Update app.py

app.py

@@ -1,204 +1,456 @@
+# import gradio as gr
+# from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
+# import pandas as pd
+# from apscheduler.schedulers.background import BackgroundScheduler
+# from huggingface_hub import snapshot_download
+
+# from src.about import (
+#     CITATION_BUTTON_LABEL,
+#     CITATION_BUTTON_TEXT,
+#     EVALUATION_QUEUE_TEXT,
+#     INTRODUCTION_TEXT,
+#     LLM_BENCHMARKS_TEXT,
+#     TITLE,
+# )
+# from src.display.css_html_js import custom_css
+# from src.display.utils import (
+#     BENCHMARK_COLS,
+#     COLS,
+#     EVAL_COLS,
+#     EVAL_TYPES,
+#     AutoEvalColumn,
+#     ModelType,
+#     fields,
+#     WeightType,
+#     Precision
+# )
+# from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN
+# from src.populate import get_evaluation_queue_df, get_leaderboard_df
+# from src.submission.submit import add_new_eval
+
+
+# def restart_space():
+#     API.restart_space(repo_id=REPO_ID)
+
+# ### Space initialisation
+# try:
+#     print(EVAL_REQUESTS_PATH)
+#     snapshot_download(
+#         repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
+#     )
+# except Exception:
+#     restart_space()
+# try:
+#     print(EVAL_RESULTS_PATH)
+#     snapshot_download(
+#         repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
+#     )
+# except Exception:
+#     restart_space()
+
+
+# LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
+
+# (
+#     finished_eval_queue_df,
+#     running_eval_queue_df,
+#     pending_eval_queue_df,
+# ) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
+
+# def init_leaderboard(dataframe):
+#     if dataframe is None or dataframe.empty:
+#         raise ValueError("Leaderboard DataFrame is empty or None.")
+#     return Leaderboard(
+#         value=dataframe,
+#         datatype=[c.type for c in fields(AutoEvalColumn)],
+#         select_columns=SelectColumns(
+#             default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default],
+#             cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden],
+#             label="Select Columns to Display:",
+#         ),
+#         search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
+#         hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
+#         filter_columns=[
+#             ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"),
+#             ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"),
+#             ColumnFilter(
+#                 AutoEvalColumn.params.name,
+#                 type="slider",
+#                 min=0.01,
+#                 max=150,
+#                 label="Select the number of parameters (B)",
+#             ),
+#             ColumnFilter(
+#                 AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=True
+#             ),
+#         ],
+#         bool_checkboxgroup_label="Hide models",
+#         interactive=False,
+#     )
+
+
+# demo = gr.Blocks(css=custom_css)
+# with demo:
+#     gr.HTML(TITLE)
+#     gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
+
+#     with gr.Tabs(elem_classes="tab-buttons") as tabs:
+#         with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
+#             leaderboard = init_leaderboard(LEADERBOARD_DF)
+
+#         with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
+#             gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
+
+#         with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3):
+#             with gr.Column():
+#                 with gr.Row():
+#                     gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
+
+#                 with gr.Column():
+#                     with gr.Accordion(
+#                         f"✅ Finished Evaluations ({len(finished_eval_queue_df)})",
+#                         open=False,
+#                     ):
+#                         with gr.Row():
+#                             finished_eval_table = gr.components.Dataframe(
+#                                 value=finished_eval_queue_df,
+#                                 headers=EVAL_COLS,
+#                                 datatype=EVAL_TYPES,
+#                                 row_count=5,
+#                             )
+#                     with gr.Accordion(
+#                         f"🔄 Running Evaluation Queue ({len(running_eval_queue_df)})",
+#                         open=False,
+#                     ):
+#                         with gr.Row():
+#                             running_eval_table = gr.components.Dataframe(
+#                                 value=running_eval_queue_df,
+#                                 headers=EVAL_COLS,
+#                                 datatype=EVAL_TYPES,
+#                                 row_count=5,
+#                             )
+
