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CakeCN
2024-12-04 23:24:09 +08:00
parent 4198ca63b1
commit 202b8ea4e2
229 changed files with 1 additions and 11180 deletions
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7
Html/runs/run_20241114-163533_2h3jhw/.config
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{
"project": "CCpiuC",
"name": "2h3jhw",
"run_id": "run_20241114-163533_2h3jhw",
"timestamp": "2024-11-14 16:35:33",
"pid": 24464
}

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Html/runs/run_20241114-163533_2h3jhw/agentscope.db
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Html/runs/run_20241114-163533_2h3jhw/code/app.py
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from flask import Flask, session, request, jsonify, render_template, send_from_directory
from flask_cors import CORS
from flask_socketio import SocketIO, join_room, emit
import markdown
import os
import uuid
import shutil
import sys
import json
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.insert(0, parent_dir)
from AlgoriAgent.projects.algoriAgent.agent_manager import AgentManager
app = Flask(__name__)
app.secret_key = 'cakebaker'
# 配置 CORS
CORS(app, resources={r"/*": {"origins": "http://localhost:9888"}}, supports_credentials=True)
'''
Backboard
'''
class Backboard:
pass
@app.route('/backboard')
def backboard():
return jsonify({'status': 'success'})
'''
Agent and Chat
'''
socketio = SocketIO(app, cors_allowed_origins="*")
agent_manager = AgentManager()
def realtime_response(config, realtime_action):
print("config"+str(config))
print("realtime_action"+str(realtime_action))
pass
@socketio.on('connect')
def handle_connect():
user_id = session.get('user_id')
if (user_id is None):
print("No user_id in session")
return jsonify({'error': 'Connection failed bacause no user_id found'}), 401
print(f'User connected with session user_id: {user_id}')
id, agent = agent_manager.get_agent(user_id)
if user_id:
join_room(user_id) # 将该用户加入以user_id为名的room
# 当接收到前端的消息时，触发此事件
@socketio.on('send_message')
def handle_message(data):
print(f"Message from client: {data}")
id = session.get('user_id')
# 构造回复消息
res = agent_manager.invoke(id, data)
reply = f"AI: {res.content['speak']}"
# 将回复发送回前端
emit('receive_message', reply)
'''
Markdown to HTML
'''
# 配置文件路径
MARKDOWN_DIR = 'books/markdown' # 存放 markdown 文件的文件夹
STATIC_DIR = 'static' # 存放生成的 HTML 和图片资源的文件夹
IMAGE_DIR = 'image' # 图片资源相对于 markdown 的位置
@app.route('/<filename>-markdown', methods=['GET'])
def convert_md(filename):
md_file_path = os.path.join(MARKDOWN_DIR, f'{filename}.md')
if not os.path.exists(md_file_path):
return jsonify({'error': 'Markdown file not found'}), 404
# 读取 markdown 文件内容
with open(md_file_path, 'r', encoding='utf-8') as file:
md_content = file.read()
# 将 markdown 转换为 HTML
html_content = markdown.markdown(md_content)
# 插入 CSS 样式，限制图片最大宽度为 100%，高度自动调整
html_with_styles = f"""
<html>
<head>
<style>
img {{
max-width: 100%;
height: auto;
}}
</style>
</head>
<body>
{html_content}
</body>
</html>
"""
# 保存 HTML 文件到 static 文件夹
html_output_path = os.path.join(STATIC_DIR, f'{filename}.html')
with open(html_output_path, 'w', encoding='utf-8') as html_file:
html_file.write(html_with_styles)
# 处理图片资源，将 image/xxx 文件夹拷贝到 static/image/xxx
image_source_dir = os.path.join(MARKDOWN_DIR, IMAGE_DIR, filename)
image_target_dir = os.path.join(STATIC_DIR, IMAGE_DIR, filename)
print(f"Copying image resources from {image_source_dir} to {image_target_dir}")
if os.path.exists(image_source_dir):
# 确保 static/image/xxx 目录存在
if not os.path.exists(image_target_dir):
os.makedirs(image_target_dir)
# 拷贝图片资源
for image_file in os.listdir(image_source_dir):
full_image_file_path = os.path.join(image_source_dir, image_file)
if os.path.isfile(full_image_file_path):
shutil.copy(full_image_file_path, image_target_dir)
return jsonify({'html_url': f'/static/{filename}.html'})
# 静态文件（HTML 和图片资源）的访问
@app.route('/static/<path:filename>')
def serve_static(filename):
return send_from_directory(STATIC_DIR, filename)
'''
Vscode
'''
@app.route('/desktop/<user_id>/<path>')
def hello_world(user_id, path):
print("session"+str(session))
if 'user_id' not in session:
session['user_id'] = 'user_' + str(uuid.uuid4())
# 在上层 目录下创建一个名为 user_id_path 的文件夹
path_dir = os.path.join(parent_dir, user_id, path)
os.makedirs(path_dir, exist_ok=True)
# 在此文件夹内部创建一个.config文件，并写入 user_id=user_id\n path=path
config_path = os.path.join(path_dir, '.config')
with open(config_path, 'w') as f:
f.write(f"user_id={user_id}\npath={path}")
return render_template('index.html', user_id=user_id, path=path)
@app.route('/vscode_data', methods=['POST'])
def vscode_data():
data = request.json
config = data['config']
realtime_response(config,data)
print(f"Received data from VSCode: {data}")
return jsonify({"status": "success", "received": data})
@app.route('/get_session')
def get_session():
user_session = session.get('user_id', 'default_session')
return jsonify({"session": user_session})
if __name__ == '__main__':
socketio.run(app, host='0.0.0.0', port=5500)

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Html/runs/run_20241114-163533_2h3jhw/code/backboardManager.py
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import time
class BackBoardManager:
def __init__(self):
self.backboards = {} # user_id: Backboard
def add_backboard(self, user_id, folder):
self.backboards[user_id] = Backboard(user_id, folder)
def get_backboard(self, user_id):
if user_id not in self.backboards:
return None
return self.backboards[user_id]
class Backboard:
def __init__(self, user_id, folder):
self.user_id = user_id
self.folder = folder
self.create_time = time.time()
self.enter_chapter_time = time.time()
def next_chapter(self):
self.enter_chapter_time = time.time()
def get_info_prompt(self):
return f"###Global Info:###\n\
User is {self.user_id}\n\
User's total study time is {self.get_deltatime_mmss(self.create_time, time.time())}\n\
User's current chapter study time is {self.get_deltatime_mmss(self.enter_chapter_time, time.time())}\n\
\n\n\
"
def get_deltatime_mmss(self, start_time, end_time):
elapsed_time = end_time - start_time
hours = int(elapsed_time // 3600)
minutes = int((elapsed_time % 3600) // 60)
seconds = int(elapsed_time % 60)
return f"{hours:02d}:{minutes:02d}:{seconds:02d}"

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Html/runs/run_20241114-163533_2h3jhw/logging.chat
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Html/runs/run_20241114-163533_2h3jhw/logging.log
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2024-11-14 16:35:40.507 | INFO | agentscope.models:read_model_configs:186 - Load configs for model wrapper: openai_cfg

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Html/runs/run_20241114-164343_jmd8td/.config
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{
"project": "7rGHWe",
"name": "jmd8td",
"run_id": "run_20241114-164343_jmd8td",
"timestamp": "2024-11-14 16:43:43",
"pid": 28598
}

