Update app.py

app.py

@@ -155,11 +155,13 @@ def get_recog_table(model_table_df):
         values.append(row)
     return values
 
-def build_leaderboard_tab(leaderboard_table_file, text_recog_file, show_plot=False):
+def build_leaderboard_tab(leaderboard_table_file, text_recog_file, Inaccessible_model_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
@@ -168,8 +170,8 @@ def build_leaderboard_tab(leaderboard_table_file, text_recog_file, show_plot=Fal
         gr.Markdown(md_head, elem_id="leaderboard_markdown")
         with gr.Tabs() as tabs:
             # arena table
-            arena_table_vals = get_arena_table(model_table_df)
             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(
@@ -240,7 +242,41 @@ def build_leaderboard_tab(leaderboard_table_file, text_recog_file, show_plot=Fal
                     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."
+                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,
+                )
     else:
         pass
     md_tail = f"""
@@ -249,7 +285,7 @@ def build_leaderboard_tab(leaderboard_table_file, text_recog_file, show_plot=Fal
     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 zhangli123@hust.edu.cn. We will update the leaderboard in time."""
     gr.Markdown(md_tail, elem_id="leaderboard_markdown")
 
-def build_demo(leaderboard_table_file, recog_table_file):
+def build_demo(leaderboard_table_file, recog_table_file, Inaccessible_model_file):
     text_size = gr.themes.sizes.text_lg
 
     with gr.Blocks(
@@ -258,7 +294,7 @@ def build_demo(leaderboard_table_file, recog_table_file):
         css=block_css,
     ) as demo:
         leader_components = build_leaderboard_tab(
-            leaderboard_table_file, recog_table_file,show_plot=True
+            leaderboard_table_file, recog_table_file,Inaccessible_model_file,show_plot=True
         )
     return demo
 
@@ -267,7 +303,8 @@ if __name__ == "__main__":
     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)
+    demo = build_demo(args.OCRBench_file, args.TextRecognition_file, args.Inaccessible_model_file)
     demo.launch()