+#                     with gr.Accordion(
+#                         f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})",
+#                         open=False,
+#                     ):
+#                         with gr.Row():
+#                             pending_eval_table = gr.components.Dataframe(
+#                                 value=pending_eval_queue_df,
+#                                 headers=EVAL_COLS,
+#                                 datatype=EVAL_TYPES,
+#                                 row_count=5,
+#                             )
+#             with gr.Row():
+#                 gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text")
+
+#             with gr.Row():
+#                 with gr.Column():
+#                     model_name_textbox = gr.Textbox(label="Model name")
+#                     revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main")
+#                     model_type = gr.Dropdown(
+#                         choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown],
+#                         label="Model type",
+#                         multiselect=False,
+#                         value=None,
+#                         interactive=True,
+#                     )
+
+#                 with gr.Column():
+#                     precision = gr.Dropdown(
+#                         choices=[i.value.name for i in Precision if i != Precision.Unknown],
+#                         label="Precision",
+#                         multiselect=False,
+#                         value="float16",
+#                         interactive=True,
+#                     )
+#                     weight_type = gr.Dropdown(
+#                         choices=[i.value.name for i in WeightType],
+#                         label="Weights type",
+#                         multiselect=False,
+#                         value="Original",
+#                         interactive=True,
+#                     )
+#                     base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")
+
+#             submit_button = gr.Button("Submit Eval")
+#             submission_result = gr.Markdown()
+#             submit_button.click(
+#                 add_new_eval,
+#                 [
+#                     model_name_textbox,
+#                     base_model_name_textbox,
+#                     revision_name_textbox,
+#                     precision,
+#                     weight_type,
+#                     model_type,
+#                 ],
+#                 submission_result,
+#             )
+
+#     with gr.Row():
+#         with gr.Accordion("📙 Citation", open=False):
+#             citation_button = gr.Textbox(
+#                 value=CITATION_BUTTON_TEXT,
+#                 label=CITATION_BUTTON_LABEL,
+#                 lines=20,
+#                 elem_id="citation-button",
+#                 show_copy_button=True,
+#             )
+
+# scheduler = BackgroundScheduler()
+# scheduler.add_job(restart_space, "interval", seconds=1800)
+# scheduler.start()
+# demo.queue(default_concurrency_limit=40).launch()
+__all__ = ['block', 'make_clickable_model', 'make_clickable_user', 'get_submissions']
+import os
+
 import gradio as gr
-from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
 import pandas as pd
-from apscheduler.schedulers.background import BackgroundScheduler
-from huggingface_hub import snapshot_download
-
-from src.about import (
-    CITATION_BUTTON_LABEL,
-    CITATION_BUTTON_TEXT,
-    EVALUATION_QUEUE_TEXT,
-    INTRODUCTION_TEXT,
-    LLM_BENCHMARKS_TEXT,
-    TITLE,
-)
-from src.display.css_html_js import custom_css
-from src.display.utils import (
-    BENCHMARK_COLS,
-    COLS,
-    EVAL_COLS,
-    EVAL_TYPES,
-    AutoEvalColumn,
-    ModelType,
-    fields,
-    WeightType,
-    Precision
-)
-from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN
-from src.populate import get_evaluation_queue_df, get_leaderboard_df
-from src.submission.submit import add_new_eval
-
-
-def restart_space():
-    API.restart_space(repo_id=REPO_ID)
-
-### Space initialisation
-try:
-    print(EVAL_REQUESTS_PATH)
-    snapshot_download(
-        repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
-    )
-except Exception:
-    restart_space()
-try:
-    print(EVAL_RESULTS_PATH)
-    snapshot_download(
-        repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
-    )
-except Exception:
-    restart_space()
-
-
-LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
-
-(
-    finished_eval_queue_df,
-    running_eval_queue_df,
-    pending_eval_queue_df,
-) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
-
-def init_leaderboard(dataframe):
-    if dataframe is None or dataframe.empty:
-        raise ValueError("Leaderboard DataFrame is empty or None.")
-    return Leaderboard(
