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.gitignore

@@ -1,155 +1,155 @@
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-cover/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-.pybuilder/
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-#   For a library or package, you might want to ignore these files since the code is
-#   intended to run in multiple environments; otherwise, check them in:
-# .python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# poetry
-#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/
-
-# pytype static type analyzer
-.pytype/
-
-# Cython debug symbols
-cython_debug/
-
-# PyCharm
-#  JetBrains specific template is maintainted in a separate JetBrains.gitignore that can
-#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
-#  and can be added to the global gitignore or merged into this file.  For a more nuclear
-#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
-
-#database
-*.csv
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintainted in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+#database
+*.csv

app.py

@@ -2,7 +2,6 @@
 import random
 import requests
 import io
-import gradio_i18n
 import gradio as gr
 import pandas as pd
 from PIL import Image

requirements.txt

@@ -2,4 +2,3 @@ gradio>=4.40.0
 jinja2>=3.1.2
 httpx
 pymongo
-gradio-i18n==0.0.10

useapi.py

@@ -1,172 +1,172 @@
-import asyncio
-import httpx
-import json
-import requests
-import math
-import os
-client = httpx.AsyncClient()
-# 请求URL
-recommand_base_url = "https://" + os.getenv("recommand_base_url")
-chat_url = "https://" + os.getenv("chat_url")
-model_url = "https://" + os.getenv("model_url")
-character_url = "https://" + os.getenv("character_url")
-avatar_url = "https://" + os.getenv("avatar_url")
-image_url = "https://" + os.getenv("image_url")
-auth = os.getenv("auth")
-#headers
-def create_headers(language):
-    # 映射
-    language_mapping = {
-        'Chinese': 'zh',
-        'English': 'en',
-        'Japanese': 'ja',
-        'Korean': 'ko'
-    }
-    
-    # 获取对应的语言代码，如果不存在则默认为 'zh'
-    language_code = language_mapping.get(language, 'zh')
-    
-    return {
-        'X-Refresh-Token': '',
-        'X-Language': language_code,
-        'accept-language': '',
-        'User-Agent': 'Apifox/1.0.0 (https://apifox.com)',
-        'Authorization': auth,
-        'Accept': '*/*',
-        'Connection': 'keep-alive'
-    }
-
-def recommand_character(language):
-    response = requests.get(character_url, headers=create_headers(language))
-    json_data = response.json()
-    characters = [{
-        "name": item["name"],
-        "_id": item["_id"],
-        "avatar_url": str(avatar_url + item['_id'] + "_avatar.webp")
-    } for item in json_data['data']]
-    return characters
-
-def id_to_avatar(char_id):
-    return str(avatar_url + char_id + "_avatar.webp")
-
-#GET模型列表
-def get_models():
-    class ModelStorage:
-        def __init__(self):
-            self.models = []
-
-        def add_models(self, models):
-            for model_info in models:
-                # 过滤掉 'gpt-4o' 和 'gpt-4o-mini'
-                if model_info['model'] not in ['gpt-4o', 'gpt-4o-mini', 'mythomax-13b']:
-                    if model_info['model'] in ['qwen-2-7b', 'gemma-2-9b', 'llama-3.1-8b', 'glm-4-9b']:
-                        weight = 12  # Assign a low weight to reduce their frequency
-                    else:
-                        weight = int(math.ceil(24 / model_info['price'] + 0.5))
-                    self.models.extend([model_info['model']] * weight)
-
-    model_storage = ModelStorage()
-
-    # 从指定的 URL 获取 JSON 数据
-    response = requests.get(model_url)
-
-    if response.status_code == 200:
-        data = response.json()
-        # 添加模型到 self.models
-        model_storage.add_models(data['data'])
-        return model_storage.models
-
-#解析推荐json
-def extract_recommand(data):
-    return [
-        {
-            "character_id": item["character_id"],
-            "avatar_url" : str(avatar_url+item["character_id"]+"_avatar.webp"),
-            "_id": item["_id"],
-            "image_url" : str(image_url+item["_id"]+"_large.webp"),
