Update app.py

app.py

@@ -108,61 +108,10 @@ def get_arena_table(model_table_df):
         values.append(row)
     return values
 
-def get_recog_table(model_table_df):
-    # sort by rating
-    values = []
-    for i in range(len(model_table_df)):
-        row = []
-        model_key = model_table_df.index[i]
-        model_name = model_table_df["Model"].values[model_key]
-        # rank
-        row.append(i + 1)
-        # model display name
-        row.append(model_name)
-
-        row.append(
-            model_table_df["Language Model"].values[model_key]
-        )
-        row.append(
-            model_table_df["Open Source"].values[model_key]
-        )
-        row.append(
-            model_table_df["Regular Text"].values[model_key]
-        )
-
-        row.append(
-            model_table_df["Irregular Text"].values[model_key]
-        )
-
-        row.append(
-            model_table_df["Artistic Text"].values[model_key]
-        )
-
-        row.append(
-            model_table_df["Handwriting"].values[model_key]
-        )
-
-        row.append(
-            model_table_df["Digit string"].values[model_key]
-        )
-
-        row.append(
-            model_table_df["Non-semantic Text"].values[model_key]
-        )
-        row.append(
-            model_table_df["ALL"].values[model_key]
-        )
-        values.append(row)
-    return values
-
-def build_leaderboard_tab(leaderboard_table_file, text_recog_file, Inaccessible_model_file, show_plot=False):
+def build_leaderboard_tab(leaderboard_table_file, show_plot=False):
     if leaderboard_table_file:
         data = load_leaderboard_table_csv(leaderboard_table_file)
-        data_recog = load_leaderboard_table_csv(text_recog_file)
-        data_Inaccessible = load_leaderboard_table_csv(Inaccessible_model_file)
         model_table_df = pd.DataFrame(data)
-        model_table_df_Inaccessible = pd.DataFrame(data_Inaccessible)
-        recog_table_df = pd.DataFrame(data_recog)
         md_head = f"""
         # 🏆 OCRBench Leaderboard
         | [GitHub](https://github.com/Yuliang-Liu/MultimodalOCR) | [Paper](https://arxiv.org/abs/2305.07895) |
@@ -172,79 +121,7 @@ def build_leaderboard_tab(leaderboard_table_file, text_recog_file, Inaccessible_
             # arena table
             with gr.Tab("OCRBench", id=0):
                 arena_table_vals = get_arena_table(model_table_df)
-                md = "OCRBench is a comprehensive evaluation benchmark designed to assess the OCR capabilities of Large Multimodal Models. It comprises five components: Text Recognition, SceneText-Centric VQA, Document-Oriented VQA, Key Information Extraction, and Handwritten Mathematical Expression Recognition. The benchmark includes 1000 question-answer pairs, and all the answers undergo manual verification and correction to ensure a more precise evaluation."
-                gr.Markdown(md, elem_id="leaderboard_markdown")
-                gr.Dataframe(
-                    headers=[
-                        "Rank",
-                        "Name",
-                        "Language Model",
-                        "Open Source",
-                        "Text Recognition",
-                        "Scene Text-Centric VQA",
-                        "Doc-Oriented VQA",
-                        "KIE",
-                        "HMER",
-                        "Final Score",
-                    ],
-                    datatype=[
-                        "str",
-                        "markdown",
-                        "str",
-                        "str",
-                        "number",
-                        "number",
-                        "number",
-                        "number",
-                        "number",
-                        "number",
-                    ],
-                    value=arena_table_vals,
-                    elem_id="arena_leaderboard_dataframe",
-                    height=700,
-                    column_widths=[60, 120,150,100, 150, 200, 180, 80, 80, 160],
-                    wrap=True,
-                )
-            with gr.Tab("Text Recognition", id=1):
-                arena_table_vals = get_recog_table(recog_table_df)
-                md = "OCRBench is a comprehensive evaluation benchmark designed to assess the OCR capabilities of Large Multimodal Models. It comprises five components: Text Recognition, SceneText-Centric VQA, Document-Oriented VQA, Key Information Extraction, and Handwritten Mathematical Expression Recognition. The benchmark includes 1000 question-answer pairs, and all the answers undergo manual verification and correction to ensure a more precise evaluation."
-                gr.Markdown(md, elem_id="leaderboard_markdown")
-                gr.Dataframe(
-                    headers=[
-                        "Rank",
-                        "Name",
-                        "Language Model",
-                        "Open Source",
-                        "Regular Text",
-                        "Irregular Text",
-                        "Artistic Text",
-                        "Handwriting",
-                        "Digit string",
-                        "Non-semantic Text",
-                        "ALL",
-                    ],
-                    datatype=[
-                        "str",
-                        "markdown",
-                        "str",
-                        "str",
-                        "number",
-                        "number",
-                        "number",
-                        "number",
-                        "number",
-                        "number",
-                        "number",
-                    ],
-                    value=arena_table_vals,
-                    elem_id="arena_leaderboard_dataframe",
-                    height=700,
-                    column_widths=[60, 120,150,100, 100, 100, 100, 100, 100,100, 80],
-                    wrap=True,
-                )
-            with gr.Tab("Inaccessible Model", id=2):
-                arena_table_vals = get_arena_table(model_table_df_Inaccessible)
-                md = "The models on this list are neither open-source nor have API call interfaces available."
+                md = "OCRBench v2 is a comprehensive evaluation benchmark designed to assess the OCR capabilities of Large Multimodal Models. It comprises five components: Text Recognition, SceneText-Centric VQA, Document-Oriented VQA, Key Information Extraction, and Handwritten Mathematical Expression Recognition. The benchmark includes 1000 question-answer pairs, and all the answers undergo manual verification and correction to ensure a more precise evaluation."
                 gr.Markdown(md, elem_id="leaderboard_markdown")
                 gr.Dataframe(
                     headers=[
@@ -285,7 +162,7 @@ def build_leaderboard_tab(leaderboard_table_file, text_recog_file, Inaccessible_
     If you would like to include your model in the OCRBench leaderboard, please follow the evaluation instructions provided on [GitHub](https://github.com/Yuliang-Liu/MultimodalOCR), [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) or [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval) and feel free to contact us via email at [email protected]. We will update the leaderboard in time."""
     gr.Markdown(md_tail, elem_id="leaderboard_markdown")
 
-def build_demo(leaderboard_table_file, recog_table_file, Inaccessible_model_file):
+def build_demo(leaderboard_table_file):
     text_size = gr.themes.sizes.text_lg
 
     with gr.Blocks(
@@ -294,7 +171,7 @@ def build_demo(leaderboard_table_file, recog_table_file, Inaccessible_model_file
         css=block_css,
     ) as demo:
         leader_components = build_leaderboard_tab(
-            leaderboard_table_file, recog_table_file,Inaccessible_model_file,show_plot=True
+            leaderboard_table_file,show_plot=True
         )
     return demo
 
@@ -302,9 +179,7 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--share", action="store_true")
     parser.add_argument("--OCRBench_file", type=str, default="./OCRBench.csv")
-    parser.add_argument("--TextRecognition_file", type=str, default="./TextRecognition.csv")
-    parser.add_argument("--Inaccessible_model_file", type=str, default="./Inaccessible_model.csv")
     args = parser.parse_args()
 
-    demo = build_demo(args.OCRBench_file, args.TextRecognition_file, args.Inaccessible_model_file)
+    demo = build_demo(args.OCRBench_file)
     demo.launch()