[Update]Back to leaderboard

app.py

@@ -1,12 +1,15 @@
+import subprocess
 import gradio as gr
 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,
-    #FAQ_TEXT,
     TITLE,
 )
 from src.display.css_html_js import custom_css
@@ -23,163 +26,320 @@ from src.display.utils import (
     WeightType,
     Precision
 )
-from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, H4_TOKEN, IS_PUBLIC, QUEUE_REPO, REPO_ID, RESULTS_REPO
-from PIL import Image
-from dummydatagen import dummy_data_for_plot, create_metric_plot_obj_1, dummydf
-import copy
+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, token=H4_TOKEN)
+    API.restart_space(repo_id=REPO_ID)
 
-# Searching and filtering
-raw_data = dummydf()
-methods = list(set(raw_data['Method']))
-metrics = ["Style-UA", "Style-IRA", "Style-CRA", "Object-UA", "Object-IRA", "Object-CRA", "FID", "Time (s)", "Storage (GB)", "Memory (GB)"]
+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()
+
+
+raw_data, original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
+leaderboard_df = original_df.copy()
 
+(
+    finished_eval_queue_df,
+    running_eval_queue_df,
+    pending_eval_queue_df,
+) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
+
+
+# Searching and filtering
 def update_table(
     hidden_df: pd.DataFrame,
-    columns_1: list,
-    columns_2: list,
-    columns_3: list,
-    model1: list,
+    columns: list,
+    type_query: list,
+    precision_query: str,
+    size_query: list,
+    show_deleted: bool,
+    query: str,
 ):
+    filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted)
+    filtered_df = filter_queries(query, filtered_df)
+    df = select_columns(filtered_df, columns)
+    return df
 
-    filtered_df = select_columns(hidden_df, columns_1, columns_2, columns_3)
 
-    filtered_df = filter_model1(filtered_df, model1)
-
-    return filtered_df
+def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
+    return df[(df[AutoEvalColumn.model.name].str.contains(query, case=False))]
 
 
-def select_columns(df: pd.DataFrame, columns_1: list, columns_2: list,  columns_3: list) -> pd.DataFrame:
-    always_here_cols = ["Method"]
-    
+def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
+    always_here_cols = [
+        AutoEvalColumn.model_type_symbol.name,
+        AutoEvalColumn.model.name,
+    ]
     # We use COLS to maintain sorting
-    all_columns = metrics
+    filtered_df = df[
+        always_here_cols + [c for c in COLS if c in df.columns and c in columns] 
+    ]
+    return filtered_df
 
-    if (len(columns_1)+len(columns_2) + len(columns_3)) == 0:
-        filtered_df = df[
-            always_here_cols +
-            [c for c in all_columns if c in df.columns]
-        ]
 
-    else:
-        filtered_df = df[
-            always_here_cols +
-            [c for c in all_columns if c in df.columns and (c in columns_1 or c in columns_2 or c in columns_3 ) ]
-        ]
+def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame:
+    final_df = []
+    if query != "":
+        queries = [q.strip() for q in query.split(";")]
+        for _q in queries:
+            _q = _q.strip()
+            if _q != "":
+                temp_filtered_df = search_table(filtered_df, _q)
+                if len(temp_filtered_df) > 0:
+                    final_df.append(temp_filtered_df)
+        if len(final_df) > 0:
+            filtered_df = pd.concat(final_df)
+            filtered_df = filtered_df.drop_duplicates(
+                subset=[AutoEvalColumn.model.name, AutoEvalColumn.precision.name, AutoEvalColumn.revision.name]
+            )
 
     return filtered_df
 
 
-def filter_model1(df: pd.DataFrame, model_query: list) -> pd.DataFrame:
+def filter_models(
+    df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool
+) -> pd.DataFrame:
     # Show all models
-    if len(model_query) == 0:
-        return df
-    
-    filtered_df = df
-    filtered_df = filtered_df[filtered_df["Method"].isin(model_query)]
-    return filtered_df
+    if show_deleted:
+        filtered_df = df
+    else:  # Show only still on the hub models
+        filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True]
 
+    type_emoji = [t[0] for t in type_query]
+    filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
+    filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
 
+    numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
+    params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
+    mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
+    filtered_df = filtered_df.loc[mask]
 
