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app.py

@@ -1,24 +1,417 @@
+# -*- coding: utf-8 -*-
+"""
+@Project -> File   ：leaderboard -> app.py.py
+@Author : Www多金
+@Date   ：2023/12/12 15:57
+@Desc   : study Mechine Learning
+"""
 import gradio as gr
-
-def submit_model(model_name, model_description):
-    # 这里的逻辑是用来处理模型提交的。
-    # 你需要将模型添加到评估队列，并返回提交状态。
-    # 这个示例函数只是返回一个确认消息。
-    return f"模型 '{model_name}' 已提交评估。描述: {model_description}"
-
-# 创建一个Gradio表单接口
-iface = gr.Interface(
-    fn=submit_model,  # 提交模型的函数
-    inputs=[          # 输入字段
-        gr.Textbox(label="模型名称"),
-        gr.TextArea(label="模型描述")
-    ],
-    outputs=[         # 输出字段
-        gr.Text(label="提交状态")
-    ],
-    title="Open MLLM Leaderboard",  # 界面标题
-    description="The Open LLM Leaderboard aims to track, rank, and evaluate LLMs and chatbots.",  # 界面描述
-    article="Submit a model for automated evaluation on the Open LLM Leaderboard on the 'Submit' page! The leaderboard's backend runs the great EleutherAI Language Model Evaluation Harness - read more details in the 'About' page!"  # 底部的文章或额外信息
+import pandas as pd
+from apscheduler.schedulers.background import BackgroundScheduler
+from huggingface_hub import snapshot_download
+from gradio_space_ci import configure_space_ci # FOR CI
+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
+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, H4_TOKEN, IS_PUBLIC, QUEUE_REPO, REPO_ID, RESULTS_REPO
+from src.populate import get_evaluation_queue_df, get_leaderboard_df
+from src.submission.submit import add_new_eval
+from src.tools.collections import update_collections
+from src.tools.plots import (
+    create_metric_plot_obj,
+    create_plot_df,
+    create_scores_df,
+)
+
+def restart_space():
+    API.restart_space(repo_id=REPO_ID, token=H4_TOKEN)
+
+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
+    )
+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
+    )
+except Exception:
+    restart_space()
+
+raw_data, original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
+update_collections(original_df.copy())
+leaderboard_df = original_df.copy()
+
+plot_df = create_plot_df(create_scores_df(raw_data))
+(
+    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 load_query(request: gr.Request):  # triggered only once at startup => read query parameter if it exists
+    query = request.query_params.get("query") or ""
+    return query, query # return one for the "search_bar", one for a hidden component that triggers a reload only if value has changed
+
+def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
+    return df[(df[AutoEvalColumn.dummy.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] + [AutoEvalColumn.dummy.name]
+    ]
+    return filtered_df
+
+
+def filter_queries(query: str, filtered_df: pd.DataFrame):
+    """Added by Abishek"""
+    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
+    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]
+
+    return filtered_df
+# def restart_space():
+#     API.restart_space(repo_id=REPO_ID, token=H4_TOKEN)
+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("🏅 MLLM 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",
+                        )
+                    with gr.Row():
+                        shown_columns = gr.CheckboxGroup(
+                            choices=[
+                                c.name
+                                for c in fields(AutoEvalColumn)
+                                if not c.hidden and not c.never_hidden and not c.dummy
+                            ],
+                            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
+                    + [AutoEvalColumn.dummy.name]
+                ],
+                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%"]
+            )
+
+            # 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, 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("📈 Metrics through time", elem_id="llm-benchmark-tab-table", id=4):
+        #     with gr.Row():
+        #         with gr.Column():
+        #             chart = create_metric_plot_obj(
+        #                 plot_df,
+        #                 [AutoEvalColumn.average.name],
+        #                 title="Average of Top Scores and Human Baseline Over Time (from last update)",
+        #             )
+        #             gr.Plot(value=chart, min_width=500)
+        #         with gr.Column():
+        #             chart = create_metric_plot_obj(
+        #                 plot_df,
+        #                 BENCHMARK_COLS,
+        #                 title="Top Scores and Human Baseline Over Time (from last update)",
+        #             )
+        #             gr.Plot(value=chart, min_width=500)
+        # 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")
+        #             private = gr.Checkbox(False, label="Private", visible=not IS_PUBLIC)
+        #             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,
+        #             private,
+        #             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()
+# Both launches the space and its CI
+configure_space_ci(
+    demo.queue(default_concurrency_limit=40),
+    # trusted_authors=[],  # add manually trusted authors
+    # private="True",  # ephemeral spaces will have same visibility as the main space. Otherwise, set to `True` or `False` explicitly.
+    # variables={},  # We overwrite HF_HOME as tmp CI spaces will have no cache
+    # secrets=[" HF_TOKEN", "H4_TOKEN"],  # which secret do I want to copy from the main space? Can be a `List[str]`.
+    # hardware=None,  # "cpu-basic" by default. Otherwise set to "auto" to have same hardware as the main space or any valid string value.
+    # storage=None,  # no storage by default. Otherwise set to "auto" to have same storage as the main space or any valid string value.
+).launch()
+
 
-iface.launch()
+# scheduler = BackgroundScheduler()
+# scheduler.add_job(restart_space, "interval", seconds=1800)
+# scheduler.start()
+# def submit_model(model_name, model_description):
+#     # 这里的逻辑是用来处理模型提交的。
+#     # 你需要将模型添加到评估队列，并返回提交状态。
+#     # 这个示例函数只是返回一个确认消息。
+#     return f"模型 '{model_name}' 已提交评估。描述: {model_description}"
+#
+# # 创建一个Gradio表单接口
+# iface = gr.Interface(
+#     fn=submit_model,  # 提交模型的函数
+#     inputs=[          # 输入字段
+#         gr.Textbox(label="模型名称"),
+#         gr.TextArea(label="模型描述")
+#     ],
+#     outputs=[         # 输出字段
+#         gr.Text(label="提交状态")
+#     ],
+#     title="Open MLLM Leaderboard",  # 界面标题
+#     description="The Open LLM Leaderboard aims to track, rank, and evaluate LLMs and chatbots.",  # 界面描述
+#     article="Submit a model for automated evaluation on the Open LLM Leaderboard on the 'Submit' page! The leaderboard's backend runs the great EleutherAI Language Model Evaluation Harness - read more details in the 'About' page!"  # 底部的文章或额外信息
+# )
+#
+# iface.launch()

requirements.txt

@@ -0,0 +1,18 @@
+APScheduler==3.10.1
+black==23.11.0
+click==8.1.3
+datasets==2.14.5
+gradio==4.4.0
+gradio_client==0.7.0
+huggingface-hub>=0.18.0
+matplotlib==3.7.1
+numpy==1.24.2
+pandas==2.0.0
+plotly==5.14.1
+python-dateutil==2.8.2
+requests==2.28.2
+sentencepiece
+tqdm==4.65.0
+transformers==4.36.0
+tokenizers>=0.15.0
+gradio-space-ci@git+https://huggingface.co/spaces/Wauplin/gradio-space-ci # CI !!!