "top5": "imagenetv2_top5"})
-sketch_df = sketch_df.rename(columns={"top1": "sketch_top1", "top5": "sketch_top5"})
-imagenet_a_df = imagenet_a_df.rename(columns={"top1": "imagenet_a_top1", "top5": "imagenet_a_top5"})
-imagenet_r_df = imagenet_r_df.rename(columns={"top1": "imagenet_r_top1", "top5": "imagenet_r_top5"})
-
-# Merge all dataframes
-result = pd.merge(imagenet_df, imagenet_real_df, on=['model', 'param_count', 'img_size', 'crop_pct', 'interpolation'], how='outer')
-result = pd.merge(result, imagenetv2_df, on=['model', 'param_count', 'img_size', 'crop_pct', 'interpolation'], how='outer')
-result = pd.merge(result, sketch_df, on=['model', 'param_count', 'img_size', 'crop_pct', 'interpolation'], how='outer')
-result = pd.merge(result, imagenet_a_df, on=['model', 'param_count', 'img_size', 'crop_pct', 'interpolation'], how='outer')
-result = pd.merge(result, imagenet_r_df, on=['model', 'param_count', 'img_size', 'crop_pct', 'interpolation'], how='outer')
-
-# Average top1 and top5 and add the average column after `model` column
-result['average_top1'] = result[['imagenet_top1', 'imagenet_real_top1', 'imagenetv2_top1', 'sketch_top1', 'imagenet_a_top1', 'imagenet_r_top1']].mean(axis=1)
-result['average_top5'] = result[['imagenet_top5', 'imagenet_real_top5', 'imagenetv2_top5', 'sketch_top5', 'imagenet_a_top5', 'imagenet_r_top5']].mean(axis=1)
-result = result[['model', 'average_top1', 'average_top5', 'param_count', 'img_size', 'crop_pct', 'interpolation', 'imagenet_top1', 'imagenet_top5', 'imagenet_real_top1', 'imagenet_real_top5', 'imagenetv2_top1', 'imagenetv2_top5', 'sketch_top1', 'sketch_top5', 'imagenet_a_top1', 'imagenet_a_top5', 'imagenet_r_top1', 'imagenet_r_top5']]
-result = result.sort_values(by='average_top1', ascending=False)
-
-# Round the values to 3 decimal places
-result = result.round(3)
-
-with gr.Blocks("Timm Leaderboard") as leaderboard:
+def load_leaderboard():
+    imagenet_df = pd.read_csv('https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-imagenet.csv')
+    imagenet_real_df = pd.read_csv('https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-imagenet-real.csv')
+    imagenetv2_df = pd.read_csv('https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-imagenetv2-matched-frequency.csv')
+    sketch_df = pd.read_csv('https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-sketch.csv')
+    imagenet_a_df = pd.read_csv('https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-imagenet-a.csv')
+    imagenet_r_df = pd.read_csv('https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-imagenet-r.csv')
+
+    # columns to remove from each dataframe
+    remove_column_names = ["top1_err", "top5_err", "top1_diff", "top5_diff", "rank_diff"]
+    for remove_column_name in remove_column_names:
+        if remove_column_name in imagenet_df.columns:
+            imagenet_df = imagenet_df.drop(columns=remove_column_name)
+        if remove_column_name in imagenet_real_df.columns:
+            imagenet_real_df = imagenet_real_df.drop(columns=remove_column_name)
+        if remove_column_name in imagenetv2_df.columns:
+            imagenetv2_df = imagenetv2_df.drop(columns=remove_column_name)
+        if remove_column_name in sketch_df.columns:
+            sketch_df = sketch_df.drop(columns=remove_column_name)
+        if remove_column_name in imagenet_a_df.columns:
+            imagenet_a_df = imagenet_a_df.drop(columns=remove_column_name)
+        if remove_column_name in imagenet_r_df.columns:
+            imagenet_r_df = imagenet_r_df.drop(columns=remove_column_name)
+
+    # Rename top1 and top5 columns to the name of the dataframe+top1/top5
+    imagenet_df = imagenet_df.rename(columns={"top1": "imagenet_top1", "top5": "imagenet_top5"})
+    imagenet_real_df = imagenet_real_df.rename(columns={"top1": "imagenet_real_top1", "top5": "imagenet_real_top5"})
+    imagenetv2_df = imagenetv2_df.rename(columns={"top1": "imagenetv2_top1", "top5": "imagenetv2_top5"})
+    sketch_df = sketch_df.rename(columns={"top1": "sketch_top1", "top5": "sketch_top5"})
+    imagenet_a_df = imagenet_a_df.rename(columns={"top1": "imagenet_a_top1", "top5": "imagenet_a_top5"})
+    imagenet_r_df = imagenet_r_df.rename(columns={"top1": "imagenet_r_top1", "top5": "imagenet_r_top5"})
+
+    # Merge all dataframes
+    result = pd.merge(imagenet_df, imagenet_real_df, on=['model', 'param_count', 'img_size', 'crop_pct', 'interpolation'], how='outer')
+    result = pd.merge(result, imagenetv2_df, on=['model', 'param_count', 'img_size', 'crop_pct', 'interpolation'], how='outer')
+    result = pd.merge(result, sketch_df, on=['model', 'param_count', 'img_size', 'crop_pct', 'interpolation'], how='outer')
+    result = pd.merge(result, imagenet_a_df, on=['model', 'param_count', 'img_size', 'crop_pct', 'interpolation'], how='outer')
+    result = pd.merge(result, imagenet_r_df, on=['model', 'param_count', 'img_size', 'crop_pct', 'interpolation'], how='outer')
+
+    # Average top1 and top5 and add the average column after `model` column
+    result['average_top1'] = result[['imagenet_top1', 'imagenet_real_top1', 'imagenetv2_top1', 'sketch_top1', 'imagenet_a_top1', 'imagenet_r_top1']].mean(axis=1)
+    result['average_top5'] = result[['imagenet_top5', 'imagenet_real_top5', 'imagenetv2_top5', 'sketch_top5', 'imagenet_a_top5', 'imagenet_r_top5']].mean(axis=1)
+    result = result[['model', 'average_top1', 'average_top5', 'param_count', 'img_size', 'crop_pct', 'interpolation', 'imagenet_top1', 'imagenet_top5', 'imagenet_real_top1', 'imagenet_real_top5', 'imagenetv2_top1', 'imagenetv2_top5', 'sketch_top1', 'sketch_top5', 'imagenet_a_top1', 'imagenet_a_top5', 'imagenet_r_top1', 'imagenet_r_top5']]
+    result = result.sort_values(by='average_top1', ascending=False)
+
+    # Round the values to 3 decimal places
+    result = result.round(3)
+
+    return result
+
+df = load_leaderboard()
+
+with gr.Blocks("Timm Leaderboard") as app:
     gr.HTML("<center><h1>PyTorch Image Models Leaderboard</h1></center>")
     gr.Markdown("This leaderboard is based on the results of the models from the [PyTorch Image Models]('https://github.com/huggingface/pytorch-image-models') repository.")
-    gr.DataFrame(result)
+    leaderboard = gr.DataFrame(df)
+    refresh_button = gr.Button(value="Refresh Leaderboard", variant="primary")
+    refresh_button.click(load_leaderboard, outputs=[leaderboard])
 
-leaderboard.launch()
+app.launch()