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Html/runs/run_20241114-164343_jmd8td/code/app.py
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from flask import Flask, session, request, jsonify, render_template, send_from_directory
from flask_cors import CORS
from flask_socketio import SocketIO, join_room, emit
import markdown
import os
import uuid
import shutil
import sys
import json
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.insert(0, parent_dir)
from AlgoriAgent.projects.algoriAgent.agent_manager import AgentManager
app = Flask(__name__)
app.secret_key = 'cakebaker'
# 配置 CORS
CORS(app, resources={r"/*": {"origins": "http://localhost:9888"}}, supports_credentials=True)
'''
Backboard
'''
class Backboard:
pass
@app.route('/backboard')
def backboard():
return jsonify({'status': 'success'})
'''
Agent and Chat
'''
socketio = SocketIO(app, cors_allowed_origins="*")
agent_manager = AgentManager()
def realtime_response(config, realtime_action):
print("config"+str(config))
print("realtime_action"+str(realtime_action))
pass
@socketio.on('connect')
def handle_connect():
user_id = session.get('user_id')
if (user_id is None):
print("No user_id in session")
return jsonify({'error': 'Connection failed bacause no user_id found'}), 401
print(f'User connected with session user_id: {user_id}')
id, agent = agent_manager.get_agent(user_id)
if user_id:
join_room(user_id) # 将该用户加入以user_id为名的room
# 当接收到前端的消息时，触发此事件
@socketio.on('send_message')
def handle_message(data):
print(f"Message from client: {data}")
id = session.get('user_id')
# 构造回复消息
res = agent_manager.invoke(id, data)
reply = f"AI: {res.content['speak']}"
# 将回复发送回前端
emit('receive_message', reply)
'''
Markdown to HTML
'''
# 配置文件路径
MARKDOWN_DIR = 'books/markdown' # 存放 markdown 文件的文件夹
STATIC_DIR = 'static' # 存放生成的 HTML 和图片资源的文件夹
IMAGE_DIR = 'image' # 图片资源相对于 markdown 的位置
@app.route('/<filename>-markdown', methods=['GET'])
def convert_md(filename):
md_file_path = os.path.join(MARKDOWN_DIR, f'{filename}.md')
if not os.path.exists(md_file_path):
return jsonify({'error': 'Markdown file not found'}), 404
# 读取 markdown 文件内容
with open(md_file_path, 'r', encoding='utf-8') as file:
md_content = file.read()
# 将 markdown 转换为 HTML
html_content = markdown.markdown(md_content)
# 插入 CSS 样式，限制图片最大宽度为 100%，高度自动调整
html_with_styles = f"""
<html>
<head>
<style>
img {{
max-width: 100%;
height: auto;
}}
</style>
</head>
<body>
{html_content}
</body>
</html>
"""
# 保存 HTML 文件到 static 文件夹
html_output_path = os.path.join(STATIC_DIR, f'{filename}.html')
with open(html_output_path, 'w', encoding='utf-8') as html_file:
html_file.write(html_with_styles)
# 处理图片资源，将 image/xxx 文件夹拷贝到 static/image/xxx
image_source_dir = os.path.join(MARKDOWN_DIR, IMAGE_DIR, filename)
image_target_dir = os.path.join(STATIC_DIR, IMAGE_DIR, filename)
print(f"Copying image resources from {image_source_dir} to {image_target_dir}")
if os.path.exists(image_source_dir):
# 确保 static/image/xxx 目录存在
if not os.path.exists(image_target_dir):
os.makedirs(image_target_dir)
# 拷贝图片资源
for image_file in os.listdir(image_source_dir):
full_image_file_path = os.path.join(image_source_dir, image_file)
if os.path.isfile(full_image_file_path):
shutil.copy(full_image_file_path, image_target_dir)
return jsonify({'html_url': f'/static/{filename}.html'})
# 静态文件（HTML 和图片资源）的访问
@app.route('/static/<path:filename>')
def serve_static(filename):
return send_from_directory(STATIC_DIR, filename)
'''
Vscode
'''
@app.route('/desktop/<user_id>/<path>')
def hello_world(user_id, path):
if 'user_id' not in session:
session['user_id'] = 'user_' + str(uuid.uuid4())
# 在上层 目录下创建一个名为 user_id_path 的文件夹
path_dir = os.path.join(parent_dir, user_id, path)
os.makedirs(path_dir, exist_ok=True)
# 在此文件夹内部创建一个.config文件，并写入 user_id=user_id\n path=path
config_path = os.path.join(path_dir, '.config')
with open(config_path, 'w') as f:
f.write(f"user_id={user_id}\npath={path}")
return render_template('index.html', user_id=user_id, path=path)
@app.route('/vscode_data', methods=['POST'])
def vscode_data():
data = request.json
config = data['config']
realtime_response(config,data)
print(f"Received data from VSCode: {data}")
return jsonify({"status": "success", "received": data})
@app.route('/get_session')
def get_session():
user_session = session.get('user_id', 'default_session')
return jsonify({"session": user_session})
if __name__ == '__main__':
socketio.run(app, host='0.0.0.0', port=5500)

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Html/runs/run_20241114-164343_jmd8td/code/backboardManager.py
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import time
class BackBoardManager:
def __init__(self):
self.backboards = {} # user_id: Backboard
def add_backboard(self, user_id, folder):
self.backboards[user_id] = Backboard(user_id, folder)
def get_backboard(self, user_id):
if user_id not in self.backboards:
return None
return self.backboards[user_id]
class Backboard:
def __init__(self, user_id, folder):
self.user_id = user_id
self.folder = folder
self.create_time = time.time()
self.enter_chapter_time = time.time()
def next_chapter(self):
self.enter_chapter_time = time.time()
def get_info_prompt(self):
return f"###Global Info:###\n\
User is {self.user_id}\n\
User's total study time is {self.get_deltatime_mmss(self.create_time, time.time())}\n\
User's current chapter study time is {self.get_deltatime_mmss(self.enter_chapter_time, time.time())}\n\
\n\n\
"
def get_deltatime_mmss(self, start_time, end_time):
elapsed_time = end_time - start_time
hours = int(elapsed_time // 3600)
minutes = int((elapsed_time % 3600) // 60)
seconds = int(elapsed_time % 60)
return f"{hours:02d}:{minutes:02d}:{seconds:02d}"

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Html/runs/run_20241114-164343_jmd8td/logging.chat
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2024-11-14 16:43:59.414 | INFO | agentscope.models:read_model_configs:186 - Load configs for model wrapper: openai_cfg

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Html/runs/run_20241114-214752_wb6exw/.config
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{
"project": "28cXo4",
"name": "wb6exw",
"run_id": "run_20241114-214752_wb6exw",
"timestamp": "2024-11-14 21:47:52",
"pid": 15325
}

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from flask import Flask, session, request, jsonify, render_template, send_from_directory
from flask_cors import CORS
from flask_socketio import SocketIO, join_room, emit
import markdown
import os
import uuid
import shutil
import sys
import json
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.insert(0, parent_dir)
current_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '.'))
sys.path.insert(0, current_dir)
from AlgoriAgent.projects.algoriAgent.agent_manager import AgentManager
app = Flask(__name__)
app.secret_key = 'cakebaker'
# 配置 CORS
CORS(app, resources={r"/*": {"origins": "http://localhost:9888"}}, supports_credentials=True)
userid_recorder = {} # user_id&path -> session['user_id']
'''
Backboard
'''
from backboardManager import BackBoardManager
backboard_manager = BackBoardManager()
@app.route('/backboard')
def backboard():
return jsonify({'status': 'success'})
'''
Agent and Chat
'''
socketio = SocketIO(app, cors_allowed_origins="*")
agent_manager = AgentManager()
def realtime_response(config, realtime_action):
print("config"+str(config))
print("realtime_action"+str(realtime_action))
pass
@socketio.on('connect')
def handle_connect():
user = request.args.get('username')
folder_name = request.args.get('folder')
user_id = userid_recorder[user+'&'+folder_name]
session['user_id'] = user_id
print(f'User connected with session user_id: {user_id}')
# 从markdown、markdown_prompts、score_prompts中各读取数据folder.md并进行new_agent
with open(f'books/markdown/{folder_name}.md','r') as fmd, open(f'books/markdown_prompts/{folder_name}.md','r')as fmdp, open(f'books/score_prompts/{folder_name}.md','r') as fsp:
markdown = fmd.read()
markdown_prompts = fmdp.read()
score_prompts = fsp.read()
id, agent = agent_manager.new_agent(markdown, markdown_prompts, score_prompts, id=user_id)
join_room(id) # 将该用户加入以user_id为名的room
# 当接收到前端的消息时，触发此事件
@socketio.on('send_message')
def handle_message(data):
print(f"Message from client: {data}")
id = session.get('user_id')
# 构造回复消息
res = agent_manager.invoke(id, data)
reply = f"AI: {res.content['speak']}"
# 将回复发送回前端
emit('receive_message', reply)
'''
Markdown to HTML
'''
# 配置文件路径
MARKDOWN_DIR = 'books/markdown' # 存放 markdown 文件的文件夹
STATIC_DIR = 'static' # 存放生成的 HTML 和图片资源的文件夹
IMAGE_DIR = 'image' # 图片资源相对于 markdown 的位置
@app.route('/<filename>-markdown', methods=['GET'])
def convert_md(filename):
md_file_path = os.path.join(MARKDOWN_DIR, f'{filename}.md')
if not os.path.exists(md_file_path):
return jsonify({'error': 'Markdown file not found'}), 404
# 读取 markdown 文件内容
with open(md_file_path, 'r', encoding='utf-8') as file:
md_content = file.read()
# 将 markdown 转换为 HTML
html_content = markdown.markdown(md_content)
# 插入 CSS 样式，限制图片最大宽度为 100%，高度自动调整
html_with_styles = f"""
<html>
<head>
<style>
img {{
max-width: 100%;
height: auto;
}}
</style>
</head>
<body>
{html_content}
</body>
</html>
"""
# 保存 HTML 文件到 static 文件夹
html_output_path = os.path.join(STATIC_DIR, f'{filename}.html')
with open(html_output_path, 'w', encoding='utf-8') as html_file:
html_file.write(html_with_styles)
# 处理图片资源，将 image/xxx 文件夹拷贝到 static/image/xxx
image_source_dir = os.path.join(MARKDOWN_DIR, IMAGE_DIR, filename)
image_target_dir = os.path.join(STATIC_DIR, IMAGE_DIR, filename)
print(f"Copying image resources from {image_source_dir} to {image_target_dir}")
if os.path.exists(image_source_dir):
# 确保 static/image/xxx 目录存在
if not os.path.exists(image_target_dir):
os.makedirs(image_target_dir)
# 拷贝图片资源
for image_file in os.listdir(image_source_dir):
full_image_file_path = os.path.join(image_source_dir, image_file)
if os.path.isfile(full_image_file_path):
shutil.copy(full_image_file_path, image_target_dir)
return jsonify({'html_url': f'/static/{filename}.html'})
# 静态文件（HTML 和图片资源）的访问
@app.route('/static/<path:filename>')
def serve_static(filename):
return send_from_directory(STATIC_DIR, filename)
'''
Vscode
'''
@app.route('/desktop/<user_id>/<path>')
def hello_world(user_id, path):
if 'user_id' not in session:
session['user_id'] = 'user_' + str(uuid.uuid4())
userid_recorder[user_id+'&'+path] = session['user_id']
# 在上层 目录下创建一个名为 user_id_path 的文件夹
path_dir = os.path.join(parent_dir, user_id, path)
os.makedirs(path_dir, exist_ok=True)
# 在此文件夹内部创建一个.config文件，并写入 user_id=user_id\n path=path
config_path = os.path.join(path_dir, '.config')
with open(config_path, 'w') as f:
f.write(f"user_id={user_id}\npath={path}")
return render_template('index.html', user_id=user_id, path=path)
@app.route('/vscode_data', methods=['POST'])
def vscode_data():
data = request.json
config = data['config']
realtime_response(config,data)
print(f"Received data from VSCode: {data}")
return jsonify({"status": "success", "received": data})
@app.route('/get_session')
def get_session():
user_session = session.get('user_id', 'default_session')
return jsonify({"session": user_session})
if __name__ == '__main__':
socketio.run(app, host='0.0.0.0', port=5500)

