Update app.py

app.py

@@ -1,330 +1,330 @@
-import os
-import orjson
-import concurrent.futures
-import random
-import torch
-import threading
-import time
-import uuid
-import glob
-import gradio as gr
-import pandas as pd
-import matplotlib.pyplot as plt
-from huggingface_hub import snapshot_download, hf_hub_download, HfApi
-
-from riichienv import RiichiEnv, GameRule
-
-# 分别导入两个不同架构的加载函数，防止命名冲突
-from model3pLOCAL import load_model as load_model_local
-from model3pNEW import load_model as load_model_new
-
-# ==========================================
-# 0. 核心对抗配置开关 (在这里切换模式)
-# ==========================================
-# True: 1个 NEW架构(TEST_MODEL) VS 2个 LOCAL架构(EXAMINER_MODEL)
-# False: 1个 LOCAL架构(TEST_MODEL) VS 2个 NEW架构(EXAMINER_MODEL)
-ONE_NEW_VS_TWO_LOCAL = True  
-
-# ==========================================
-# 0. 分布式多开与云端持久化配置
-# ==========================================
-DATA_REPO_ID = "ffzeroHua/mj-eval-results"  # 📊 战绩数据集仓库
-MODEL_REPO_ID = "ffzeroHua/Riichi-Model-Repo" # 🧠 模型权重仓库
-HF_TOKEN = os.getenv("HF_TOKEN")
-
-# 为当前节点生成唯一的 ID
-WORKER_ID = os.getenv("WORKER_ID", str(uuid.uuid4())[:6])
-
-# 根据开关状态自动调整保存的文件前缀
-BASE_REPORT_PREFIX = '7k_vs_9070_eval_report'
-if ONE_NEW_VS_TWO_LOCAL:
-    REPORT_FILE_PREFIX = BASE_REPORT_PREFIX
-else:
-    REPORT_FILE_PREFIX = f"inverse_{BASE_REPORT_PREFIX}"
-
-REPORT_FILE = f"{REPORT_FILE_PREFIX}_{WORKER_ID}.txt"
-
-api = HfApi()
-EVAL_RUNNING = True
-
-# 🚀 设定要从云端拉取并进行对抗的两个模型
-TEST_MODEL = "Finetuned_Step7000.pth"
-EXAMINER_MODEL = "Elite4z9070.pth"
-
-def sync_models_from_hub():
-    """启动时从指定的模型仓库拉取对战双方的权重文件"""
-    if HF_TOKEN and "你的用户名" not in MODEL_REPO_ID:
-        print(f"☁️ 正在从模型仓库 [{MODEL_REPO_ID}] 拉取评估模型...")
-        try:
-            hf_hub_download(repo_id=MODEL_REPO_ID, filename=TEST_MODEL, repo_type="model", local_dir=".", token=HF_TOKEN)
-            print(f"✅ 成功拉取测试模型: {TEST_MODEL}")
-            
-            hf_hub_download(repo_id=MODEL_REPO_ID, filename=EXAMINER_MODEL, repo_type="model", local_dir=".", token=HF_TOKEN)
-            print(f"✅ 成功拉取考官模型: {EXAMINER_MODEL}")
-            
-            print("🎉 模型环境准备完毕！")
-        except Exception as e:
-            print(f"❌ 拉取模型失败，请检查文件名或仓库权限: {e}")
-    else:
-        print("⚠️ 未配置有效 HF_TOKEN 或未修改 MODEL_REPO_ID，将尝试使用本地已存在的模型文件。")
-
-def sync_data_from_hub():
-    """启动时从数据集下载所有节点的战绩分片文件"""
-    if HF_TOKEN and "你的用户名" not in DATA_REPO_ID:
-        try:
-            print(f"🔄 正在从 Hub 拉取全局历史战绩数据 (前缀匹配: {REPORT_FILE_PREFIX})...")
