Update app.py

app.py

@@ -54,7 +54,7 @@ def load_leaderboard_table_csv(filename, add_hyperlink=True):
         for j in range(len(heads)):
             item = {}
             for h, v in zip(heads, row):
-                if h != "Model" and h != "Link":
+                if h != "Model" and h != "Link" and h != "Language Model" and h != "Open Source":
                     item[h] = int(v)
                 else:
                     item[h] = v
@@ -76,6 +76,12 @@ def get_arena_table(model_table_df):
         # 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["Text Recognition"].values[model_key]
         )
@@ -102,10 +108,59 @@ def get_arena_table(model_table_df):
         values.append(row)
     return values
 
-def build_leaderboard_tab(leaderboard_table_file, show_plot=False):
+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, show_plot=False):
     if leaderboard_table_file:
         data = load_leaderboard_table_csv(leaderboard_table_file)
+        data_recog = load_leaderboard_table_csv(text_recog_file)
         model_table_df = pd.DataFrame(data)
+        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) |
@@ -121,6 +176,8 @@ def build_leaderboard_tab(leaderboard_table_file, show_plot=False):
                     headers=[
                         "Rank",
                         "Name",
+                        "Language Model",
+                        "Open Source",
                         "Text Recognition",
                         "Scene Text-Centric VQA",
                         "Doc-Oriented VQA",
@@ -131,6 +188,45 @@ def build_leaderboard_tab(leaderboard_table_file, show_plot=False):
                     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",
@@ -141,9 +237,10 @@ def build_leaderboard_tab(leaderboard_table_file, show_plot=False):
                     value=arena_table_vals,
                     elem_id="arena_leaderboard_dataframe",
                     height=700,
-                    column_widths=[60, 120, 150, 200, 180, 80, 80, 160],
+                    column_widths=[60, 120,150,100, 100, 100, 100, 100, 100,100, 80],
                     wrap=True,
                 )
+
     else:
         pass
     md_tail = f"""
@@ -151,7 +248,7 @@ def build_leaderboard_tab(leaderboard_table_file, show_plot=False):
     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) 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):
+def build_demo(leaderboard_table_file, recog_table_file):
     text_size = gr.themes.sizes.text_lg
 
     with gr.Blocks(
@@ -160,7 +257,7 @@ def build_demo(leaderboard_table_file):
         css=block_css,
     ) as demo:
         leader_components = build_leaderboard_tab(
-            leaderboard_table_file, show_plot=True
+            leaderboard_table_file, recog_table_file,show_plot=True
         )
     return demo
 
@@ -168,7 +265,8 @@ 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")
     args = parser.parse_args()
 
-    demo = build_demo(args.OCRBench_file)
+    demo = build_demo(args.OCRBench_file, args.TextRecognition_file)
     demo.launch()