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Html/runs/run_20241114-214752_wb6exw/code/backboardManager.py
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import time
class BackBoardManager:
def __init__(self):
self.backboards = {} # user_id: Backboard
def add_backboard(self, user_id, folder):
self.backboards[user_id] = Backboard(user_id, folder)
def get_backboard(self, user_id):
if user_id not in self.backboards:
return None
return self.backboards[user_id]
class Backboard:
def __init__(self, user_id, folder):
self.user_id = user_id
self.folder = folder
self.create_time = time.time()
self.enter_chapter_time = time.time()
def next_chapter(self):
self.enter_chapter_time = time.time()
def get_info_prompt(self):
return f"###Global Info:###\n\
User is {self.user_id}\n\
User's total study time is {self.get_deltatime_mmss(self.create_time, time.time())}\n\
User's current chapter study time is {self.get_deltatime_mmss(self.enter_chapter_time, time.time())}\n\
\n\n\
"
def get_deltatime_mmss(self, start_time, end_time):
elapsed_time = end_time - start_time
hours = int(elapsed_time // 3600)
minutes = int((elapsed_time % 3600) // 60)
seconds = int(elapsed_time % 60)
return f"{hours:02d}:{minutes:02d}:{seconds:02d}"

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2024-11-14 21:48:13.935 | INFO | agentscope.models:read_model_configs:186 - Load configs for model wrapper: openai_cfg

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{
"project": "JOBFuf",
"name": "mgm60l",
"run_id": "run_20241114-214935_mgm60l",
"timestamp": "2024-11-14 21:49:35",
"pid": 16725
}

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from flask import Flask, session, request, jsonify, render_template, send_from_directory
from flask_cors import CORS
from flask_socketio import SocketIO, join_room, emit
import markdown
import os
import uuid
import shutil
import sys
import json
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.insert(0, parent_dir)
current_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '.'))
sys.path.insert(0, current_dir)
from AlgoriAgent.projects.algoriAgent.agent_manager import AgentManager
app = Flask(__name__)
app.secret_key = 'cakebaker'
# 配置 CORS
CORS(app, resources={r"/*": {"origins": "http://localhost:9888"}}, supports_credentials=True)
userid_recorder = {} # user_id&path -> session['user_id']
'''
Backboard
'''
from backboardManager import BackBoardManager
backboard_manager = BackBoardManager()
@app.route('/backboard')
def backboard():
return jsonify({'status': 'success'})
'''
Agent and Chat
'''
socketio = SocketIO(app, cors_allowed_origins="*")
agent_manager = AgentManager()
def realtime_response(config, realtime_action):
print("config"+str(config))
print("realtime_action"+str(realtime_action))
pass
@socketio.on('connect')
def handle_connect():
user = request.args.get('username')
folder_name = request.args.get('folder')
user_id = userid_recorder[user+'&'+folder_name]
session['user_id'] = user_id
print(f'User connected with session user_id: {user_id}')
# 从markdown、markdown_prompts、score_prompts中各读取数据folder.md并进行new_agent
with open(f'books/markdown/{folder_name}.md','r') as fmd, open(f'books/markdown_prompts/{folder_name}.md','r')as fmdp, open(f'books/score_prompts/{folder_name}.md','r') as fsp:
markdown = fmd.read()
markdown_prompts = fmdp.read()
score_prompts = fsp.read()
id, agent = agent_manager.new_agent(markdown, markdown_prompts, score_prompts, id=user_id)
join_room(id) # 将该用户加入以user_id为名的room
# 当接收到前端的消息时，触发此事件
@socketio.on('send_message')
def handle_message(data):
print(f"Message from client: {data}")
id = session.get('user_id')
# 构造回复消息
res = agent_manager.invoke(id, data)
reply = f"AI: {res.content['speak']}"
# 将回复发送回前端
emit('receive_message', reply)
'''
Markdown to HTML
'''
# 配置文件路径
MARKDOWN_DIR = 'books/markdown' # 存放 markdown 文件的文件夹
STATIC_DIR = 'static' # 存放生成的 HTML 和图片资源的文件夹
IMAGE_DIR = 'image' # 图片资源相对于 markdown 的位置
@app.route('/<filename>-markdown', methods=['GET'])
def convert_md(filename):
md_file_path = os.path.join(MARKDOWN_DIR, f'{filename}.md')
if not os.path.exists(md_file_path):
return jsonify({'error': 'Markdown file not found'}), 404
# 读取 markdown 文件内容
with open(md_file_path, 'r', encoding='utf-8') as file:
md_content = file.read()
# 将 markdown 转换为 HTML
html_content = markdown.markdown(md_content)
# 插入 CSS 样式，限制图片最大宽度为 100%，高度自动调整
html_with_styles = f"""
<html>
<head>
<style>
img {{
max-width: 100%;
height: auto;
}}
</style>
</head>
<body>
{html_content}
</body>
</html>
"""
# 保存 HTML 文件到 static 文件夹
html_output_path = os.path.join(STATIC_DIR, f'{filename}.html')
with open(html_output_path, 'w', encoding='utf-8') as html_file:
html_file.write(html_with_styles)
# 处理图片资源，将 image/xxx 文件夹拷贝到 static/image/xxx
image_source_dir = os.path.join(MARKDOWN_DIR, IMAGE_DIR, filename)
image_target_dir = os.path.join(STATIC_DIR, IMAGE_DIR, filename)
print(f"Copying image resources from {image_source_dir} to {image_target_dir}")
if os.path.exists(image_source_dir):
# 确保 static/image/xxx 目录存在
if not os.path.exists(image_target_dir):
os.makedirs(image_target_dir)
# 拷贝图片资源
for image_file in os.listdir(image_source_dir):
full_image_file_path = os.path.join(image_source_dir, image_file)
if os.path.isfile(full_image_file_path):
shutil.copy(full_image_file_path, image_target_dir)
return jsonify({'html_url': f'/static/{filename}.html'})
# 静态文件（HTML 和图片资源）的访问
@app.route('/static/<path:filename>')
def serve_static(filename):
return send_from_directory(STATIC_DIR, filename)
'''
Vscode
'''
@app.route('/desktop/<user_id>/<path>')
def hello_world(user_id, path):
if 'user_id' not in session:
session['user_id'] = 'user_' + str(uuid.uuid4())
userid_recorder[user_id+'&'+path] = session['user_id']
# 在上层 目录下创建一个名为 user_id_path 的文件夹
path_dir = os.path.join(parent_dir, user_id, path)
os.makedirs(path_dir, exist_ok=True)
# 在此文件夹内部创建一个.config文件，并写入 user_id=user_id\n path=path
config_path = os.path.join(path_dir, '.config')
with open(config_path, 'w') as f:
f.write(f"user_id={user_id}\npath={path}")
return render_template('index.html', user_id=user_id, path=path)
@app.route('/vscode_data', methods=['POST'])
def vscode_data():
data = request.json
config = data['config']
realtime_response(config,data)
print(f"Received data from VSCode: {data}")
return jsonify({"status": "success", "received": data})
@app.route('/get_session')
def get_session():
user_session = session.get('user_id', 'default_session')
return jsonify({"session": user_session})
if __name__ == '__main__':
socketio.run(app, host='0.0.0.0', port=5500)