-            snapshot_download(
-                repo_id=DATA_REPO_ID,
-                repo_type="dataset",
-                local_dir=".",
-                allow_patterns=REPORT_FILE_PREFIX + "_*.txt",
-                token=HF_TOKEN
-            )
-            print("✅ 历史数据拉取完成。")
-        except Exception as e:
-            print(f"⚠️ 拉取历史战绩失败: {e}")
-
-def sync_data_to_hub():
-    """将当前节点的战绩文件备份到数据集"""
-    if HF_TOKEN and "你的用户名" not in DATA_REPO_ID:
-        try:
-            api.upload_file(
-                path_or_fileobj=REPORT_FILE,
-                path_in_repo=REPORT_FILE,
-                repo_id=DATA_REPO_ID,
-                repo_type="dataset",
-                token=HF_TOKEN
-            )
-            print(f"☁️ 节点 {WORKER_ID} 战绩已同步至 Hub: {time.strftime('%H:%M:%S')}")
-        except Exception as e:
-            print(f"❌ 同步失败: {e}")
-
-# ==========================================
-# 1. 高频及模型加载逻辑
-# ==========================================
-def patch_event_fast(event_str):
-    if '"kita"' in event_str:
-        event_str = event_str.replace('"kita"', '"nukidora"')
-    
-    if '"start_kyoku"' in event_str or '"deltas"' in event_str:
-        event = orjson.loads(event_str)
-        if event.get('type') == 'start_kyoku':
-            scores = event.setdefault('scores', [])
-            while len(scores) < 4: scores.append(0)
-            tehais = event.setdefault('tehais', [])
-            while len(tehais) < 4: tehais.append(["?" for _ in range(13)])
-        if 'deltas' in event:
-            deltas = event['deltas']
-            while len(deltas) < 4: deltas.append(0)
-        return orjson.dumps(event).decode('utf-8')
-    return event_str
-
-def patch_resp_fast(resp_str):
-    if not resp_str: return resp_str
-    return resp_str.replace('"nukidora"', '"kita"')
-
-_MODEL_CACHE = {}
-
-def get_cached_model(player_id: int, model_file: str, arch_type: str):
-    """根据指定的架构类型 (new 或 local) 加载模型"""
-    key = (player_id, model_file, arch_type)
-    if key not in _MODEL_CACHE:
-        torch.set_num_threads(1) 
-        if arch_type == 'new':
-            _MODEL_CACHE[key] = load_model_new(player_id, model_file)
-        else:
-            _MODEL_CACHE[key] = load_model_local(player_id, model_file)
-    return _MODEL_CACHE[key]
-
-class MortalAgent:
-    def __init__(self, player_id: int, model_file: str, arch_type: str):
-        self.player_id = player_id
-        self.arch_type = arch_type
-        self.model = get_cached_model(player_id, model_file, arch_type)
-
-    def act(self, obs):
-        resp = None
-        for event in obs.new_events():
-            event_patched = patch_event_fast(event)
-            resp = patch_resp_fast(self.model.react(event_patched))
-        action = obs.select_action_from_mjai(resp)
-        assert action is not None, "Mortal must return a legal action"
-        return action
-
-# ==========================================
-# 2. 核心对局任务
-# ==========================================
-def play_one_game(game_index):
-    env = RiichiEnv(game_mode="3p-red-half", rule=GameRule.default_tenhou())
-    new_seat = random.randrange(3)
-    
-    agents = {}
-    for i in range(3):
-        if i == new_seat:
-            # 🚀 挑战者位
-            model_file = TEST_MODEL
-            arch = 'new' if ONE_NEW_VS_TWO_LOCAL else 'local'
-        else:
-            # 🚀 考官位
-            model_file = EXAMINER_MODEL
-            arch = 'local' if ONE_NEW_VS_TWO_LOCAL else 'new'
-            
-        agents[i] = MortalAgent(i, model_file, arch)
-    
-    obs_dict = env.reset()
-    while not env.done():
-        actions = {pid: agents[pid].act(obs) for pid, obs in obs_dict.items()}
-        obs_dict = env.step(actions)
-        
-    scores = env.scores()
-    ranks = env.ranks()