-        value=dataframe,
-        datatype=[c.type for c in fields(AutoEvalColumn)],
-        select_columns=SelectColumns(
-            default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default],
-            cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden],
-            label="Select Columns to Display:",
-        ),
-        search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
-        hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
-        filter_columns=[
-            ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"),
-            ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"),
-            ColumnFilter(
-                AutoEvalColumn.params.name,
-                type="slider",
-                min=0.01,
-                max=150,
-                label="Select the number of parameters (B)",
-            ),
-            ColumnFilter(
-                AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=True
-            ),
-        ],
-        bool_checkboxgroup_label="Hide models",
+import json
+import tempfile
+
+from constants import *
+from huggingface_hub import Repository
+HF_TOKEN = os.environ.get("HF_TOKEN")
+
+global data_component, filter_component
+
+def download_csv():
+    # pull the results and return this file!
+    submission_repo = Repository(local_dir=SUBMISSION_NAME, clone_from=SUBMISSION_URL, use_auth_token=HF_TOKEN, repo_type="dataset")
+    submission_repo.git_pull()
+    return CSV_DIR, gr.update(visible=True)
+
+def upload_file(files):
+    file_paths = [file.name for file in files]
+    return file_paths
+
+def add_new_eval(
+    input_file,
+    model_name_textbox: str,
+    revision_name_textbox: str,
+    model_type: str,
+    model_link: str,
+    model_size: str,
+    LLM_type: str,
+    LLM_name_textbox: str,
+):
+    if input_file is None:
+        return "Error! Empty file!"
+
+    upload_data=json.loads(input_file)
+    submission_repo = Repository(local_dir=SUBMISSION_NAME, clone_from=SUBMISSION_URL, use_auth_token=HF_TOKEN, repo_type="dataset")
+    submission_repo.git_pull()
+    csv_data = pd.read_csv(CSV_DIR)
+
+    if LLM_type == 'Other':
+        LLM_name = LLM_name_textbox
+    else:
+        LLM_name = LLM_type
+    
+    if revision_name_textbox == '':
+        col = csv_data.shape[0]
+        model_name = model_name_textbox
+    else:
+        model_name = revision_name_textbox
+        model_name_list = csv_data['Model']
+        name_list = [name.split(']')[0][1:] for name in model_name_list]
+        if revision_name_textbox not in name_list:
+            col = csv_data.shape[0]
+        else:
+            col = name_list.index(revision_name_textbox)    
+    
+    if model_link == '':
+        model_name = model_name  # no url
+    else:
+        model_name = '[' + model_name + '](' + model_link + ')'
+
+    # add new data
+    new_data = [
+        model_type, 
+        model_name, 
+        LLM_name
+        ]
+    for key in TASK_INFO:
+        if key in upload_data:
+            new_data.append(upload_data[key])
+        else:
+            new_data.append(0)
+    csv_data.loc[col] = new_data
+    csv_data = csv_data.to_csv(CSV_DIR, index=False)
+    submission_repo.push_to_hub()
+    return 0
+
+def get_baseline_df():
+    submission_repo = Repository(local_dir=SUBMISSION_NAME, clone_from=SUBMISSION_URL, use_auth_token=HF_TOKEN, repo_type="dataset")
+    submission_repo.git_pull()
+    df = pd.read_csv(CSV_DIR)
+    df = df.sort_values(by="Avg", ascending=False)
+    present_columns = MODEL_INFO + checkbox_group.value
+    df = df[present_columns]
+    return df
+
+def get_all_df():
+    submission_repo = Repository(local_dir=SUBMISSION_NAME, clone_from=SUBMISSION_URL, use_auth_token=HF_TOKEN, repo_type="dataset")
+    submission_repo.git_pull()
+    df = pd.read_csv(CSV_DIR)
+    df = df.sort_values(by="Avg", ascending=False)
+    return df
+
+def on_filter_model_size_method_change(selected_columns):
+    updated_data = get_all_df()
+
+    # columns:
+    selected_columns = [item for item in TASK_INFO if item in selected_columns]
+    present_columns = MODEL_INFO + selected_columns
+    # print("selected_columns",'|'.join(selected_columns))
+    updated_data = updated_data[present_columns]
+    updated_data = updated_data.sort_values(by=selected_columns[0], ascending=False)
+    updated_headers = present_columns
+    update_datatype = [DATA_TITILE_TYPE[COLUMN_NAMES.index(x)] for x in updated_headers]
+    # print(updated_data,present_columns,update_datatype)
+    filter_component = gr.components.Dataframe(
+        value=updated_data, 
+        headers=updated_headers,
+        type="pandas", 
+        datatype=update_datatype,
         interactive=False,
-    )
+        visible=True,
+        )
 