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import time
class BackBoardManager:
def __init__(self):
self.backboards = {} # user_id: Backboard
def add_backboard(self, user_id, folder):
self.backboards[user_id] = Backboard(user_id, folder)
def get_backboard(self, user_id):
if user_id not in self.backboards:
return None
return self.backboards[user_id]
class Backboard:
def __init__(self, user_id, folder):
self.user_id = user_id
self.folder = folder
self.create_time = time.time()
self.enter_chapter_time = time.time()
def next_chapter(self):
self.enter_chapter_time = time.time()
def get_info_prompt(self):
return f"###Global Info:###\n\
User is {self.user_id}\n\
User's total study time is {self.get_deltatime_mmss(self.create_time, time.time())}\n\
User's current chapter study time is {self.get_deltatime_mmss(self.enter_chapter_time, time.time())}\n\
\n\n\
"
def get_deltatime_mmss(self, start_time, end_time):
elapsed_time = end_time - start_time
hours = int(elapsed_time // 3600)
minutes = int((elapsed_time % 3600) // 60)
seconds = int(elapsed_time % 60)
return f"{hours:02d}:{minutes:02d}:{seconds:02d}"

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@@ -1 +0,0 @@
2024-11-14 21:49:45.643 | INFO | agentscope.models:read_model_configs:186 - Load configs for model wrapper: openai_cfg

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@@ -1,7 +0,0 @@
{
"project": "adTV6W",
"name": "4l1w9g",
"run_id": "run_20241114-215220_4l1w9g",
"timestamp": "2024-11-14 21:52:20",
"pid": 18255
}

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from flask import Flask, session, request, jsonify, render_template, send_from_directory
from flask_cors import CORS
from flask_socketio import SocketIO, join_room, emit
import markdown
import os
import uuid
import shutil
import sys
import json
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.insert(0, parent_dir)
current_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '.'))
sys.path.insert(0, current_dir)
from AlgoriAgent.projects.algoriAgent.agent_manager import AgentManager
app = Flask(__name__)
app.secret_key = 'cakebaker'
# 配置 CORS
CORS(app, resources={r"/*": {"origins": "http://localhost:9888"}}, supports_credentials=True)
userid_recorder = {} # user_id&path -> session['user_id']
'''
Backboard
'''
from backboardManager import BackBoardManager
backboard_manager = BackBoardManager()
@app.route('/backboard')
def backboard():
return jsonify({'status': 'success'})
'''
Agent and Chat
'''
socketio = SocketIO(app, cors_allowed_origins="*")
agent_manager = AgentManager()
def realtime_response(config, realtime_action):
print("config"+str(config))
print("realtime_action"+str(realtime_action))
pass
@socketio.on('connect')
def handle_connect():
user = request.args.get('username')
folder_name = request.args.get('folder')
user_id = userid_recorder[user+'&'+folder_name]
session['user_id'] = user_id
print(f'User connected with session user_id: {user_id}')
# 从markdown、markdown_prompts、score_prompts中各读取数据folder.md并进行new_agent
with open(f'books/markdown/{folder_name}.md','r') as fmd,\
open(f'books/markdown_prompts/{folder_name}.md','r')as fmdp,\
open(f'books/score_prompts/{folder_name}.md','r') as fsp:
markdown = fmd.read()
markdown_prompts = fmdp.read()
score_prompts = fsp.read()
id, agent = agent_manager.new_agent(markdown, markdown_prompts, score_prompts, id=user_id)
join_room(id) # 将该用户加入以user_id为名的room
# 当接收到前端的消息时，触发此事件
@socketio.on('send_message')
def handle_message(data):
print(f"Message from client: {data}")
id = session.get('user_id')
# 构造回复消息
res = agent_manager.invoke(id, data)
reply = f"AI: {res.content['speak']}"
# 将回复发送回前端
emit('receive_message', reply)
'''
Markdown to HTML
'''
# 配置文件路径
MARKDOWN_DIR = 'books/markdown' # 存放 markdown 文件的文件夹
STATIC_DIR = 'static' # 存放生成的 HTML 和图片资源的文件夹
IMAGE_DIR = 'image' # 图片资源相对于 markdown 的位置
@app.route('/<filename>-markdown', methods=['GET'])
def convert_md(filename):
md_file_path = os.path.join(MARKDOWN_DIR, f'{filename}.md')
if not os.path.exists(md_file_path):
return jsonify({'error': 'Markdown file not found'}), 404
# 读取 markdown 文件内容
with open(md_file_path, 'r', encoding='utf-8') as file:
md_content = file.read()
# 将 markdown 转换为 HTML
html_content = markdown.markdown(md_content)
# 插入 CSS 样式，限制图片最大宽度为 100%，高度自动调整
html_with_styles = f"""
<html>
<head>
<style>
img {{
max-width: 100%;
height: auto;
}}
</style>
</head>
<body>
{html_content}
</body>
</html>
"""
# 保存 HTML 文件到 static 文件夹
html_output_path = os.path.join(STATIC_DIR, f'{filename}.html')
with open(html_output_path, 'w', encoding='utf-8') as html_file:
html_file.write(html_with_styles)
# 处理图片资源，将 image/xxx 文件夹拷贝到 static/image/xxx
image_source_dir = os.path.join(MARKDOWN_DIR, IMAGE_DIR, filename)
image_target_dir = os.path.join(STATIC_DIR, IMAGE_DIR, filename)
print(f"Copying image resources from {image_source_dir} to {image_target_dir}")
if os.path.exists(image_source_dir):
# 确保 static/image/xxx 目录存在
if not os.path.exists(image_target_dir):
os.makedirs(image_target_dir)
# 拷贝图片资源
for image_file in os.listdir(image_source_dir):
full_image_file_path = os.path.join(image_source_dir, image_file)
if os.path.isfile(full_image_file_path):
shutil.copy(full_image_file_path, image_target_dir)
return jsonify({'html_url': f'/static/{filename}.html'})
# 静态文件（HTML 和图片资源）的访问
@app.route('/static/<path:filename>')
def serve_static(filename):
return send_from_directory(STATIC_DIR, filename)
'''
Vscode
'''
@app.route('/desktop/<user_id>/<path>')
def hello_world(user_id, path):
if 'user_id' not in session:
session['user_id'] = 'user_' + str(uuid.uuid4())
userid_recorder[user_id+'&'+path] = session['user_id']
# 在上层 目录下创建一个名为 user_id_path 的文件夹
path_dir = os.path.join(parent_dir, user_id, path)
os.makedirs(path_dir, exist_ok=True)
# 在此文件夹内部创建一个.config文件，并写入 user_id=user_id\n path=path
config_path = os.path.join(path_dir, '.config')
with open(config_path, 'w') as f:
f.write(f"user_id={user_id}\npath={path}")
return render_template('index.html', user_id=user_id, path=path)
@app.route('/vscode_data', methods=['POST'])
def vscode_data():
data = request.json
config = data['config']
realtime_response(config,data)
print(f"Received data from VSCode: {data}")
return jsonify({"status": "success", "received": data})
@app.route('/get_session')
def get_session():
user_session = session.get('user_id', 'default_session')
return jsonify({"session": user_session})
if __name__ == '__main__':
socketio.run(app, host='0.0.0.0', port=5500)

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import time
class BackBoardManager:
def __init__(self):
self.backboards = {} # user_id: Backboard
def add_backboard(self, user_id, folder):
self.backboards[user_id] = Backboard(user_id, folder)
def get_backboard(self, user_id):
if user_id not in self.backboards:
return None
return self.backboards[user_id]
class Backboard:
def __init__(self, user_id, folder):
self.user_id = user_id
self.folder = folder
self.create_time = time.time()
self.enter_chapter_time = time.time()
def next_chapter(self):
self.enter_chapter_time = time.time()
def get_info_prompt(self):
return f"###Global Info:###\n\
User is {self.user_id}\n\
User's total study time is {self.get_deltatime_mmss(self.create_time, time.time())}\n\
User's current chapter study time is {self.get_deltatime_mmss(self.enter_chapter_time, time.time())}\n\
\n\n\
"
def get_deltatime_mmss(self, start_time, end_time):
elapsed_time = end_time - start_time
hours = int(elapsed_time // 3600)
minutes = int((elapsed_time % 3600) // 60)
seconds = int(elapsed_time % 60)
return f"{hours:02d}:{minutes:02d}:{seconds:02d}"

0
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@@ -1 +0,0 @@
2024-11-14 21:52:45.458 | INFO | agentscope.models:read_model_configs:186 - Load configs for model wrapper: openai_cfg

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@@ -1,7 +0,0 @@
{
"project": "bWVpBF",
"name": "13ob9v",
"run_id": "run_20241114-215426_13ob9v",
"timestamp": "2024-11-14 21:54:26",
"pid": 19572
}