-    return ranks[new_seat], scores[new_seat]
-
-# ==========================================
-# 3. 后台独立评估线程
-# ==========================================
-def background_eval_loop():
-    sync_models_from_hub() # 🚀 启动时从 Riichi-Model-Repo 拉取对战模型
-    sync_data_from_hub()   # 🚀 启动时从战绩仓库拉取历史战绩
-    
-    NUM_WORKERS = 1 
-    
-    mode_str = "1只 NEW 挑战 2只 LOCAL" if ONE_NEW_VS_TWO_LOCAL else "1只 LOCAL 挑战 2只 NEW"
-    print(f"🚀 节点 [{WORKER_ID}] 后台对战线程已启动: 模式为 [{mode_str}]")
-    
-    if not os.path.exists(REPORT_FILE):
-        open(REPORT_FILE, 'w').close()
-
-    games_since_last_sync = 0
-
-    with concurrent.futures.ProcessPoolExecutor(max_workers=NUM_WORKERS) as executor:
-        futures = {executor.submit(play_one_game, i) for i in range(NUM_WORKERS * 2)}
-        games_completed = 0
-        
-        while EVAL_RUNNING and futures:
-            done, futures = concurrent.futures.wait(
-                futures, return_when=concurrent.futures.FIRST_COMPLETED
-            )
-            
-            with open(REPORT_FILE, "a") as f:
-                for future in done:
-                    try:
-                        rank, score = future.result()
-                        f.write(f"{rank} {score}\n")
-                        f.flush()
-                        games_completed += 1
-                        games_since_last_sync += 1
-                        print(f"[节点 {WORKER_ID}] 完成 {games_completed} 局: 顺位 {rank}, 得点 {score}")
-                    except Exception as e:
-                        print(f"对局异常: {e}")
-                    
-                    if EVAL_RUNNING:
-                        futures.add(executor.submit(play_one_game, games_completed))
-            
-            if games_since_last_sync >= 100:
-                sync_data_to_hub()
-                sync_data_from_hub()
-                games_since_last_sync = 0
-
-# ==========================================
-# 4. 前端 Gradio 实时展示面板 (全局汇总)
-# ==========================================
-def read_and_analyze():
-    all_files = glob.glob(f"{REPORT_FILE_PREFIX}_*.txt")
-    
-    main_arch = "NEW架构" if ONE_NEW_VS_TWO_LOCAL else "LOCAL架构"
-    opp_arch = "LOCAL架构" if ONE_NEW_VS_TWO_LOCAL else "NEW架构"
-    
-    if not all_files:
-        return f"⏳ 正在拉取模型并等待 [{main_arch}] `{TEST_MODEL}` VS [{opp_arch}] `{EXAMINER_MODEL}` 第一局完成...", None
-    
-    ranks, scores = [], []
-    try:
-        for file in all_files:
-            with open(file, "r") as f:
-                lines = f.readlines()
-                for line in lines:
-                    parts = line.strip().split()
-                    if len(parts) == 2:
-                        ranks.append(int(float(parts[0])))
-                        scores.append(float(parts[1]))
-        total = len(ranks)
-        if total == 0:
-            return f"⏳ 模型已就绪，正在进行第一局对抗...", None
-
-        avg_rank = sum(ranks) / total
-        avg_score = sum(scores) / total
-        rank1_rate = ranks.count(1) / total * 100
-        rank2_rate = ranks.count(2) / total * 100
-        rank3_rate = ranks.count(3) / total * 100
-
-        last_update = time.strftime('%Y-%m-%d %H:%M:%S')
-
-        md_text = f"""
-        ### 📊 对战简报
-        - ⚔️ **对抗阵容:** 1只 `{TEST_MODEL}` ({main_arch}) **VS** 2只 `{EXAMINER_MODEL}` ({opp_arch})
-        - 🧮 **总对局数:** {total} 局 (跨节点全局汇集)
-        - 🏆 **平均顺位:** {avg_rank:.3f}
-        - 💰 **平均得点:** {avg_score:.0f}
-        ---
-        - 🥇 **一位率:** {rank1_rate:.1f}%
-        - 🥈 **二位率:** {rank2_rate:.1f}%
-        - 🥉 **三位率:** {rank3_rate:.1f}%
-        ---
-        - 🌐 **当前节点 ID:** `{WORKER_ID}`
-        - 🕒 **刷新时间:** {last_update}
-        """
-
-        fig = plt.figure(figsize=(10, 4))
-        
-        ax1 = fig.add_subplot(121)
