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
import matplotlib.pyplot as plt
import numpy as np
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

from src.about import (
    CITATION_BUTTON_LABEL,
    CITATION_BUTTON_TEXT,
    EVALUATION_QUEUE_TEXT,
    INTRODUCTION_TEXT,
    LLM_BENCHMARKS_TEXT,
    TITLE,
    REPORT_TEXT,
)
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()

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)

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,
    )

def draw_grace_radar():
    models = ["LLaMA-2-7b-chat", "Qwen-7B-Chat"]
    labels = ["Instruction Following", "Coding", "Math", "Reasoning", "Multilingual"]
    scores = [
        [0.89, 0.87, 0.82, 0.92, 0.88],
        [0.85, 0.84, 0.80, 0.90, 0.91],
    ]
    angles = np.linspace(0, 2 * np.pi, len(labels), endpoint=False).tolist()
    angles += angles[:1]

    fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(polar=True))
    for model, score in zip(models, scores):
        score += score[:1]
        ax.plot(angles, score, label=model)
        ax.fill(angles, score, alpha=0.25)
    ax.set_theta_offset(np.pi / 2)
    ax.set_theta_direction(-1)
    ax.set_thetagrids(np.degrees(angles[:-1]), labels)
    ax.set_ylim(0, 1)
    ax.legend(loc='upper right', bbox_to_anchor=(1.3, 1.1))
    plt.title("GRACE Radar Evaluation")
    return fig

# --- 本地加载两个大模型 ---
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")

MODEL_A_PATH = "gpt2"
MODEL_B_PATH = "distilgpt2"

print("Loading Model A...")
tokenizer_a = AutoTokenizer.from_pretrained(MODEL_A_PATH)
model_a = AutoModelForCausalLM.from_pretrained(MODEL_A_PATH).to(device)
print("Model A loaded.")

print("Loading Model B...")
tokenizer_b = AutoTokenizer.from_pretrained(MODEL_B_PATH)
model_b = AutoModelForCausalLM.from_pretrained(MODEL_B_PATH).to(device)
print("Model B loaded.")

def model_a_infer(input_text: str) -> str:
    inputs = tokenizer_a(input_text, return_tensors="pt").to(device)
    with torch.no_grad():
        outputs = model_a.generate(
            **inputs,
            max_new_tokens=64,
            do_sample=True,
            temperature=0.8,
            pad_token_id=tokenizer_a.eos_token_id,
        )
    input_len = len(inputs['input_ids'][0])
    generated_only = tokenizer_a.decode(outputs[0][input_len:], skip_special_tokens=True)
    return generated_only.strip()

def model_b_infer(input_text: str) -> str:
    inputs = tokenizer_b(input_text, return_tensors="pt").to(device)
    with torch.no_grad():
        outputs = model_b.generate(
            **inputs,
            max_new_tokens=64,
            do_sample=True,
            temperature=0.8,
            pad_token_id=tokenizer_b.eos_token_id,
        )
    input_len = len(inputs['input_ids'][0])
    generated_only = tokenizer_b.decode(outputs[0][input_len:], skip_special_tokens=True)
    return generated_only.strip()

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)
            radar_plot = gr.Plot(value=draw_grace_radar(), label="GRACE Radar Evaluation")
            gr.Markdown("本图展示了两个模型在 GRACE 五大任务维度下的性能对比。")

        with gr.TabItem("🧪 Arena", elem_id="arena-tab-table", id=4):
            gr.Markdown("## 🔁 Arena: 模型同台竞技")
            arena_input = gr.Textbox(label="输入文本 (适用于所有模型)", lines=3)
            arena_output_a = gr.Textbox(label="模型 A 输出 (GPT2)", lines=6)
            arena_output_b = gr.Textbox(label="模型 B 输出 (DistilGPT2)", lines=6)
            arena_button = gr.Button("运行 Arena 对比")

            def run_arena(text):
                if not text.strip():
                    return "请输入内容", "请输入内容"
                return model_a_infer(text), model_b_infer(text)

            arena_button.click(run_arena, inputs=arena_input, outputs=[arena_output_a, arena_output_b])

        with gr.TabItem("📝 About", elem_id="about-tab-table", id=2):
            gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
            gr.Markdown(REPORT_TEXT, elem_classes="markdown-text")

        with gr.TabItem("🚀 Submit here!", elem_id="submit-tab-table", id=3):
            with gr.Column():
                gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")

                with gr.Accordion(
                    f"✅ Finished Evaluations ({len(finished_eval_queue_df)})",
                    open=False,
                ):
                    gr.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,
                ):
                    gr.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,
                ):
                    gr.Dataframe(
                        value=pending_eval_queue_df,
                        headers=EVAL_COLS,
                        datatype=EVAL_TYPES,
                        row_count=5,
                    )

                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()