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from flask import Flask, session, request, jsonify, render_template, send_from_directory
from flask_cors import CORS
from flask_socketio import SocketIO, join_room, emit
import markdown
import os
import uuid
import shutil
import sys
import json
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.insert(0, parent_dir)
current_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '.'))
sys.path.insert(0, current_dir)
from AlgoriAgent.projects.algoriAgent.agent_manager import AgentManager
app = Flask(__name__)
app.secret_key = 'cakebaker'
# 配置 CORS
CORS(app, resources={r"/*": {"origins": "http://localhost:9888"}}, supports_credentials=True)
userid_recorder = {} # user_id&path -> session['user_id']
'''
Backboard
'''
from backboardManager import BackBoardManager
backboard_manager = BackBoardManager()
@app.route('/backboard')
def backboard():
return jsonify({'status': 'success'})
'''
Agent and Chat
'''
socketio = SocketIO(app, cors_allowed_origins="*")
agent_manager = AgentManager()
def realtime_response(config, realtime_action):
print("config"+str(config))
print("realtime_action"+str(realtime_action))
pass
@socketio.on('connect')
def handle_connect():
user = request.args.get('username')
folder_name = request.args.get('folder')
user_id = userid_recorder[user+'&'+folder_name]
session['user_id'] = user_id
print(f'User connected with session user_id: {user_id}')
# 从markdown、markdown_prompts、score_prompts中各读取数据folder.md并进行new_agent
with open(f'books/markdown/{folder_name}.md','r') as fmd,\
open(f'books/markdown_prompts/{folder_name}.md','r')as fmdp,\
open(f'books/score_prompts/{folder_name}.md','r') as fsp:
markdown = fmd.read()
markdown_prompts = fmdp.read()
score_prompts = fsp.read()
id, agent = agent_manager.new_agent(markdown, markdown_prompts, score_prompts, id=user_id)
join_room(id) # 将该用户加入以user_id为名的room
# 当接收到前端的消息时，触发此事件
@socketio.on('send_message')
def handle_message(data):
print(f"Message from client: {data}")
id = session.get('user_id')
# 构造回复消息
res = agent_manager.invoke(id, data)
reply = f"AI: {res.content['speak']}"
# 将回复发送回前端
emit('receive_message', reply)
'''
Markdown to HTML
'''
# 配置文件路径
MARKDOWN_DIR = 'books/markdown' # 存放 markdown 文件的文件夹
STATIC_DIR = 'static' # 存放生成的 HTML 和图片资源的文件夹
IMAGE_DIR = 'image' # 图片资源相对于 markdown 的位置
@app.route('/<filename>-markdown', methods=['GET'])
def convert_md(filename):
md_file_path = os.path.join(MARKDOWN_DIR, f'{filename}.md')
if not os.path.exists(md_file_path):
return jsonify({'error': 'Markdown file not found'}), 404
# 读取 markdown 文件内容
with open(md_file_path, 'r', encoding='utf-8') as file:
md_content = file.read()
# 将 markdown 转换为 HTML
html_content = markdown.markdown(md_content)
# 插入 CSS 样式，限制图片最大宽度为 100%，高度自动调整
html_with_styles = f"""
<html>
<head>
<style>
img {{
max-width: 100%;
height: auto;
}}
</style>
</head>
<body>
{html_content}
</body>
</html>
"""
# 保存 HTML 文件到 static 文件夹
html_output_path = os.path.join(STATIC_DIR, f'{filename}.html')
with open(html_output_path, 'w', encoding='utf-8') as html_file:
html_file.write(html_with_styles)
# 处理图片资源，将 image/xxx 文件夹拷贝到 static/image/xxx
image_source_dir = os.path.join(MARKDOWN_DIR, IMAGE_DIR, filename)
image_target_dir = os.path.join(STATIC_DIR, IMAGE_DIR, filename)
print(f"Copying image resources from {image_source_dir} to {image_target_dir}")
if os.path.exists(image_source_dir):
# 确保 static/image/xxx 目录存在
if not os.path.exists(image_target_dir):
os.makedirs(image_target_dir)
# 拷贝图片资源
for image_file in os.listdir(image_source_dir):
full_image_file_path = os.path.join(image_source_dir, image_file)
if os.path.isfile(full_image_file_path):
shutil.copy(full_image_file_path, image_target_dir)
return jsonify({'html_url': f'/static/{filename}.html'})
# 静态文件（HTML 和图片资源）的访问
@app.route('/static/<path:filename>')
def serve_static(filename):
return send_from_directory(STATIC_DIR, filename)
'''
Vscode
'''
@app.route('/desktop/<user_id>/<path>')
def hello_world(user_id, path):
if 'user_id' not in session:
session['user_id'] = 'user_' + str(uuid.uuid4())
userid_recorder[user_id+'&'+path] = session['user_id']
# 在上层 目录下创建一个名为 user_id_path 的文件夹
path_dir = os.path.join(parent_dir, user_id, path)
os.makedirs(path_dir, exist_ok=True)
# 在此文件夹内部创建一个.config文件，并写入 user_id=user_id\n path=path
config_path = os.path.join(path_dir, '.config')
with open(config_path, 'w') as f:
f.write(f"user_id={user_id}\npath={path}")
return render_template('index.html', user_id=user_id, path=path)
@app.route('/vscode_data', methods=['POST'])
def vscode_data():
data = request.json
config = data['config']
realtime_response(config,data)
print(f"Received data from VSCode: {data}")
return jsonify({"status": "success", "received": data})
@app.route('/get_session')
def get_session():
user_session = session.get('user_id', 'default_session')
return jsonify({"session": user_session})
if __name__ == '__main__':
socketio.run(app, host='0.0.0.0', port=5500)

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import time
class BackBoardManager:
def __init__(self):
self.backboards = {} # user_id: Backboard
def add_backboard(self, user_id, folder):
self.backboards[user_id] = Backboard(user_id, folder)
def get_backboard(self, user_id):
if user_id not in self.backboards:
return None
return self.backboards[user_id]
class Backboard:
def __init__(self, user_id, folder):
self.user_id = user_id
self.folder = folder
self.create_time = time.time()
self.enter_chapter_time = time.time()
def next_chapter(self):
self.enter_chapter_time = time.time()
def get_info_prompt(self):
return f"###Global Info:###\n\
User is {self.user_id}\n\
User's total study time is {self.get_deltatime_mmss(self.create_time, time.time())}\n\
User's current chapter study time is {self.get_deltatime_mmss(self.enter_chapter_time, time.time())}\n\
\n\n\
"
def get_deltatime_mmss(self, start_time, end_time):
elapsed_time = end_time - start_time
hours = int(elapsed_time // 3600)
minutes = int((elapsed_time % 3600) // 60)
seconds = int(elapsed_time % 60)
return f"{hours:02d}:{minutes:02d}:{seconds:02d}"

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2024-11-14 21:54:42.058 | INFO | agentscope.models:read_model_configs:186 - Load configs for model wrapper: openai_cfg

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@@ -1,7 +0,0 @@
{
"project": "JpOniY",
"name": "4hht6r",
"run_id": "run_20241114-215514_4hht6r",
"timestamp": "2024-11-14 21:55:14",
"pid": 20265
}