+    return filter_component#.value
 
-demo = gr.Blocks(css=custom_css)
-with demo:
-    gr.HTML(TITLE)
-    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
+block = gr.Blocks()
 
+
+with block:
+    gr.Markdown(
+        LEADERBORAD_INTRODUCTION
+    )
     with gr.Tabs(elem_classes="tab-buttons") as tabs:
-        with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
-            leaderboard = init_leaderboard(LEADERBOARD_DF)
+        with gr.TabItem("📊 MVBench", elem_id="mvbench-tab-table", id=1):
+            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",
+                        lines=10,
+                    )
+    
+            gr.Markdown(
+                TABLE_INTRODUCTION
+            )
 
-        with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
-            gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
+            # selection for column part:
+            checkbox_group = gr.CheckboxGroup(
+                choices=TASK_INFO,
+                value=AVG_INFO,
+                label="Evaluation Dimension",
+                interactive=True,
+            )
 
-        with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3):
-            with gr.Column():
-                with gr.Row():
-                    gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
+            data_component = gr.components.Dataframe(
+                value=get_baseline_df, 
+                headers=COLUMN_NAMES,
+                type="pandas", 
+                datatype=DATA_TITILE_TYPE,
+                interactive=False,
+                visible=True,
+                )
+
+
+            checkbox_group.change(fn=on_filter_model_size_method_change, inputs=[ checkbox_group], outputs=data_component)
+
+        # table 2
+        with gr.TabItem("📝 About", elem_id="mvbench-tab-table", id=2):
+            gr.Markdown(LEADERBORAD_INFO, elem_classes="markdown-text")
+        
+        # table 3 
+        with gr.TabItem("🚀 Submit here! ", elem_id="mvbench-tab-table", id=3):
+            gr.Markdown(LEADERBORAD_INTRODUCTION, elem_classes="markdown-text")
+
+            with gr.Row():
+                gr.Markdown(SUBMIT_INTRODUCTION, elem_classes="markdown-text")
 
-                with gr.Column():
-                    with gr.Accordion(
-                        f"✅ Finished Evaluations ({len(finished_eval_queue_df)})",
-                        open=False,
-                    ):
-                        with gr.Row():
-                            finished_eval_table = gr.components.Dataframe(
-                                value=finished_eval_queue_df,
-                                headers=EVAL_COLS,
-                                datatype=EVAL_TYPES,
-                                row_count=5,
-                            )
-                    with gr.Accordion(
-                        f"🔄 Running Evaluation Queue ({len(running_eval_queue_df)})",
-                        open=False,
-                    ):
-                        with gr.Row():
-                            running_eval_table = gr.components.Dataframe(
-                                value=running_eval_queue_df,
-                                headers=EVAL_COLS,
-                                datatype=EVAL_TYPES,
-                                row_count=5,
-                            )
-
-                    with gr.Accordion(
-                        f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})",
-                        open=False,
-                    ):
-                        with gr.Row():
-                            pending_eval_table = gr.components.Dataframe(
-                                value=pending_eval_queue_df,
-                                headers=EVAL_COLS,
-                                datatype=EVAL_TYPES,
-                                row_count=5,
-                            )
             with gr.Row():
-                gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text")
+                gr.Markdown("# ✉️✨ Submit your model evaluation json file here!", elem_classes="markdown-text")
 
