app.py

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 Initialisation ---
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()

# --- Data Loading ---
LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)

(
    finished_eval_queue_df,
    running_eval_queue_df,
    pending_eval_queue_df,
) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)

# --- 📌 Define Column Groups ---

# Tab 1: Generation (Table 1)
COLS_GEN = [
    "Model",
    # LongText2Video
    "LongText2Video / CLIP", "LongText2Video / DINO", "LongText2Video / MLLM",
    # Entities2Video
    "Entities2Video / CLIP", "Entities2Video / DINO", "Entities2Video / MLLM",
    # Video2Video
    "Video2Video / CLIP", "Video2Video / DINO", "Video2Video / MLLM"
]

# Tab 2: Perception & Editing (Table 2)
COLS_LONG = [
    "Model", 
    "LongVideo QA / Acc",      # Understanding
    "Editing / CLIP",          # Editing
    "Editing / DINO",
    "Editing / MLLM",
    "Segmentation / J",        # Segmentation
    "Segmentation / F",
    "Segmentation / J&F"       
]

# --- UI Layout ---
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:
        
        # === Tab 1: Generation (Table 1) ===
        with gr.TabItem("🎬 Generation", elem_id="tab-gen", id=0):
            gr.Markdown("### Table 1: Comparison across LongText2Video, Entities2Video and Video2Video")
            
            # 1. Select Columns
            valid_gen_cols = [c for c in COLS_GEN if c in LEADERBOARD_DF.columns]
            df_gen = LEADERBOARD_DF[valid_gen_cols].copy()
            
            # 2. Filter Rows (Only specific models for Table 1)
            target_models_gen = ["UniVA", "LTX-Video", "Wan", "Seedance"]
            
            if "Model" in df_gen.columns:
                df_gen = df_gen[df_gen["Model"].isin(target_models_gen)]
                
                # 3. Sort by defined order (Using Categorical)
                df_gen["Model"] = pd.Categorical(df_gen["Model"], categories=target_models_gen, ordered=True)
                df_gen = df_gen.sort_values("Model")
                
                # 🔴【关键修复】排序完后，必须转回 string 类型，否则后面 fillna("-") 会报错
                df_gen["Model"] = df_gen["Model"].astype(str)

            # 4. Clean up: Replace NaN with "-"
            df_gen = df_gen.fillna("-")

            gr.Dataframe(
                value=df_gen,
                headers=valid_gen_cols,
                datatype="markdown",
                elem_id="leaderboard-table-gen",
                interactive=False,
                visible=True,
            )

        # === Tab 2: Perception & Editing (Table 2) ===
        with gr.TabItem("🧠 Perception & Editing", elem_id="tab-long", id=1):
            gr.Markdown("### Table 2: Understanding, Editing, and Segmentation")
            
            # 1. Select Columns
            valid_long_cols = [c for c in COLS_LONG if c in LEADERBOARD_DF.columns]
            df_long = LEADERBOARD_DF[valid_long_cols].copy()

            # 2. Filter Rows (Only specific models for Table 2)
            target_models_long = [
                "UniVA", 
                "InternVL3-38B", 
                "Qwen2.5-VL-72B", 
                "Gemini 2.5 Pro", 
                "GPT-4o", 
                "Vace", 
                "SA2VA"
            ]

            if "Model" in df_long.columns:
                df_long = df_long[df_long["Model"].isin(target_models_long)]
                
                # 3. Sort: Default by Understanding Acc
                sort_col = "LongVideo QA / Acc"
                if sort_col in df_long.columns:
                    df_long = df_long.sort_values(by=sort_col, ascending=False)
            
            # 4. Clean up: Replace NaN with "-"
            df_long = df_long.fillna("-")

            gr.Dataframe(
                value=df_long,
                headers=valid_long_cols,
                datatype="markdown",
                elem_id="leaderboard-table-long",
                interactive=False,
                visible=True,
            )

        # === Tab 3: About ===
        with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
            gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")

        # === Tab 4: Submit ===
        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,
            )

    # --- Citation ---
    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()