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from flask import Flask, session, request, jsonify, render_template, send_from_directory
from flask_cors import CORS
from flask_socketio import SocketIO, join_room, emit
import markdown
import os
import uuid
import shutil
import sys
import json
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.insert(0, parent_dir)
current_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '.'))
sys.path.insert(0, current_dir)
from AlgoriAgent.projects.algoriAgent.agent_manager import AgentManager
app = Flask(__name__)
app.secret_key = 'cakebaker'
# 配置 CORS
CORS(app, resources={r"/*": {"origins": "http://localhost:9888"}}, supports_credentials=True)
userid_recorder = {} # user_id&path -> session['user_id']
'''
Backboard
'''
from backboardManager import BackBoardManager
backboard_manager = BackBoardManager()
@app.route('/backboard')
def backboard():
return jsonify({'status': 'success'})
'''
Agent and Chat
'''
socketio = SocketIO(app, cors_allowed_origins="*")
agent_manager = AgentManager()
def realtime_response(config, realtime_action):
print("config"+str(config))
print("realtime_action"+str(realtime_action))
pass
@socketio.on('connect')
def handle_connect():
user = request.args.get('username')
folder_name = request.args.get('folder')
user_id = userid_recorder[user+'&'+folder_name]
session['user_id'] = user_id
print(f'User connected with session user_id: {user_id}')
# 从markdown、markdown_prompts、score_prompts中各读取数据folder.md并进行new_agent
with open(f'books/markdown/{folder_name}.md','r') as fmd,\
open(f'books/markdown_prompts/{folder_name}.md','r')as fmdp,\
open(f'books/score_prompts/{folder_name}.md','r') as fsp:
markdown = fmd.read()
markdown_prompts = fmdp.read()
score_prompts = fsp.read()
print(user_id)
id, agent = agent_manager.new_agent(markdown, markdown_prompts, score_prompts, id=user_id)
join_room(id) # 将该用户加入以user_id为名的room
# 当接收到前端的消息时，触发此事件
@socketio.on('send_message')
def handle_message(data):
print(f"Message from client: {data}")
id = session.get('user_id')
# 构造回复消息
res = agent_manager.invoke(id, data)
reply = f"AI: {res.content['speak']}"
# 将回复发送回前端
emit('receive_message', reply)
'''
Markdown to HTML
'''
# 配置文件路径
MARKDOWN_DIR = 'books/markdown' # 存放 markdown 文件的文件夹
STATIC_DIR = 'static' # 存放生成的 HTML 和图片资源的文件夹
IMAGE_DIR = 'image' # 图片资源相对于 markdown 的位置
@app.route('/<filename>-markdown', methods=['GET'])
def convert_md(filename):
md_file_path = os.path.join(MARKDOWN_DIR, f'{filename}.md')
if not os.path.exists(md_file_path):
return jsonify({'error': 'Markdown file not found'}), 404
# 读取 markdown 文件内容
with open(md_file_path, 'r', encoding='utf-8') as file:
md_content = file.read()
# 将 markdown 转换为 HTML
html_content = markdown.markdown(md_content)
# 插入 CSS 样式，限制图片最大宽度为 100%，高度自动调整
html_with_styles = f"""
<html>
<head>
<style>
img {{
max-width: 100%;
height: auto;
}}
</style>
</head>
<body>
{html_content}
</body>
</html>
"""
# 保存 HTML 文件到 static 文件夹
html_output_path = os.path.join(STATIC_DIR, f'{filename}.html')
with open(html_output_path, 'w', encoding='utf-8') as html_file:
html_file.write(html_with_styles)
# 处理图片资源，将 image/xxx 文件夹拷贝到 static/image/xxx
image_source_dir = os.path.join(MARKDOWN_DIR, IMAGE_DIR, filename)
image_target_dir = os.path.join(STATIC_DIR, IMAGE_DIR, filename)
print(f"Copying image resources from {image_source_dir} to {image_target_dir}")
if os.path.exists(image_source_dir):
# 确保 static/image/xxx 目录存在
if not os.path.exists(image_target_dir):
os.makedirs(image_target_dir)
# 拷贝图片资源
for image_file in os.listdir(image_source_dir):
full_image_file_path = os.path.join(image_source_dir, image_file)
if os.path.isfile(full_image_file_path):
shutil.copy(full_image_file_path, image_target_dir)
return jsonify({'html_url': f'/static/{filename}.html'})
# 静态文件（HTML 和图片资源）的访问
@app.route('/static/<path:filename>')
def serve_static(filename):
return send_from_directory(STATIC_DIR, filename)
'''
Vscode
'''
@app.route('/desktop/<user_id>/<path>')
def hello_world(user_id, path):
if 'user_id' not in session:
session['user_id'] = 'user_' + str(uuid.uuid4())
userid_recorder[user_id+'&'+path] = session['user_id']
# 在上层 目录下创建一个名为 user_id_path 的文件夹
path_dir = os.path.join(parent_dir, user_id, path)
os.makedirs(path_dir, exist_ok=True)
# 在此文件夹内部创建一个.config文件，并写入 user_id=user_id\n path=path
config_path = os.path.join(path_dir, '.config')
with open(config_path, 'w') as f:
f.write(f"user_id={user_id}\npath={path}")
return render_template('index.html', user_id=user_id, path=path)
@app.route('/vscode_data', methods=['POST'])
def vscode_data():
data = request.json
config = data['config']
realtime_response(config,data)
print(f"Received data from VSCode: {data}")
return jsonify({"status": "success", "received": data})
@app.route('/get_session')
def get_session():
user_session = session.get('user_id', 'default_session')
return jsonify({"session": user_session})
if __name__ == '__main__':
socketio.run(app, host='0.0.0.0', port=5500)

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@@ -1,51 +0,0 @@
import time
class BackBoardManager:
def __init__(self):
self.backboards = {} # user_id: Backboard
def add_backboard(self, user_id, folder):
self.backboards[user_id] = Backboard(user_id, folder)
def get_backboard(self, user_id):
if user_id not in self.backboards:
return None
return self.backboards[user_id]
class Backboard:
def __init__(self, user_id, folder):
self.user_id = user_id
self.folder = folder
self.create_time = time.time()
self.enter_chapter_time = time.time()
def next_chapter(self):
self.enter_chapter_time = time.time()
def get_info_prompt(self):
return f"###Global Info:###\n\
User is {self.user_id}\n\
User's total study time is {self.get_deltatime_mmss(self.create_time, time.time())}\n\
User's current chapter study time is {self.get_deltatime_mmss(self.enter_chapter_time, time.time())}\n\
\n\n\
"
def get_deltatime_mmss(self, start_time, end_time):
elapsed_time = end_time - start_time
hours = int(elapsed_time // 3600)
minutes = int((elapsed_time % 3600) // 60)
seconds = int(elapsed_time % 60)
return f"{hours:02d}:{minutes:02d}:{seconds:02d}"

0
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1
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@@ -1 +0,0 @@
2024-11-14 21:55:29.213 | INFO | agentscope.models:read_model_configs:186 - Load configs for model wrapper: openai_cfg

7
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@@ -1,7 +0,0 @@
{
"project": "c6FVzt",
"name": "s92n8y",
"run_id": "run_20241114-215712_s92n8y",
"timestamp": "2024-11-14 21:57:12",
"pid": 21458
}

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from flask import Flask, session, request, jsonify, render_template, send_from_directory
from flask_cors import CORS
from flask_socketio import SocketIO, join_room, emit
import markdown
import os
import uuid
import shutil
import sys
import json
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.insert(0, parent_dir)
current_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '.'))
sys.path.insert(0, current_dir)
from AlgoriAgent.projects.algoriAgent.agent_manager import AgentManager
app = Flask(__name__)
app.secret_key = 'cakebaker'
# 配置 CORS
CORS(app, resources={r"/*": {"origins": "http://localhost:9888"}}, supports_credentials=True)
userid_recorder = {} # user_id&path -> session['user_id']
'''
Backboard
'''
from backboardManager import BackBoardManager
backboard_manager = BackBoardManager()
@app.route('/backboard')
def backboard():
return jsonify({'status': 'success'})
'''
Agent and Chat
'''
socketio = SocketIO(app, cors_allowed_origins="*")
agent_manager = AgentManager()
def realtime_response(config, realtime_action):
print("config"+str(config))
print("realtime_action"+str(realtime_action))
pass
@socketio.on('connect')
def handle_connect():
user = request.args.get('username')
folder_name = request.args.get('folder')
user_id = userid_recorder[user+'&'+folder_name]
session['user_id'] = user_id
print(f'User connected with session user_id: {user_id}')
# 从markdown、markdown_prompts、score_prompts中各读取数据folder.md并进行new_agent
with open(f'books/markdown/{folder_name}.md','r') as fmd,\
open(f'books/markdown_prompts/{folder_name}.md','r')as fmdp,\
open(f'books/score_prompts/{folder_name}.md','r') as fsp:
markdown = fmd.read()
markdown_prompts = fmdp.read()
score_prompts = fsp.read()
print('-----------------------')
print(user_id)
id, agent = agent_manager.new_agent(markdown, markdown_prompts, score_prompts, id=user_id)
join_room(id) # 将该用户加入以user_id为名的room
# 当接收到前端的消息时，触发此事件
@socketio.on('send_message')
def handle_message(data):
print(f"Message from client: {data}")
id = session.get('user_id')
# 构造回复消息
res = agent_manager.invoke(id, data)
reply = f"AI: {res.content['speak']}"
# 将回复发送回前端
emit('receive_message', reply)
'''
Markdown to HTML
'''
# 配置文件路径
MARKDOWN_DIR = 'books/markdown' # 存放 markdown 文件的文件夹
STATIC_DIR = 'static' # 存放生成的 HTML 和图片资源的文件夹
IMAGE_DIR = 'image' # 图片资源相对于 markdown 的位置
@app.route('/<filename>-markdown', methods=['GET'])
def convert_md(filename):
md_file_path = os.path.join(MARKDOWN_DIR, f'{filename}.md')
if not os.path.exists(md_file_path):
return jsonify({'error': 'Markdown file not found'}), 404
# 读取 markdown 文件内容
with open(md_file_path, 'r', encoding='utf-8') as file:
md_content = file.read()
# 将 markdown 转换为 HTML
html_content = markdown.markdown(md_content)
# 插入 CSS 样式，限制图片最大宽度为 100%，高度自动调整
html_with_styles = f"""
<html>
<head>
<style>
img {{
max-width: 100%;
height: auto;
}}
</style>
</head>
<body>
{html_content}
</body>
</html>
"""
# 保存 HTML 文件到 static 文件夹
html_output_path = os.path.join(STATIC_DIR, f'{filename}.html')
with open(html_output_path, 'w', encoding='utf-8') as html_file:
html_file.write(html_with_styles)
# 处理图片资源，将 image/xxx 文件夹拷贝到 static/image/xxx
image_source_dir = os.path.join(MARKDOWN_DIR, IMAGE_DIR, filename)
image_target_dir = os.path.join(STATIC_DIR, IMAGE_DIR, filename)
print(f"Copying image resources from {image_source_dir} to {image_target_dir}")
if os.path.exists(image_source_dir):
# 确保 static/image/xxx 目录存在
if not os.path.exists(image_target_dir):
os.makedirs(image_target_dir)
# 拷贝图片资源
for image_file in os.listdir(image_source_dir):
full_image_file_path = os.path.join(image_source_dir, image_file)
if os.path.isfile(full_image_file_path):
shutil.copy(full_image_file_path, image_target_dir)
return jsonify({'html_url': f'/static/{filename}.html'})
# 静态文件（HTML 和图片资源）的访问
@app.route('/static/<path:filename>')
def serve_static(filename):
return send_from_directory(STATIC_DIR, filename)
'''
Vscode
'''
@app.route('/desktop/<user_id>/<path>')
def hello_world(user_id, path):
if 'user_id' not in session:
session['user_id'] = 'user_' + str(uuid.uuid4())
userid_recorder[user_id+'&'+path] = session['user_id']
# 在上层 目录下创建一个名为 user_id_path 的文件夹
path_dir = os.path.join(parent_dir, user_id, path)
os.makedirs(path_dir, exist_ok=True)
# 在此文件夹内部创建一个.config文件，并写入 user_id=user_id\n path=path
config_path = os.path.join(path_dir, '.config')
with open(config_path, 'w') as f:
f.write(f"user_id={user_id}\npath={path}")
return render_template('index.html', user_id=user_id, path=path)
@app.route('/vscode_data', methods=['POST'])
def vscode_data():
data = request.json
config = data['config']
realtime_response(config,data)
print(f"Received data from VSCode: {data}")
return jsonify({"status": "success", "received": data})
@app.route('/get_session')
def get_session():
user_session = session.get('user_id', 'default_session')
return jsonify({"session": user_session})
if __name__ == '__main__':
socketio.run(app, host='0.0.0.0', port=5500)