-            "description": item["description"],
-            "name": item["title"],
-            "opening": item["opening"]
-        }
-        for item in data["data"]["moments"]
-    ]
-
-#请求推荐API
-async def recommand(char_id, language):
-    recommand_url = str(recommand_base_url + char_id)
-    response = await client.get(recommand_url, headers=create_headers(language))
-    json_data = response.json()
-    return extract_recommand(json_data)
-
-async def fetch_stream(query, model, moment_id, session_id, bio, request_name, queue, language):
-    payload = {"query": query, "model": model, "bio": bio, "moment_id": moment_id}
-    if session_id:
-        payload["session_id"] = session_id
-    async with client.stream(
-        "POST", chat_url, json=payload, headers=create_headers(language)
-    ) as response:
-        # 获取并返回 header
-        if response.status_code != 200:
-            await queue.put((request_name, "content", "Error Occur!"))
-            await queue.put((request_name, "end", None))
-            return
-        response_headers = dict(response.headers)
-        session_id = response_headers.get("x-session-id")
-        await queue.put((request_name, "header", response_headers))
-
-        # 流式处理响应内容
-        async for chunk in response.aiter_bytes():
-            await queue.put((request_name, "content", chunk.decode()))
-
-    # 标记流结束
-    await queue.put((request_name, "end", None))
-
-    return session_id
-
-
-async def combine_streams(
-    query_a,
-    query_b,
-    model_a,
-    model_b,
-    moment_id_a,
-    moment_id_b,
-    session_id_a,
-    session_id_b,
-    bio_a,
-    bio_b,
-    language
-):
-    queue = asyncio.Queue()
-    task_a = asyncio.create_task(
-        fetch_stream(
-            query_a, model_a, moment_id_a, session_id_a, bio_a, "requestA", queue, language
-        )
-    )
-    task_b = asyncio.create_task(
-        fetch_stream(
-            query_b, model_b, moment_id_b, session_id_b, bio_b, "requestB", queue, language
-        )
-    )
-
-    headers = {}
-    content = {"requestA": "", "requestB": ""}
-    active_streams = 2
-
-    while active_streams > 0:
-        request_name, data_type, data = await queue.get()
-
-        if data_type == "header":
-            headers[f"{request_name}_header"] = data
-            if len(headers) == 2:
-                yield headers
-        elif data_type == "content":
-            content[request_name] = data.strip()
-            if content["requestA"] or content["requestB"]:
-                yield content
-                content = {"requestA": "", "requestB": ""}
-        elif data_type == "end":
-            active_streams -= 1
-
-    session_id_a = await task_a
+import asyncio
+import httpx
+import json
+import requests
+import math
+import os
+client = httpx.AsyncClient()
+# 请求URL
+recommand_base_url = "https://" + os.getenv("recommand_base_url")
+chat_url = "https://" + os.getenv("chat_url")
+model_url = "https://" + os.getenv("model_url")
+character_url = "https://" + os.getenv("character_url")
+avatar_url = "https://" + os.getenv("avatar_url")
+image_url = "https://" + os.getenv("image_url")
+auth = os.getenv("auth")
+#headers
+def create_headers(language):
+    # 映射
+    language_mapping = {
+        'Chinese': 'zh',
+        'English': 'en',
+        'Japanese': 'ja',
+        'Korean': 'ko'
+    }
+    
+    # 获取对应的语言代码，如果不存在则默认为 'zh'
+    language_code = language_mapping.get(language, 'zh')
+    
+    return {
+        'X-Refresh-Token': '',
+        'X-Language': language_code,
+        'accept-language': '',
+        'User-Agent': 'Apifox/1.0.0 (https://apifox.com)',
+        'Authorization': auth,
+        'Accept': '*/*',
+        'Connection': 'keep-alive'
+    }
+
+def recommand_character(language):
+    response = requests.get(character_url, headers=create_headers(language))
+    json_data = response.json()
+    characters = [{
+        "name": item["name"],
+        "_id": item["_id"],
+        "avatar_url": str(avatar_url + item['_id'] + "_avatar.webp")
+    } for item in json_data['data']]
+    return characters
