[Update]Change app.py

app.py

@@ -1,15 +1,12 @@
-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 (
+from src.display.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
@@ -26,320 +23,163 @@ from src.display.utils import (
     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
+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
 
 
 def restart_space():
-    API.restart_space(repo_id=REPO_ID)
-
-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)
-
+    API.restart_space(repo_id=REPO_ID, token=H4_TOKEN)
 
 # 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)"]
+
 def update_table(
     hidden_df: pd.DataFrame,
-    columns: list,
-    type_query: list,
-    precision_query: str,
-    size_query: list,
-    show_deleted: bool,
-    query: str,
+    columns_1: list,
+    columns_2: list,
+    columns_3: list,
+    model1: list,
 ):
-    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)
 
-def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
-    return df[(df[AutoEvalColumn.model.name].str.contains(query, case=False))]
+    filtered_df = filter_model1(filtered_df, model1)
+
+    return filtered_df
 
 
-def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
-    always_here_cols = [
-        AutoEvalColumn.model_type_symbol.name,
-        AutoEvalColumn.model.name,
-    ]
+def select_columns(df: pd.DataFrame, columns_1: list, columns_2: list,  columns_3: list) -> pd.DataFrame:
+    always_here_cols = ["Method"]
+    
     # We use COLS to maintain sorting
-    filtered_df = df[
-        always_here_cols + [c for c in COLS if c in df.columns and c in columns] 
-    ]
-    return filtered_df
+    all_columns = metrics
 
+    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]
+        ]
 
-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]
-            )
+    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 ) ]
+        ]
 
     return filtered_df
 
 
-def filter_models(
-    df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool
-) -> pd.DataFrame:
+def filter_model1(df: pd.DataFrame, model_query: list) -> pd.DataFrame:
     # Show all models
-    if show_deleted:
-        filtered_df = df
-    else:  # Show only still on the hub models
-        filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True]
+    if len(model_query) == 0:
+        return df
+    
+    filtered_df = df
+    filtered_df = filtered_df[filtered_df["Method"].isin(model_query)]
+    return filtered_df
 
-    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]
 
-    return filtered_df
+demo = gr.Blocks(css=custom_css)
 
 
-demo = gr.Blocks(css=custom_css)
 with demo:
-    gr.HTML(TITLE)
-    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
+    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.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
     with gr.Tabs(elem_classes="tab-buttons") as tabs:
-        with gr.TabItem("UnlearnDiffAtk Benchmark", elem_id="UnlearnDiffAtk-benchmark-tab-table", id=0):
+        with gr.TabItem("🏅 UnlearnCanvas Benchmark", elem_id="llm-benchmark-tab-table", id=0):
             with gr.Row():
                 with gr.Column():
                     with gr.Row():
-                        search_bar = gr.Textbox(
-                            placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...",
-                            show_label=False,
-                            elem_id="search-bar",
+                        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,
                         )
+                        
                     with gr.Row():
-                        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",
+                        shown_columns_2 = gr.CheckboxGroup(
+                            choices=["FID"],
+                            label="Image Quality",
                             elem_id="column-select",
                             interactive=True,
                         )
+                    
                     with gr.Row():
-                        deleted_models_visibility = gr.Checkbox(
-                            value=False, label="Show gated/private/deleted models", interactive=True
+                        shown_columns_3 = gr.CheckboxGroup(
+                            choices=["Time (s)", "Memory (GB)", "Storage (GB)"],
+                            label="Resource Costs",
+                            elem_id="column-select",
+                            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=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,
+                value= raw_data,
                 elem_id="leaderboard-table",
                 interactive=False,
                 visible=True,
+                # column_widths=["2%", "33%"]
             )
 
-            # 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,
+            game_bench_df_for_search = gr.components.Dataframe(
+                value= raw_data,
+                elem_id="leaderboard-table",
+                interactive=False,
                 visible=False,
+                # column_widths=["2%", "33%"]
             )
-            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]:
+
+
+            for selector in [shown_columns_1,shown_columns_2, shown_columns_3, model1_column]:
                 selector.change(
                     update_table,
-                    [
-                        hidden_leaderboard_table_for_search,
-                        shown_columns,
-                        filter_columns_type,
-                        filter_columns_precision,
-                        filter_columns_size,
-                        deleted_models_visibility,
-                        search_bar,
+                    [   
+                        game_bench_df_for_search,
+                        shown_columns_1,
+                        shown_columns_2,
+                        shown_columns_3,
+                        model1_column, 
                     ],
                     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")
-
-        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,
-            )
+            gr.Markdown(FAQ_TEXT, elem_classes="markdown-text")
 
     with gr.Row():
-        with gr.Accordion("📙 Citation", open=False):
+        with gr.Accordion("📙 Citation", open=True):
             citation_button = gr.Textbox(
                 value=CITATION_BUTTON_TEXT,
                 label=CITATION_BUTTON_LABEL,
-                lines=10,
+                lines=8,
                 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()
+
+demo.launch()
+