-        ax1.bar(['1st', '2nd', '3rd'], [rank1_rate, rank2_rate, rank3_rate], color=['#FFD700', '#C0C0C0', '#CD7F32'])
-        ax1.set_title(f'Rank Distribution for {TEST_MODEL}')
-        ax1.set_ylim(0, max(100, max([rank1_rate, rank2_rate, rank3_rate] + [0]) + 10))
-        for i, v in enumerate([rank1_rate, rank2_rate, rank3_rate]):
-            ax1.text(i, v + 2, f"{v:.1f}%", ha='center')
-
-        ax2 = fig.add_subplot(122)
-        df = pd.DataFrame({'score': scores})
-        df['ma'] = df['score'].rolling(window=min(10, max(1, len(df))), min_periods=1).mean()
-        ax2.plot(df['score'], alpha=0.3, color='gray', label='Raw Score')
-        ax2.plot(df['ma'], color='crimson', linewidth=2, label='Moving Avg (10)')
-        ax2.set_title('Score Trend')
-        ax2.legend()
-
-        plt.tight_layout()
-        return md_text, fig
-        
-    except Exception as e:
-        return f"❌ 数据解析出错: {e}", None
-
-# ==========================================
-# 5. 启动 Gradio 应用
-# ==========================================
-with gr.Blocks() as demo:
-    gr.Markdown("# 🀄 Mahjong AI 基准评估舱")
-    
-    header_main = "NEW架构" if ONE_NEW_VS_TWO_LOCAL else "LOCAL架构"
-    header_opp = "LOCAL架构" if ONE_NEW_VS_TWO_LOCAL else "NEW架构"
-    
-    gr.Markdown(f"当前正在评估: 1名 **{TEST_MODEL} ({header_main})** 单挑 2名 **{EXAMINER_MODEL} ({header_opp})**。启动时会自动拉取权重。")
-    
-    with gr.Row():
-        with gr.Column(scale=1):
-            stats_output = gr.Markdown("🚀 正在初始化基准环境并连接模型仓库...")
-            refresh_btn = gr.Button("🔄 手动刷新全局战绩")
-        with gr.Column(scale=2):
-            plot_output = gr.Plot()
-
-    demo.load(fn=read_and_analyze, inputs=None, outputs=[stats_output, plot_output])
-    
-    timer = gr.Timer(15)
-    timer.tick(fn=read_and_analyze, inputs=None, outputs=[stats_output, plot_output])
-    
-    refresh_btn.click(fn=read_and_analyze, inputs=None, outputs=[stats_output, plot_output])
-
-if __name__ == "__main__":
-    t = threading.Thread(target=background_eval_loop, daemon=True)
-    t.start()
-    
-    demo.queue().launch(server_name="0.0.0.0", server_port=7860, theme=gr.themes.Soft())
+import os
+import orjson
+import concurrent.futures
+import random
+import torch
+import threading
+import time
+import uuid
+import glob
+import gradio as gr
+import pandas as pd
+import matplotlib.pyplot as plt
+from huggingface_hub import snapshot_download, hf_hub_download, HfApi
+
+from riichienv import RiichiEnv, GameRule
+
+# 分别导入两个不同架构的加载函数，防止命名冲突
+from model3pLOCAL import load_model as load_model_local
+from model3pNEW import load_model as load_model_new
+
+# ==========================================
+# 0. 核心对抗配置开关 (在这里切换模式)
+# ==========================================
+# True: 1个 NEW架构(TEST_MODEL) VS 2个 LOCAL架构(EXAMINER_MODEL)
+# False: 1个 LOCAL架构(TEST_MODEL) VS 2个 NEW架构(EXAMINER_MODEL)
+ONE_NEW_VS_TWO_LOCAL = True  
+
+# ==========================================
+# 0. 分布式多开与云端持久化配置
+# ==========================================
+DATA_REPO_ID = "ffzeroHua/mj-eval-results"  # 📊 战绩数据集仓库
+MODEL_REPO_ID = "ffzeroHua/Riichi-Model-Repo" # 🧠 模型权重仓库
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+# 为当前节点生成唯一的 ID
+WORKER_ID = os.getenv("WORKER_ID", str(uuid.uuid4())[:6])
+
+# 根据开关状态自动调整保存的文件前缀
+BASE_REPORT_PREFIX = 'D57k_vs_9070_eval_report'
+if ONE_NEW_VS_TWO_LOCAL:
+    REPORT_FILE_PREFIX = BASE_REPORT_PREFIX
+else:
+    REPORT_FILE_PREFIX = f"inverse_{BASE_REPORT_PREFIX}"
+
+REPORT_FILE = f"{REPORT_FILE_PREFIX}_{WORKER_ID}.txt"
+
+api = HfApi()
+EVAL_RUNNING = True
+
+# 🚀 设定要从云端拉取并进行对抗的两个模型
+TEST_MODEL = "StudentSanma_Distilled_Step57000.pth"
+EXAMINER_MODEL = "Elite4z9070.pth"
+
+def sync_models_from_hub():
+    """启动时从指定的模型仓库拉取对战双方的权重文件"""
+    if HF_TOKEN and "你的用户名" not in MODEL_REPO_ID:
+        print(f"☁️ 正在从模型仓库 [{MODEL_REPO_ID}] 拉取评估模型...")