+
+def id_to_avatar(char_id):
+    return str(avatar_url + char_id + "_avatar.webp")
+
+#GET模型列表
+def get_models():
+    class ModelStorage:
+        def __init__(self):
+            self.models = []
+
+        def add_models(self, models):
+            for model_info in models:
+                # 过滤掉 'gpt-4o' 和 'gpt-4o-mini'
+                if model_info['model'] not in ['gpt-4o', 'gpt-4o-mini', 'mythomax-13b']:
+                    if model_info['model'] in ['qwen-2-7b', 'gemma-2-9b', 'llama-3.1-8b', 'internLM-2.5-7b']:
+                        weight = 12  # Assign a low weight to reduce their frequency
+                    else:
+                        weight = int(math.ceil(24 / model_info['price'] + 0.5))
+                    self.models.extend([model_info['model']] * weight)
+
+    model_storage = ModelStorage()
+
+    # 从指定的 URL 获取 JSON 数据
+    response = requests.get(model_url)
+
+    if response.status_code == 200:
+        data = response.json()
+        # 添加模型到 self.models
+        model_storage.add_models(data['data'])
+        return model_storage.models
+
+#解析推荐json
+def extract_recommand(data):
+    return [
+        {
+            "character_id": item["character_id"],
+            "avatar_url" : str(avatar_url+item["character_id"]+"_avatar.webp"),
+            "_id": item["_id"],
+            "image_url" : str(image_url+item["_id"]+"_large.webp"),
+            "description": item["description"],
+            "name": item["title"],
+            "opening": item["opening"]
+        }
+        for item in data["data"]["moments"]
+    ]
+
+#请求推荐API
+async def recommand(char_id, language):
+    recommand_url = str(recommand_base_url + char_id)
+    response = await client.get(recommand_url, headers=create_headers(language))
+    json_data = response.json()
+    return extract_recommand(json_data)
+
+async def fetch_stream(query, model, moment_id, session_id, bio, request_name, queue, language):
+    payload = {"query": query, "model": model, "bio": bio, "moment_id": moment_id}
+    if session_id:
+        payload["session_id"] = session_id
+    async with client.stream(
+        "POST", chat_url, json=payload, headers=create_headers(language)
+    ) as response:
+        # 获取并返回 header
+        if response.status_code != 200:
+            await queue.put((request_name, "content", "Error Occur!"))
+            await queue.put((request_name, "end", None))
+            return
+        response_headers = dict(response.headers)
+        session_id = response_headers.get("x-session-id")
+        await queue.put((request_name, "header", response_headers))
+
+        # 流式处理响应内容
+        async for chunk in response.aiter_bytes():
+            await queue.put((request_name, "content", chunk.decode()))
+
+    # 标记流结束
+    await queue.put((request_name, "end", None))
+
+    return session_id
+
+
+async def combine_streams(
+    query_a,
+    query_b,
+    model_a,
+    model_b,
+    moment_id_a,
+    moment_id_b,
+    session_id_a,
+    session_id_b,
+    bio_a,
+    bio_b,
+    language
+):
+    queue = asyncio.Queue()
+    task_a = asyncio.create_task(
+        fetch_stream(
+            query_a, model_a, moment_id_a, session_id_a, bio_a, "requestA", queue, language
+        )
+    )
+    task_b = asyncio.create_task(
+        fetch_stream(
+            query_b, model_b, moment_id_b, session_id_b, bio_b, "requestB", queue, language
+        )
+    )
+
+    headers = {}
+    content = {"requestA": "", "requestB": ""}
+    active_streams = 2
+
+    while active_streams > 0:
+        request_name, data_type, data = await queue.get()
+
+        if data_type == "header":
+            headers[f"{request_name}_header"] = data
+            if len(headers) == 2:
+                yield headers
+        elif data_type == "content":
+            content[request_name] = data.strip()
+            if content["requestA"] or content["requestB"]:
+                yield content
+                content = {"requestA": "", "requestB": ""}
+        elif data_type == "end":
+            active_streams -= 1
+
+    session_id_a = await task_a
     session_id_b = await task_b