-demo = gr.Blocks(css=custom_css)
+    return filtered_df
 
 
+demo = gr.Blocks(css=custom_css)
 with demo:
-    with gr.Row():
-        gr.Image("./assets/logo.png", height="200px", width="200px", scale=0.1,
-                 show_download_button=False, container=False)
-        gr.HTML(TITLE, elem_id="title")
-
+    gr.HTML(TITLE)
     gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
+
     with gr.Tabs(elem_classes="tab-buttons") as tabs:
-        with gr.TabItem("🏅 UnlearnCanvas Benchmark", elem_id="llm-benchmark-tab-table", id=0):
+        with gr.TabItem("UnlearnDiffAtk Benchmark", elem_id="UnlearnDiffAtk-benchmark-tab-table", id=0):
             with gr.Row():
                 with gr.Column():
                     with gr.Row():
-                        model1_column = gr.CheckboxGroup(
-                        label="Unlearning Methods",
-                        choices=methods,
-                        interactive=True,
-                        elem_id="column-select",
-                    )
-                        
-                    with gr.Row():
-                        shown_columns_1 = gr.CheckboxGroup(
-                            choices=["Style-UA", "Style-IRA", "Style-CRA", "Object-UA", "Object-IRA", "Object-CRA"],
-                            label="Style / Object  Unlearning Effectiveness",
-                            elem_id="column-select",
-                            interactive=True,
+                        search_bar = gr.Textbox(
+                            placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...",
+                            show_label=False,
+                            elem_id="search-bar",
                         )
-                        
                     with gr.Row():
-                        shown_columns_2 = gr.CheckboxGroup(
-                            choices=["FID"],
-                            label="Image Quality",
+                        shown_columns = gr.CheckboxGroup(
+                            choices=[
+                                c.name
+                                for c in fields(AutoEvalColumn)
+                                if not c.hidden and not c.never_hidden
+                            ],
+                            value=[
+                                c.name
+                                for c in fields(AutoEvalColumn)
+                                if c.displayed_by_default and not c.hidden and not c.never_hidden
+                            ],
+                            label="Select columns to show",
                             elem_id="column-select",
                             interactive=True,
                         )
-                    
                     with gr.Row():
-                        shown_columns_3 = gr.CheckboxGroup(
-                            choices=["Time (s)", "Memory (GB)", "Storage (GB)"],
-                            label="Resource Costs",
-                            elem_id="column-select",
-                            interactive=True,
+                        deleted_models_visibility = gr.Checkbox(
+                            value=False, label="Show gated/private/deleted models", interactive=True
                         )
-                    
+                with gr.Column(min_width=320):
+                    #with gr.Box(elem_id="box-filter"):
+                    filter_columns_type = gr.CheckboxGroup(
+                        label="Unlearning types",
+                        choices=[t.to_str() for t in ModelType],
+                        value=[t.to_str() for t in ModelType],
+                        interactive=True,
+                        elem_id="filter-columns-type",
+                    )
+                    filter_columns_precision = gr.CheckboxGroup(
+                        label="Precision",
+                        choices=[i.value.name for i in Precision],
+                        value=[i.value.name for i in Precision],
+                        interactive=True,
+                        elem_id="filter-columns-precision",
+                    )
+                    filter_columns_size = gr.CheckboxGroup(
+                        label="Model sizes (in billions of parameters)",
+                        choices=list(NUMERIC_INTERVALS.keys()),
+                        value=list(NUMERIC_INTERVALS.keys()),
+                        interactive=True,
+                        elem_id="filter-columns-size",
+                    )
 
             leaderboard_table = gr.components.Dataframe(
-                value= raw_data,
+                value=leaderboard_df[
+                    [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
+                    + shown_columns.value
+                ],
+                headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value,
+                datatype=TYPES,
                 elem_id="leaderboard-table",
                 interactive=False,
                 visible=True,
-                # column_widths=["2%", "33%"]
             )
 
-            game_bench_df_for_search = gr.components.Dataframe(
-                value= raw_data,
-                elem_id="leaderboard-table",
-                interactive=False,
+            # Dummy leaderboard for handling the case when the user uses backspace key
+            hidden_leaderboard_table_for_search = gr.components.Dataframe(
+                value=original_df[COLS],
+                headers=COLS,
+                datatype=TYPES,
                 visible=False,
-                # column_widths=["2%", "33%"]
             )
-
-
-            for selector in [shown_columns_1,shown_columns_2, shown_columns_3, model1_column]:
+            search_bar.submit(
+                update_table,
+                [
+                    hidden_leaderboard_table_for_search,
+                    shown_columns,
+                    filter_columns_type,
+                    filter_columns_precision,
+                    filter_columns_size,
+                    deleted_models_visibility,
+                    search_bar,
+                ],
+                leaderboard_table,
+            )
+            for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size, deleted_models_visibility]:
                 selector.change(
                     update_table,
-                    [   
-                        game_bench_df_for_search,
-                        shown_columns_1,
-                        shown_columns_2,
-                        shown_columns_3,
-                        model1_column, 
+                    [
+                        hidden_leaderboard_table_for_search,
+                        shown_columns,
+                        filter_columns_type,
+                        filter_columns_precision,
+                        filter_columns_size,
+                        deleted_models_visibility,
+                        search_bar,
                     ],
                     leaderboard_table,
                     queue=True,
                 )
-                
-        with gr.TabItem("🚀 Model Submit", elem_id="llm-benchmark-tab-table", id=1):
-            gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
-            gr.Markdown(FAQ_TEXT, elem_classes="markdown-text")
 
         with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
             gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
-            gr.Markdown(FAQ_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=True):
+        with gr.Accordion("📙 Citation", open=False):
             citation_button = gr.Textbox(
                 value=CITATION_BUTTON_TEXT,
                 label=CITATION_BUTTON_LABEL,
-                lines=8,
+                lines=10,
                 elem_id="citation-button",
                 show_copy_button=True,
             )
 
-
-demo.launch()
-
+scheduler = BackgroundScheduler()
+scheduler.add_job(restart_space, "interval", seconds=1800)
+scheduler.start()
+demo.queue(default_concurrency_limit=40).launch()