+        try:
+            hf_hub_download(repo_id=MODEL_REPO_ID, filename=TEST_MODEL, repo_type="model", local_dir=".", token=HF_TOKEN)
+            print(f"✅ 成功拉取测试模型: {TEST_MODEL}")
+            
+            hf_hub_download(repo_id=MODEL_REPO_ID, filename=EXAMINER_MODEL, repo_type="model", local_dir=".", token=HF_TOKEN)
+            print(f"✅ 成功拉取考官模型: {EXAMINER_MODEL}")
+            
+            print("🎉 模型环境准备完毕！")
+        except Exception as e:
+            print(f"❌ 拉取模型失败，请检查文件名或仓库权限: {e}")
+    else:
+        print("⚠️ 未配置有效 HF_TOKEN 或未修改 MODEL_REPO_ID，将尝试使用本地已存在的模型文件。")
+
+def sync_data_from_hub():
+    """启动时从数据集下载所有节点的战绩分片文件"""
+    if HF_TOKEN and "你的用户名" not in DATA_REPO_ID:
+        try:
+            print(f"🔄 正在从 Hub 拉取全局历史战绩数据 (前缀匹配: {REPORT_FILE_PREFIX})...")
+            snapshot_download(
+                repo_id=DATA_REPO_ID,
+                repo_type="dataset",
+                local_dir=".",
+                allow_patterns=REPORT_FILE_PREFIX + "_*.txt",
+                token=HF_TOKEN
+            )
+            print("✅ 历史数据拉取完成。")
+        except Exception as e:
+            print(f"⚠️ 拉取历史战绩失败: {e}")
+
+def sync_data_to_hub():
+    """将当前节点的战绩文件备份到数据集"""
+    if HF_TOKEN and "你的用户名" not in DATA_REPO_ID:
+        try:
+            api.upload_file(
+                path_or_fileobj=REPORT_FILE,
+                path_in_repo=REPORT_FILE,
+                repo_id=DATA_REPO_ID,
+                repo_type="dataset",
+                token=HF_TOKEN
+            )
+            print(f"☁️ 节点 {WORKER_ID} 战绩已同步至 Hub: {time.strftime('%H:%M:%S')}")
+        except Exception as e:
+            print(f"❌ 同步失败: {e}")
+
+# ==========================================
+# 1. 高频及模型加载逻辑
+# ==========================================
+def patch_event_fast(event_str):
+    if '"kita"' in event_str:
+        event_str = event_str.replace('"kita"', '"nukidora"')
+    
+    if '"start_kyoku"' in event_str or '"deltas"' in event_str:
+        event = orjson.loads(event_str)
+        if event.get('type') == 'start_kyoku':
+            scores = event.setdefault('scores', [])
+            while len(scores) < 4: scores.append(0)
+            tehais = event.setdefault('tehais', [])
+            while len(tehais) < 4: tehais.append(["?" for _ in range(13)])
+        if 'deltas' in event:
+            deltas = event['deltas']
+            while len(deltas) < 4: deltas.append(0)
+        return orjson.dumps(event).decode('utf-8')
+    return event_str
+
+def patch_resp_fast(resp_str):
+    if not resp_str: return resp_str
+    return resp_str.replace('"nukidora"', '"kita"')
+
+_MODEL_CACHE = {}
+
+def get_cached_model(player_id: int, model_file: str, arch_type: str):
+    """根据指定的架构类型 (new 或 local) 加载模型"""
+    key = (player_id, model_file, arch_type)
+    if key not in _MODEL_CACHE:
+        torch.set_num_threads(1) 
+        if arch_type == 'new':
+            _MODEL_CACHE[key] = load_model_new(player_id, model_file)
+        else:
+            _MODEL_CACHE[key] = load_model_local(player_id, model_file)
+    return _MODEL_CACHE[key]
+
+class MortalAgent:
+    def __init__(self, player_id: int, model_file: str, arch_type: str):
+        self.player_id = player_id
+        self.arch_type = arch_type
+        self.model = get_cached_model(player_id, model_file, arch_type)
+
+    def act(self, obs):
+        resp = None
+        for event in obs.new_events():
+            event_patched = patch_event_fast(event)
+            resp = patch_resp_fast(self.model.react(event_patched))
+        action = obs.select_action_from_mjai(resp)
+        assert action is not None, "Mortal must return a legal action"
+        return action
+
+# ==========================================
+# 2. 核心对局任务
+# ==========================================
+def play_one_game(game_index):
+    env = RiichiEnv(game_mode="3p-red-half", rule=GameRule.default_tenhou())
+    new_seat = random.randrange(3)
+    
+    agents = {}
+    for i in range(3):
+        if i == new_seat:
+            # 🚀 挑战者位
+            model_file = TEST_MODEL
+            arch = 'new' if ONE_NEW_VS_TWO_LOCAL else 'local'
+        else:
+            # 🚀 考官位
+            model_file = EXAMINER_MODEL
+            arch = 'local' if ONE_NEW_VS_TWO_LOCAL else 'new'
+            
+        agents[i] = MortalAgent(i, model_file, arch)
+    
+    obs_dict = env.reset()
+    while not env.done():
+        actions = {pid: agents[pid].act(obs) for pid, obs in obs_dict.items()}
+        obs_dict = env.step(actions)
+        
+    scores = env.scores()
+    ranks = env.ranks()
+    return ranks[new_seat], scores[new_seat]
+
+# ==========================================
+# 3. 后台独立评估线程
+# ==========================================
+def background_eval_loop():
+    sync_models_from_hub() # 🚀 启动时从 Riichi-Model-Repo 拉取对战模型
+    sync_data_from_hub()   # 🚀 启动时从战绩仓库拉取历史战绩
+    
+    NUM_WORKERS = 1 
+    
+    mode_str = "1只 NEW 挑战 2只 LOCAL" if ONE_NEW_VS_TWO_LOCAL else "1只 LOCAL 挑战 2只 NEW"
+    print(f"🚀 节点 [{WORKER_ID}] 后台对战线程已启动: 模式为 [{mode_str}]")
+    
+    if not os.path.exists(REPORT_FILE):
+        open(REPORT_FILE, 'w').close()
+
+    games_since_last_sync = 0
+
+    with concurrent.futures.ProcessPoolExecutor(max_workers=NUM_WORKERS) as executor:
+        futures = {executor.submit(play_one_game, i) for i in range(NUM_WORKERS * 2)}
+        games_completed = 0
+        
+        while EVAL_RUNNING and futures:
+            done, futures = concurrent.futures.wait(
+                futures, return_when=concurrent.futures.FIRST_COMPLETED
+            )
+            
+            with open(REPORT_FILE, "a") as f:
+                for future in done:
+                    try:
+                        rank, score = future.result()
+                        f.write(f"{rank} {score}\n")
+                        f.flush()
+                        games_completed += 1
+                        games_since_last_sync += 1
+                        print(f"[节点 {WORKER_ID}] 完成 {games_completed} 局: 顺位 {rank}, 得点 {score}")
+                    except Exception as e:
+                        print(f"对局异常: {e}")
+                    
+                    if EVAL_RUNNING:
+                        futures.add(executor.submit(play_one_game, games_completed))
+            
+            if games_since_last_sync >= 100:
+                sync_data_to_hub()
+                sync_data_from_hub()
+                games_since_last_sync = 0
+
+# ==========================================
+# 4. 前端 Gradio 实时展示面板 (全局汇总)
+# ==========================================
+def read_and_analyze():
+    all_files = glob.glob(f"{REPORT_FILE_PREFIX}_*.txt")
+    
+    main_arch = "NEW架构" if ONE_NEW_VS_TWO_LOCAL else "LOCAL架构"
+    opp_arch = "LOCAL架构" if ONE_NEW_VS_TWO_LOCAL else "NEW架构"
+    
+    if not all_files:
+        return f"⏳ 正在拉取模型并等待 [{main_arch}] `{TEST_MODEL}` VS [{opp_arch}] `{EXAMINER_MODEL}` 第一局完成...", None
+    
+    ranks, scores = [], []