utils.py

@@ -1,101 +1,101 @@
-import time
-from pymongo import MongoClient
-import pandas as pd
-import math
-import os
-
-# MongoDB连接配置
-client = MongoClient(os.getenv("client_link"))
-db = client.get_database('roleplay')  
-collection = db.get_collection('model_stats')  
-
-def update_model_stats(model1_name, model2_name, winner, turn, anony, language):
-    # 获取当前时间戳
-    tstamp = time.time()
-    
-    # 插入数据到MongoDB
-    collection.insert_one({
-        "Model 1": model1_name,
-        "Model 2": model2_name,
-        "Winner": winner,
-        "Turn": turn,
-        "Anony": anony,
-        "Language": language,
-        "tstamp": tstamp
-    })
-
-def calculate_elo(winner_elo, loser_elo, k=30, outcome=1):
-    """
-    winner_elo: Elo score of the winner before the game
-    loser_elo: Elo score of the loser before the game
-    k: K-factor in Elo calculation
-    outcome: 1 if winner won, 0.5 if tie, 0 if loser won (inverted)
-    """
-    expected_win = 1 / (1 + math.pow(10, (loser_elo - winner_elo) / 400))
-    new_winner_elo = winner_elo + k * (outcome - expected_win)
-    return new_winner_elo
-
-def load_dataframe():
-    # 从MongoDB读取数据
-    cursor = collection.find({})
-    
-    # 将游标中的数据转换为DataFrame
-    data = pd.DataFrame(list(cursor))
-
-    # 创建模型名称的唯一列表
-    models = pd.unique(data[['Model 1', 'Model 2']].values.ravel('K'))
-
-    # 初始化结果字典
-    results = {'模型名称': [], '参赛次数': [], '胜利次数': [], 'ELO': []}
-    elo_dict = {model: 1000 for model in models}  # 初始化ELO分数为1000
-
-    for _, row in data.iterrows():
-        model1 = row['Model 1']
-        model2 = row['Model 2']
-        winner = row['Winner']
-
-        if winner == 'Model 1':
-            elo_dict[model1] = calculate_elo(elo_dict[model1], elo_dict[model2], outcome=1)
-            elo_dict[model2] = calculate_elo(elo_dict[model2], elo_dict[model1], outcome=0)
-        elif winner == 'Model 2':
-            elo_dict[model2] = calculate_elo(elo_dict[model2], elo_dict[model1], outcome=1)
-            elo_dict[model1] = calculate_elo(elo_dict[model1], elo_dict[model2], outcome=0)
-        elif winner == 'tie':
-            elo_dict[model1] = calculate_elo(elo_dict[model1], elo_dict[model2], outcome=0.8)
-            elo_dict[model2] = calculate_elo(elo_dict[model2], elo_dict[model1], outcome=0.8)
-        elif winner == 'bothbad':
-            elo_dict[model1] = calculate_elo(elo_dict[model1], elo_dict[model2], outcome=0.1)
-            elo_dict[model2] = calculate_elo(elo_dict[model2], elo_dict[model1], outcome=0.1)
-
-    for model in models:
-        count = data['Model 1'].value_counts().get(model, 0) + data['Model 2'].value_counts().get(model, 0)
-        win_count = 0
-        win_count += len(data[(data['Winner'] == 'Model 1') & (data['Model 1'] == model)])
-        win_count += len(data[(data['Winner'] == 'Model 2') & (data['Model 2'] == model)])
-        win_count += len(data[(data['Winner'] == 'tie') & ((data['Model 1'] == model) | (data['Model 2'] == model))])
-        results['模型名称'].append(model)
-        results['参赛次数'].append(count)
-        results['胜利次数'].append(win_count)
-        results['ELO'].append(round(elo_dict[model]))
-
-    # 将结果字典转换为DataFrame
-    result_df = pd.DataFrame(results)
-
-    # 计算胜率并排序
-    result_df["模型胜率"] = (result_df['胜利次数'] / result_df['参赛次数']) * 100
-    result_df = result_df.sort_values(by="模型胜率", ascending=False)
-    result_df["模型胜率"] = result_df["模型胜率"].map("{:.2f}%".format)
-    
-    return result_df
-
-def change_name(old,new):
-    collection.update_many(
-        { "Model 1": old },
-        { "$set": { "Model 1": new } }
-    )
-
-    # 更新 Model 2 字段
-    collection.update_many(
-        { "Model 2": old },
-        { "$set": { "Model 2": new } }