             with gr.Row():
                 with gr.Column():
-                    model_name_textbox = gr.Textbox(label="Model name")
-                    revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main")
+                    model_name_textbox = gr.Textbox(
+                        label="Model name", placeholder="LLaMA-7B"
+                        )
+                    revision_name_textbox = gr.Textbox(
+                        label="Revision Model Name", placeholder="LLaMA-7B"
+                    )
                     model_type = gr.Dropdown(
-                        choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown],
-                        label="Model type",
+                        choices=[                         
+                            "LLM",
+                            "ImageLLM",
+                            "VideoLLM",
+                            "Other", 
+                        ], 
+                        label="Model type", 
                         multiselect=False,
-                        value=None,
+                        value="ImageLLM",
                         interactive=True,
                     )
+                    
+
 
                 with gr.Column():
-                    precision = gr.Dropdown(
-                        choices=[i.value.name for i in Precision if i != Precision.Unknown],
-                        label="Precision",
+                    LLM_type = gr.Dropdown(
+                        choices=["Vicuna-7B", "Flan-T5-XL", "LLaMA-7B", "InternLM-7B", "Other"],
+                        label="LLM type", 
                         multiselect=False,
-                        value="float16",
+                        value="LLaMA-7B",
                         interactive=True,
                     )
-                    weight_type = gr.Dropdown(
-                        choices=[i.value.name for i in WeightType],
-                        label="Weights type",
-                        multiselect=False,
-                        value="Original",
-                        interactive=True,
+                    LLM_name_textbox = gr.Textbox(
+                        label="LLM model (for Other)",
+                        placeholder="LLaMA-13B"
+                    )
+                    model_link = gr.Textbox(
+                        label="Model Link", placeholder="https://huggingface.co/decapoda-research/llama-7b-hf"
+                    )
+                    model_size = gr.Textbox(
+                        label="Model size", placeholder="7B(Input content format must be 'number+B' or '-')"
                     )
-                    base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")
-
-            submit_button = gr.Button("Submit Eval")
-            submission_result = gr.Markdown()
-            submit_button.click(
-                add_new_eval,
-                [
-                    model_name_textbox,
-                    base_model_name_textbox,
-                    revision_name_textbox,
-                    precision,
-                    weight_type,
-                    model_type,
-                ],
-                submission_result,
-            )
+
+            with gr.Column():
+
+                input_file = gr.components.File(label = "Click to Upload a json File", file_count="single", type='binary')
+                submit_button = gr.Button("Submit Eval")
+    
+                submission_result = gr.Markdown()
+                submit_button.click(
+                    add_new_eval,
+                    inputs = [
+                        input_file,
+                        model_name_textbox,
+                        revision_name_textbox,
+                        model_type,
+                        model_link,
+                        model_size,
+                        LLM_type,
+                        LLM_name_textbox,
+                    ],
+                )
+
+
+    def refresh_data():
+        value1 = get_baseline_df()
+        return value1
 
     with gr.Row():
-        with gr.Accordion("📙 Citation", open=False):
-            citation_button = gr.Textbox(
-                value=CITATION_BUTTON_TEXT,
-                label=CITATION_BUTTON_LABEL,
-                lines=20,
-                elem_id="citation-button",
-                show_copy_button=True,
-            )
+        data_run = gr.Button("Refresh")
+    with gr.Row():
+        result_download = gr.Button("Download Leaderboard")
+        file_download = gr.File(label="download the csv of leaderborad.", visible=False)
+        data_run.click(on_filter_model_size_method_change, inputs=[checkbox_group], outputs=data_component)
+        result_download.click(download_csv, inputs=None, outputs= [file_download,file_download])
+
 
-scheduler = BackgroundScheduler()
-scheduler.add_job(restart_space, "interval", seconds=1800)
-scheduler.start()
-demo.queue(default_concurrency_limit=40).launch()
+block.launch()