test

app.py

@@ -1,199 +1,223 @@
+# 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 initialization
+# try:
+#     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:
+#     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()
+
+# # Prepare your DataFrame
+# LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
+
+# # Initialize DataFrames for evaluation queues
+# 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,
+#     )
+
+# # Start Gradio interface
+# 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)  # Use the prepared DataFrame
+#             gr.Row().update(leaderboard)  # Ensure the leaderboard is included
+
+#         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()
+
+
 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 initialization
-try:
-    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:
-    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()
-
-# Prepare your DataFrame
-LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
-
-# Initialize DataFrames for evaluation queues
-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,
-    )
-
-# Start Gradio interface
-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)  # Use the prepared DataFrame
-            gr.Row().update(leaderboard)  # Ensure the leaderboard is included
-
-        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()
+
+# Sample DataFrame
+data = {
+    'Model': ['Model A', 'Model B', 'Model C'],
+    'Accuracy': [0.95, 0.90, 0.85],
+    'F1 Score': [0.96, 0.89, 0.84]
+}
+df = pd.DataFrame(data)
+
+# Function to display the DataFrame
+def display_table():
+    return df
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Benchmark Results")
+    gr.DataFrame(value=df, label="Benchmark Table", interactive=False)  # Display the DataFrame
+
+# Launch the Gradio app
+demo.launch()