+import time
+from pymongo import MongoClient
+import pandas as pd
+import math
+import os
+
+# MongoDB连接配置
+client = MongoClient(os.getenv("client_link"))
+db = client.get_database('roleplay')  
+collection = db.get_collection('model_stats')  
+
+def update_model_stats(model1_name, model2_name, winner, turn, anony, language):
+    # 获取当前时间戳
+    tstamp = time.time()
+    
+    # 插入数据到MongoDB
+    collection.insert_one({
+        "Model 1": model1_name,
+        "Model 2": model2_name,
+        "Winner": winner,
+        "Turn": turn,
+        "Anony": anony,
+        "Language": language,
+        "tstamp": tstamp
+    })
+
+def calculate_elo(winner_elo, loser_elo, k=30, outcome=1):
+    """
+    winner_elo: Elo score of the winner before the game
+    loser_elo: Elo score of the loser before the game
+    k: K-factor in Elo calculation
+    outcome: 1 if winner won, 0.5 if tie, 0 if loser won (inverted)
+    """
+    expected_win = 1 / (1 + math.pow(10, (loser_elo - winner_elo) / 400))
+    new_winner_elo = winner_elo + k * (outcome - expected_win)
+    return new_winner_elo
+
+def load_dataframe():
+    # 从MongoDB读取数据
+    cursor = collection.find({})
+    
+    # 将游标中的数据转换为DataFrame
+    data = pd.DataFrame(list(cursor))
+
+    # 创建模型名称的唯一列表
+    models = pd.unique(data[['Model 1', 'Model 2']].values.ravel('K'))
+
+    # 初始化结果字典
+    results = {'模型名称': [], '参赛次数': [], '胜利次数': [], 'ELO': []}
+    elo_dict = {model: 1000 for model in models}  # 初始化ELO分数为1000
+
+    for _, row in data.iterrows():
+        model1 = row['Model 1']
+        model2 = row['Model 2']
+        winner = row['Winner']
+
+        if winner == 'Model 1':
+            elo_dict[model1] = calculate_elo(elo_dict[model1], elo_dict[model2], outcome=1)
+            elo_dict[model2] = calculate_elo(elo_dict[model2], elo_dict[model1], outcome=0)
+        elif winner == 'Model 2':
+            elo_dict[model2] = calculate_elo(elo_dict[model2], elo_dict[model1], outcome=1)
+            elo_dict[model1] = calculate_elo(elo_dict[model1], elo_dict[model2], outcome=0)
+        elif winner == 'tie':
+            elo_dict[model1] = calculate_elo(elo_dict[model1], elo_dict[model2], outcome=0.8)
+            elo_dict[model2] = calculate_elo(elo_dict[model2], elo_dict[model1], outcome=0.8)
+        elif winner == 'bothbad':
+            elo_dict[model1] = calculate_elo(elo_dict[model1], elo_dict[model2], outcome=0.1)
+            elo_dict[model2] = calculate_elo(elo_dict[model2], elo_dict[model1], outcome=0.1)
+
+    for model in models:
+        count = data['Model 1'].value_counts().get(model, 0) + data['Model 2'].value_counts().get(model, 0)
+        win_count = 0
+        win_count += len(data[(data['Winner'] == 'Model 1') & (data['Model 1'] == model)])
+        win_count += len(data[(data['Winner'] == 'Model 2') & (data['Model 2'] == model)])
+        win_count += len(data[(data['Winner'] == 'tie') & ((data['Model 1'] == model) | (data['Model 2'] == model))])
+        results['模型名称'].append(model)
+        results['参赛次数'].append(count)
+        results['胜利次数'].append(win_count)
+        results['ELO'].append(round(elo_dict[model]))
+
+    # 将结果字典转换为DataFrame
+    result_df = pd.DataFrame(results)
+
+    # 计算胜率并排序
+    result_df["模型胜率"] = (result_df['胜利次数'] / result_df['参赛次数']) * 100
+    result_df = result_df.sort_values(by="模型胜率", ascending=False)
+    result_df["模型胜率"] = result_df["模型胜率"].map("{:.2f}%".format)
+    
+    return result_df
+
+def change_name(old,new):
+    collection.update_many(
+        { "Model 1": old },
+        { "$set": { "Model 1": new } }
+    )
+
+    # 更新 Model 2 字段
+    collection.update_many(
+        { "Model 2": old },
+        { "$set": { "Model 2": new } }
     )