Create app.py

app.py

@@ -0,0 +1,174 @@
+import ast
+import argparse
+import glob
+import pickle
+
+import gradio as gr
+import numpy as np
+import pandas as pd
+block_css = """
+#notice_markdown {
+    font-size: 104%
+}
+#notice_markdown th {
+    display: none;
+}
+#notice_markdown td {
+    padding-top: 6px;
+    padding-bottom: 6px;
+}
+#leaderboard_markdown {
+    font-size: 104%
+}
+#leaderboard_markdown td {
+    padding-top: 6px;
+    padding-bottom: 6px;
+}
+#leaderboard_dataframe td {
+    line-height: 0.1em;
+}
+footer {
+    display:none !important
+}
+.image-container {
+    display: flex;
+    align-items: center;
+    padding: 1px;
+}
+.image-container img {
+    margin: 0 30px;
+    height: 20px;
+    max-height: 100%;
+    width: auto;
+    max-width: 20%;
+}
+"""
+def model_hyperlink(model_name, link):
+    return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
+def load_leaderboard_table_csv(filename, add_hyperlink=True):
+    lines = open(filename).readlines()
+    heads = [v.strip() for v in lines[0].split(",")]
+    rows = []
+    for i in range(1, len(lines)):
+        row = [v.strip() for v in lines[i].split(",")]
+        for j in range(len(heads)):
+            item = {}
+            for h, v in zip(heads, row):
+                if h != "Model" and h != "Link":
+                    item[h] = int(v)
+                else:
+                    item[h] = v
+            if add_hyperlink:
+                item["Model"] = model_hyperlink(item["Model"], item["Link"])
+        rows.append(item)
+    return rows
+
+def get_arena_table(model_table_df):
+    # sort by rating
+    model_table_df = model_table_df.sort_values(by=["Final Score"], ascending=False)
+    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["Text Recognition"].values[model_key]
+        )
+
+        row.append(
+            model_table_df["Scene Text-Centric VQA"].values[model_key]
+        )
+
+        row.append(
+            model_table_df["Doc-Oriented VQA"].values[model_key]
+        )
+
+        row.append(
+            model_table_df["KIE"].values[model_key]
+        )
+
+        row.append(
+            model_table_df["HMER"].values[model_key]
+        )
+
+        row.append(
+            model_table_df["Final Score"].values[model_key]
+        )
+        values.append(row)
+    return values
+
+def build_leaderboard_tab(leaderboard_table_file, show_plot=False):
+    if leaderboard_table_file:
+        data = load_leaderboard_table_csv(leaderboard_table_file)
+        model_table_df = pd.DataFrame(data)
+        md_head = f"""
+        # 🏆 OCRBench Leaderboard
+        | [GitHub](https://github.com/Yuliang-Liu/MultimodalOCR) | [Paper](https://arxiv.org/abs/2305.07895) |
+        """
+        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):
+                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",
+                        "Text Recognition",
+                        "Scene Text-Centric VQA",
+                        "Doc-Oriented VQA",
+                        "KIE",
+                        "HMER",
+                        "Final Score",
+                    ],
+                    datatype=[
+                        "str",
+                        "markdown",
+                        "number",
+                        "number",
+                        "number",
+                        "number",
+                        "number",
+                        "number",
+                    ],
+                    value=arena_table_vals,
+                    elem_id="arena_leaderboard_dataframe",
+                    height=700,
+                    column_widths=[60, 120, 150, 200, 180, 80, 80, 160],
+                    wrap=True,
+                )
+    else:
+        pass
+    md_tail = f"""
+    # Notice
+    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 [email protected]. We will update the leaderboard in time."""
+    gr.Markdown(md_tail, elem_id="leaderboard_markdown")
+
+def build_demo(leaderboard_table_file):
+    text_size = gr.themes.sizes.text_lg
+
+    with gr.Blocks(
+        title="OCRBench Leaderboard",
+        theme=gr.themes.Base(text_size=text_size),
+        css=block_css,
+    ) as demo:
+        leader_components = build_leaderboard_tab(
+            leaderboard_table_file, show_plot=True
+        )
+    return demo
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--share", action="store_true")
+    parser.add_argument("--OCRBench_file", type=str, default="./OCRBench.csv")
+    args = parser.parse_args()
+
+    demo = build_demo(args.OCRBench_file)
+    demo.launch()