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@@ -1,51 +0,0 @@
import time
class BackBoardManager:
def __init__(self):
self.backboards = {} # user_id: Backboard
def add_backboard(self, user_id, folder):
self.backboards[user_id] = Backboard(user_id, folder)
def get_backboard(self, user_id):
if user_id not in self.backboards:
return None
return self.backboards[user_id]
class Backboard:
def __init__(self, user_id, folder):
self.user_id = user_id
self.folder = folder
self.create_time = time.time()
self.enter_chapter_time = time.time()
def next_chapter(self):
self.enter_chapter_time = time.time()
def get_info_prompt(self):
return f"###Global Info:###\n\
User is {self.user_id}\n\
User's total study time is {self.get_deltatime_mmss(self.create_time, time.time())}\n\
User's current chapter study time is {self.get_deltatime_mmss(self.enter_chapter_time, time.time())}\n\
\n\n\
"
def get_deltatime_mmss(self, start_time, end_time):
elapsed_time = end_time - start_time
hours = int(elapsed_time // 3600)
minutes = int((elapsed_time % 3600) // 60)
seconds = int(elapsed_time % 60)
return f"{hours:02d}:{minutes:02d}:{seconds:02d}"

0
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@@ -1 +0,0 @@
2024-11-14 21:57:23.583 | INFO | agentscope.models:read_model_configs:186 - Load configs for model wrapper: openai_cfg

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@@ -1,7 +0,0 @@
{
"project": "pGMDpY",
"name": "1j41on",
"run_id": "run_20241114-220215_1j41on",
"timestamp": "2024-11-14 22:02:15",
"pid": 24013
}

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from flask import Flask, session, request, jsonify, render_template, send_from_directory
from flask_cors import CORS
from flask_socketio import SocketIO, join_room, emit
import markdown
import os
import uuid
import shutil
import sys
import json
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.insert(0, parent_dir)
current_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '.'))
sys.path.insert(0, current_dir)
from AlgoriAgent.projects.algoriAgent.agent_manager import AgentManager
app = Flask(__name__)
app.secret_key = 'cakebaker'
# 配置 CORS
CORS(app, resources={r"/*": {"origins": "http://localhost:9888"}}, supports_credentials=True)
userid_recorder = {} # user_id&path -> session['user_id']
'''
Backboard
'''
from backboardManager import BackBoardManager
backboard_manager = BackBoardManager()
@app.route('/backboard')
def backboard():
return jsonify({'status': 'success'})
'''
Agent and Chat
'''
socketio = SocketIO(app, cors_allowed_origins="*")
agent_manager = AgentManager()
def realtime_response(config, realtime_action):
print("config"+str(config))
print("realtime_action"+str(realtime_action))
pass
@socketio.on('connect')
def handle_connect():
user = request.args.get('username')
folder_name = request.args.get('folder')
user_id = userid_recorder[user+'&'+folder_name]
session['user_id'] = user_id
print(f'User connected with session user_id: {user_id}')
# 从markdown、markdown_prompts、score_prompts中各读取数据folder.md并进行new_agent
with open(f'books/markdown/{folder_name}.md','r') as fmd,\
open(f'books/markdown_prompts/{folder_name}.md','r')as fmdp,\
open(f'books/score_prompts/{folder_name}.md','r') as fsp:
markdown = fmd.read()
markdown_prompts = fmdp.read()
score_prompts = fsp.read()
print('-----------------------')
print(user_id)
user_id, agent = agent_manager.new_agent(markdown, markdown_prompts, score_prompts, id=user_id)
join_room(user_id) # 将该用户加入以user_id为名的room
# 当接收到前端的消息时，触发此事件
@socketio.on('send_message')
def handle_message(data):
print(f"Message from client: {data}")
id = session.get('user_id')
# 构造回复消息
res = agent_manager.invoke(id, data)
reply = f"AI: {res.content['speak']}"
# 将回复发送回前端
emit('receive_message', reply)
'''
Markdown to HTML
'''
# 配置文件路径
MARKDOWN_DIR = 'books/markdown' # 存放 markdown 文件的文件夹
STATIC_DIR = 'static' # 存放生成的 HTML 和图片资源的文件夹
IMAGE_DIR = 'image' # 图片资源相对于 markdown 的位置
@app.route('/<filename>-markdown', methods=['GET'])
def convert_md(filename):
md_file_path = os.path.join(MARKDOWN_DIR, f'{filename}.md')
if not os.path.exists(md_file_path):
return jsonify({'error': 'Markdown file not found'}), 404
# 读取 markdown 文件内容
with open(md_file_path, 'r', encoding='utf-8') as file:
md_content = file.read()
# 将 markdown 转换为 HTML
html_content = markdown.markdown(md_content)
# 插入 CSS 样式，限制图片最大宽度为 100%，高度自动调整
html_with_styles = f"""
<html>
<head>
<style>
img {{
max-width: 100%;
height: auto;
}}
</style>
</head>
<body>
{html_content}
</body>
</html>
"""
# 保存 HTML 文件到 static 文件夹
html_output_path = os.path.join(STATIC_DIR, f'{filename}.html')
with open(html_output_path, 'w', encoding='utf-8') as html_file:
html_file.write(html_with_styles)
# 处理图片资源，将 image/xxx 文件夹拷贝到 static/image/xxx
image_source_dir = os.path.join(MARKDOWN_DIR, IMAGE_DIR, filename)
image_target_dir = os.path.join(STATIC_DIR, IMAGE_DIR, filename)
print(f"Copying image resources from {image_source_dir} to {image_target_dir}")
if os.path.exists(image_source_dir):
# 确保 static/image/xxx 目录存在
if not os.path.exists(image_target_dir):
os.makedirs(image_target_dir)
# 拷贝图片资源
for image_file in os.listdir(image_source_dir):
full_image_file_path = os.path.join(image_source_dir, image_file)
if os.path.isfile(full_image_file_path):
shutil.copy(full_image_file_path, image_target_dir)
return jsonify({'html_url': f'/static/{filename}.html'})
# 静态文件（HTML 和图片资源）的访问
@app.route('/static/<path:filename>')
def serve_static(filename):
return send_from_directory(STATIC_DIR, filename)
'''
Vscode
'''
@app.route('/desktop/<user_id>/<path>')
def hello_world(user_id, path):
if 'user_id' not in session:
session['user_id'] = 'user_' + str(uuid.uuid4())
userid_recorder[user_id+'&'+path] = session['user_id']
# 在上层 目录下创建一个名为 user_id_path 的文件夹
path_dir = os.path.join(parent_dir, user_id, path)
os.makedirs(path_dir, exist_ok=True)
# 在此文件夹内部创建一个.config文件，并写入 user_id=user_id\n path=path
config_path = os.path.join(path_dir, '.config')
with open(config_path, 'w') as f:
f.write(f"user_id={user_id}\npath={path}")
return render_template('index.html', user_id=user_id, path=path)
@app.route('/vscode_data', methods=['POST'])
def vscode_data():
data = request.json
config = data['config']
realtime_response(config,data)
print(f"Received data from VSCode: {data}")
return jsonify({"status": "success", "received": data})
@app.route('/get_session')
def get_session():
user_session = session.get('user_id', 'default_session')
return jsonify({"session": user_session})
if __name__ == '__main__':
socketio.run(app, host='0.0.0.0', port=5500)

