move to legacy & change submission

app.py

@@ -64,15 +64,8 @@ leaderboard_df = original_df.copy()
 def update_table(
     hidden_df: pd.DataFrame,
     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)
+    df = select_columns(hidden_df, columns)
     return df
 
 
@@ -91,33 +84,6 @@ def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
     ]
     return filtered_df
 
-
-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_models(
-    df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool
-) -> pd.DataFrame:
-    # Show all models
-    return df
-
-
 demo = gr.Blocks(css=custom_css)
 with demo:
     gr.HTML(TITLE)
@@ -126,56 +92,21 @@ with demo:
     with gr.Tabs(elem_classes="tab-buttons") as tabs:
         with gr.TabItem("Leaderboard", 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",
-                    #     )
-                    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",
-                            elem_id="column-select",
-                            interactive=True,
-                        )
-                    # with gr.Row():
-                    #     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="Model 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",
-                #     )
+                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,
+                )
 
             leaderboard_table = gr.components.Dataframe(
                 value=leaderboard_df[
@@ -196,127 +127,51 @@ with demo:
                 datatype=TYPES,
                 visible=False,
             )
-            # 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]: # removed: filter_columns_type, filter_columns_precision, filter_columns_size, deleted_models_visibility
+            for selector in [shown_columns]:
                 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,
                     ],
                     leaderboard_table,
                     queue=True,
                 )
 
-        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.TabItem("Submit Algorithm", 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")
+                gr.Markdown("# Submission Form", 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,
-                    )
+                    submission_file = gr.File(label="Evaluation result (JSON file generated by `run_benchmark.py`, one algorithm on all challenges)")
 
                 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)")
+                    algo_name = gr.Textbox(label="Algorithm display name")
+                    algo_info = gr.Textbox(label="Comments & extra information")
+                    algo_link = gr.Textbox(label="One external link (e.g. GitHub repo, paper, project page)")
+                    submitter_email = gr.Textbox(label="Email address for contact (will be kept confidential)")
 
-            submit_button = gr.Button("Submit Eval")
+            submit_button = gr.Button("Submit Algorithm")
             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_file,
+                    algo_name,
+                    algo_info,
+                    algo_link,
+                    submitter_email,
                 ],
                 submission_result,
             )
 
     with gr.Row():
-        with gr.Accordion("Citation", open=False):
+        with gr.Accordion("About & Citation", open=False):
             citation_button = gr.Textbox(
                 value=CITATION_BUTTON_TEXT,
                 label=CITATION_BUTTON_LABEL,

src/legacy/app.py

@@ -0,0 +1,331 @@
+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,
+    TITLE,
+)
+from src.display.css_html_js import custom_css
+from src.display.utils import (
+    BENCHMARK_COLS,
+    COLS,
+    EVAL_COLS,
+    EVAL_TYPES,
+    NUMERIC_INTERVALS,
+    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)
+
+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: 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
+
+
+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: list) -> pd.DataFrame:
+    always_here_cols = [
+        AutoEvalColumn.model_type_symbol.name,
+        AutoEvalColumn.model.name,
+    ]
+    # 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
+
+
+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_models(
+    df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool
+) -> pd.DataFrame:
+    # Show all models
+    return df
+
+
+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("Leaderboard", 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",
+                    #     )
+                    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",
+                            elem_id="column-select",
+                            interactive=True,
+                        )
+                    # with gr.Row():
+                    #     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="Model 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,
+                elem_id="leaderboard-table",
+                interactive=False,
+                visible=True,
+            )
+
+            # 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,
+            )
+            # 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]: # removed: filter_columns_type, filter_columns_precision, filter_columns_size, deleted_models_visibility
+                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,
+                    ],
+                    leaderboard_table,
+                    queue=True,
+                )
+
+        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()

src/submission/submit.py

@@ -15,13 +15,13 @@ REQUESTED_MODELS = None
 USERS_TO_SUBMISSION_DATES = None
 
 def add_new_eval(
-    model: str,
-    base_model: str,
-    revision: str,
-    precision: str,
-    weight_type: str,
-    model_type: str,
+    submission_file: str,
+    algo_name: str,
+    algo_info: str,
+    algo_link: str,
+    submitter_email: str,
 ):
+    return 'Success!'
     global REQUESTED_MODELS
     global USERS_TO_SUBMISSION_DATES
     if not REQUESTED_MODELS: