Update config.py

config.py

@@ -1,74 +1,36 @@
-import gradio as gr
-from gradio_leaderboard import Leaderboard, SelectColumns, ColumnFilter
-import config
-from pathlib import Path
 import pandas as pd
-from datetime import datetime
 
-abs_path = Path(__file__).parent
+TYPES = [
+    "number",  # Rank
+    "str",     # Model Type
+    "str",     # Model Size
+    "str",     # Model
+    "str",     # Param (B)
+    "bool",    # Supports multiple images
+    "number"   # Avg Score
+    "number",  # HumanEval-V
+    "number",  # MBPP-V
+    "number",  # GSM8K-V
+    "number",  # MATH-V
+    "number",  # VP
+    "number",  # Matplotlib
+    "number",  # SVG
+    "number",  # TikZ
+    "number",  # Webpage
+    "number"  # Avg Score
+    "number",  # HumanEval-V
+    "number",  # MBPP-V
+    "number",  # GSM8K-V
+    "number",  # MATH-V
+    "number",  # VP
+    "number",  # Matplotlib
+    "number",  # SVG
+    "number",  # TikZ
+    "number",  # Webpage
+]
 
 
-df = pd.read_json(str(abs_path / "leader_board.json"))
-# Randomly set True/ False for the "MOE" column
-#
-# print(df.info())
-# print(df.columns)
-# print(df.head(1))
-head_content = """
-    # 🏅 BlinkCode Leaderboard
-    ### Welcome to the BlinkCode Leaderboard! On this leaderboard we share the evaluation results of MLLMs obtained by the [OpenSource Framework](github.link).
-
-    ### Currently, BlinkCode Leaderboard covers <model num> different VLMs (including GPT-4v, Gemini, QwenVLMAX, LLaVA, etc.) and 9 different task.
-    ## Main Evaluation Results
-    - Metrics:
-      - Avg Score: The average score on all task (normalized to 0 - 100, the higher the better). 
-      - The scores in the 5 tasks (HumanEval-V, MBPP-V, GSM8K-V, MATH-V, VP) represent the percentage of accuracy.
-      - The scores in the image reconstruction tasks (Matplotlib, SVG, TikZ, Webpage) represent the similarity between the reconstructed images and the original images (normalized to 0 - 100, the higher the better).
-    - By default, we present the unrefined evaluation results,, sorted by the descending order of Avg Score⬆️.   
-      -  The ⭐ symbol indicates results that have undergone two rounds of refinement.  
-        
-        
-    This leaderboard was last updated: <nowtime>.
-    """
-CITATION_BUTTON_TEXT = r"""@misc{2023opencompass,
-    title={OpenCompass: A Universal Evaluation Platform for Foundation Models},
-    author={OpenCompass Contributors},
-    howpublished = {\url{https://github.com/open-compass/opencompass}},
-    year={2023}
-}"""
-CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
-unique_models_count = df["Model"].nunique()
-# print(unique_models_count)
-nowtime = datetime.now()
-formatted_time = nowtime.strftime("%y.%m.%d %H:%M:%S")
-head_content = head_content.replace("<nowtime>", formatted_time).replace('<model num>', str(unique_models_count))
-
-with gr.Blocks() as demo:
-    gr.Markdown(head_content)
-    with gr.Tabs():
-        Leaderboard(
-            value=df,
-            select_columns=SelectColumns(
-                default_selection=config.ON_LOAD_COLUMNS,
-                cant_deselect=["Rank", "Model"],
-                label="Select Columns to Display:",
-            ),
-            search_columns=["Model", "Model Type"],
-            hide_columns=["Model Size", "Model Type", "Supports multiple images"],
-            filter_columns=[
-                "Model Size",
-                "Model Type",
-                "Supports multiple images"
-                # ColumnFilter("Params (B)", default=[0, 20]),
-            ],
-            datatype=config.TYPES,
-            column_widths=["5%", "15%"],
-        )
-    with gr.Row():
-        with gr.Accordion('Citation', open=False):
-            citation_button = gr.Textbox(
-                value=CITATION_BUTTON_TEXT,
-                label=CITATION_BUTTON_LABEL,
-                elem_id='citation-button')
-if __name__ == "__main__":
-    demo.launch()
+ON_LOAD_COLUMNS = [
+"Rank",  "Model", "Params (B)", "Avg Score⬆️", "HumanEval-V", "MBPP-V", "GSM8K-V", "MATH-V", "VP", "Matplotlib", "SVG", "TikZ", "Webpage"
+]
+# "Avg Score⬆️", "HumanEval-V", "MBPP-V", "GSM8K-V", "MATH-V", "VP", "Matplotlib", "SVG", "TikZ", "Webpage"