+    try:
+        for file in all_files:
+            with open(file, "r") as f:
+                lines = f.readlines()
+                for line in lines:
+                    parts = line.strip().split()
+                    if len(parts) == 2:
+                        ranks.append(int(float(parts[0])))
+                        scores.append(float(parts[1]))
+        total = len(ranks)
+        if total == 0:
+            return f"⏳ 模型已就绪，正在进行第一局对抗...", None
+
+        avg_rank = sum(ranks) / total
+        avg_score = sum(scores) / total
+        rank1_rate = ranks.count(1) / total * 100
+        rank2_rate = ranks.count(2) / total * 100
+        rank3_rate = ranks.count(3) / total * 100
+
+        last_update = time.strftime('%Y-%m-%d %H:%M:%S')
+
+        md_text = f"""
+        ### 📊 对战简报
+        - ⚔️ **对抗阵容:** 1只 `{TEST_MODEL}` ({main_arch}) **VS** 2只 `{EXAMINER_MODEL}` ({opp_arch})
+        - 🧮 **总对局数:** {total} 局 (跨节点全局汇集)
+        - 🏆 **平均顺位:** {avg_rank:.3f}
+        - 💰 **平均得点:** {avg_score:.0f}
+        ---
+        - 🥇 **一位率:** {rank1_rate:.1f}%
+        - 🥈 **二位率:** {rank2_rate:.1f}%
+        - 🥉 **三位率:** {rank3_rate:.1f}%
+        ---
+        - 🌐 **当前节点 ID:** `{WORKER_ID}`
+        - 🕒 **刷新时间:** {last_update}
+        """
+
+        fig = plt.figure(figsize=(10, 4))
+        
+        ax1 = fig.add_subplot(121)
+        ax1.bar(['1st', '2nd', '3rd'], [rank1_rate, rank2_rate, rank3_rate], color=['#FFD700', '#C0C0C0', '#CD7F32'])
+        ax1.set_title(f'Rank Distribution for {TEST_MODEL}')
+        ax1.set_ylim(0, max(100, max([rank1_rate, rank2_rate, rank3_rate] + [0]) + 10))
+        for i, v in enumerate([rank1_rate, rank2_rate, rank3_rate]):
+            ax1.text(i, v + 2, f"{v:.1f}%", ha='center')
+
+        ax2 = fig.add_subplot(122)
+        df = pd.DataFrame({'score': scores})
+        df['ma'] = df['score'].rolling(window=min(10, max(1, len(df))), min_periods=1).mean()
+        ax2.plot(df['score'], alpha=0.3, color='gray', label='Raw Score')
+        ax2.plot(df['ma'], color='crimson', linewidth=2, label='Moving Avg (10)')
+        ax2.set_title('Score Trend')
+        ax2.legend()
+
+        plt.tight_layout()
+        return md_text, fig
+        
+    except Exception as e:
+        return f"❌ 数据解析出错: {e}", None
+
+# ==========================================
+# 5. 启动 Gradio 应用
+# ==========================================
+with gr.Blocks() as demo:
+    gr.Markdown("# 🀄 Mahjong AI 基准评估舱")
+    
+    header_main = "NEW架构" if ONE_NEW_VS_TWO_LOCAL else "LOCAL架构"
+    header_opp = "LOCAL架构" if ONE_NEW_VS_TWO_LOCAL else "NEW架构"
+    
+    gr.Markdown(f"当前正在评估: 1名 **{TEST_MODEL} ({header_main})** 单挑 2名 **{EXAMINER_MODEL} ({header_opp})**。启动时会自动拉取权重。")
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            stats_output = gr.Markdown("🚀 正在初始化基准环境并连接模型仓库...")
+            refresh_btn = gr.Button("🔄 手动刷新全局战绩")
+        with gr.Column(scale=2):
+            plot_output = gr.Plot()
+
+    demo.load(fn=read_and_analyze, inputs=None, outputs=[stats_output, plot_output])
+    
+    timer = gr.Timer(15)
+    timer.tick(fn=read_and_analyze, inputs=None, outputs=[stats_output, plot_output])
+    
+    refresh_btn.click(fn=read_and_analyze, inputs=None, outputs=[stats_output, plot_output])
+
+if __name__ == "__main__":
+    t = threading.Thread(target=background_eval_loop, daemon=True)
+    t.start()
+    
+    demo.queue().launch(server_name="0.0.0.0", server_port=7860, theme=gr.themes.Soft())