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@@ -1,51 +0,0 @@
import time
class BackBoardManager:
def __init__(self):
self.backboards = {} # user_id: Backboard
def add_backboard(self, user_id, folder):
self.backboards[user_id] = Backboard(user_id, folder)
def get_backboard(self, user_id):
if user_id not in self.backboards:
return None
return self.backboards[user_id]
class Backboard:
def __init__(self, user_id, folder):
self.user_id = user_id
self.folder = folder
self.create_time = time.time()
self.enter_chapter_time = time.time()
def next_chapter(self):
self.enter_chapter_time = time.time()
def get_info_prompt(self):
return f"###Global Info:###\n\
User is {self.user_id}\n\
User's total study time is {self.get_deltatime_mmss(self.create_time, time.time())}\n\
User's current chapter study time is {self.get_deltatime_mmss(self.enter_chapter_time, time.time())}\n\
\n\n\
"
def get_deltatime_mmss(self, start_time, end_time):
elapsed_time = end_time - start_time
hours = int(elapsed_time // 3600)
minutes = int((elapsed_time % 3600) // 60)
seconds = int(elapsed_time % 60)
return f"{hours:02d}:{minutes:02d}:{seconds:02d}"

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2024-11-14 22:02:22.004 | INFO | agentscope.models:read_model_configs:186 - Load configs for model wrapper: openai_cfg

7
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@@ -1,7 +0,0 @@
{
"project": "GMuxD5",
"name": "9vj7vr",
"run_id": "run_20241114-220531_9vj7vr",
"timestamp": "2024-11-14 22:05:31",
"pid": 25780
}

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from flask import Flask, session, request, jsonify, render_template, send_from_directory
from flask_cors import CORS
from flask_socketio import SocketIO, join_room, emit
import markdown
import os
import uuid
import shutil
import sys
import json
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.insert(0, parent_dir)
current_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '.'))
sys.path.insert(0, current_dir)
from AlgoriAgent.projects.algoriAgent.agent_manager import AgentManager
app = Flask(__name__)
app.secret_key = 'cakebaker'
# 配置 CORS
CORS(app, resources={r"/*": {"origins": "http://localhost:9888"}}, supports_credentials=True)
userid_recorder = {} # user_id&path -> session['user_id']
'''
Backboard
'''
from backboardManager import BackBoardManager
backboard_manager = BackBoardManager()
@app.route('/backboard')
def backboard():
return jsonify({'status': 'success'})
'''
Agent and Chat
'''
socketio = SocketIO(app, cors_allowed_origins="*")
agent_manager = AgentManager()
def realtime_response(config, realtime_action):
print("config"+str(config))
print("realtime_action"+str(realtime_action))
pass
@socketio.on('connect')
def handle_connect():
user = request.args.get('username')
folder_name = request.args.get('folder')
user_id = userid_recorder[user+'&'+folder_name]
session['user_id'] = user_id
print(f'User connected with session user_id: {user_id}')
# 从markdown、markdown_prompts、score_prompts中各读取数据folder.md并进行new_agent
with open(f'books/markdown/{folder_name}.md','r') as fmd,\
open(f'books/markdown_prompts/{folder_name}.md','r')as fmdp,\
open(f'books/score_prompts/{folder_name}.md','r') as fsp:
markdown = fmd.read()
markdown_prompts = fmdp.read()
score_prompts = fsp.read()
print('-----------------------')
print(user_id)
user_id, agent = agent_manager.new_agent(markdown, markdown_prompts, score_prompts, id=user_id)
join_room(user_id) # 将该用户加入以user_id为名的room
# 当接收到前端的消息时，触发此事件
@socketio.on('send_message')
def handle_message(data):
print(f"Message from client: {data}")
id = session.get('user_id')
# 构造回复消息
res = agent_manager.invoke(id, data)
reply = f"AI: {res.content['speak']}"
# 将回复发送回前端
emit('receive_message', reply)
'''
Markdown to HTML
'''
# 配置文件路径
MARKDOWN_DIR = 'books/markdown' # 存放 markdown 文件的文件夹
STATIC_DIR = 'static' # 存放生成的 HTML 和图片资源的文件夹
IMAGE_DIR = 'image' # 图片资源相对于 markdown 的位置
@app.route('/<filename>-markdown', methods=['GET'])
def convert_md(filename):
md_file_path = os.path.join(MARKDOWN_DIR, f'{filename}.md')
if not os.path.exists(md_file_path):
return jsonify({'error': 'Markdown file not found'}), 404
# 读取 markdown 文件内容
with open(md_file_path, 'r', encoding='utf-8') as file:
md_content = file.read()
# 将 markdown 转换为 HTML
html_content = markdown.markdown(md_content)
# 插入 CSS 样式，限制图片最大宽度为 100%，高度自动调整
html_with_styles = f"""
<html>
<head>
<style>
img {{
max-width: 100%;
height: auto;
}}
</style>
</head>
<body>
{html_content}
</body>
</html>
"""
# 保存 HTML 文件到 static 文件夹
html_output_path = os.path.join(STATIC_DIR, f'{filename}.html')
with open(html_output_path, 'w', encoding='utf-8') as html_file:
html_file.write(html_with_styles)
# 处理图片资源，将 image/xxx 文件夹拷贝到 static/image/xxx
image_source_dir = os.path.join(MARKDOWN_DIR, IMAGE_DIR, filename)
image_target_dir = os.path.join(STATIC_DIR, IMAGE_DIR, filename)
print(f"Copying image resources from {image_source_dir} to {image_target_dir}")
if os.path.exists(image_source_dir):
# 确保 static/image/xxx 目录存在
if not os.path.exists(image_target_dir):
os.makedirs(image_target_dir)
# 拷贝图片资源
for image_file in os.listdir(image_source_dir):
full_image_file_path = os.path.join(image_source_dir, image_file)
if os.path.isfile(full_image_file_path):
shutil.copy(full_image_file_path, image_target_dir)
return jsonify({'html_url': f'/static/{filename}.html'})
# 静态文件（HTML 和图片资源）的访问
@app.route('/static/<path:filename>')
def serve_static(filename):
return send_from_directory(STATIC_DIR, filename)
'''
Vscode
'''
@app.route('/desktop/<user_id>/<path>')
def hello_world(user_id, path):
if 'user_id' not in session:
session['user_id'] = 'user_' + str(uuid.uuid4())
userid_recorder[user_id+'&'+path] = session['user_id']
# 在上层 目录下创建一个名为 user_id_path 的文件夹
path_dir = os.path.join(parent_dir, user_id, path)
os.makedirs(path_dir, exist_ok=True)
# 在此文件夹内部创建一个.config文件，并写入 user_id=user_id\n path=path
config_path = os.path.join(path_dir, '.config')
with open(config_path, 'w') as f:
f.write(f"user_id={user_id}\npath={path}")
return render_template('index.html', user_id=user_id, path=path)
@app.route('/vscode_data', methods=['POST'])
def vscode_data():
data = request.json
config = data['config']
realtime_response(config,data)
print(f"Received data from VSCode: {data}")
return jsonify({"status": "success", "received": data})
@app.route('/get_session')
def get_session():
user_session = session.get('user_id', 'default_session')
return jsonify({"session": user_session})
if __name__ == '__main__':
socketio.run(app, host='0.0.0.0', port=5500)

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@@ -1,51 +0,0 @@
import time
class BackBoardManager:
def __init__(self):
self.backboards = {} # user_id: Backboard
def add_backboard(self, user_id, folder):
self.backboards[user_id] = Backboard(user_id, folder)
def get_backboard(self, user_id):
if user_id not in self.backboards:
return None
return self.backboards[user_id]
class Backboard:
def __init__(self, user_id, folder):
self.user_id = user_id
self.folder = folder
self.create_time = time.time()
self.enter_chapter_time = time.time()
def next_chapter(self):
self.enter_chapter_time = time.time()
def get_info_prompt(self):
return f"###Global Info:###\n\
User is {self.user_id}\n\
User's total study time is {self.get_deltatime_mmss(self.create_time, time.time())}\n\
User's current chapter study time is {self.get_deltatime_mmss(self.enter_chapter_time, time.time())}\n\
\n\n\
"
def get_deltatime_mmss(self, start_time, end_time):
elapsed_time = end_time - start_time
hours = int(elapsed_time // 3600)
minutes = int((elapsed_time % 3600) // 60)
seconds = int(elapsed_time % 60)
return f"{hours:02d}:{minutes:02d}:{seconds:02d}"

0
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1
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@@ -1 +0,0 @@
2024-11-14 22:05:42.392 | INFO | agentscope.models:read_model_configs:186 - Load configs for model wrapper: openai_cfg

7
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@@ -1,7 +0,0 @@
{
"project": "9nK6ES",
"name": "a9u1xe",
"run_id": "run_20241114-221323_a9u1xe",
"timestamp": "2024-11-14 22:13:23",
"pid": 29558
}

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from flask import Flask, session, request, jsonify, render_template, send_from_directory
from flask_cors import CORS
from flask_socketio import SocketIO, join_room, emit
import markdown
import os
import uuid
import shutil
import sys
import json
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.insert(0, parent_dir)
current_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '.'))
sys.path.insert(0, current_dir)
from AlgoriAgent.projects.algoriAgent.agent_manager import AgentManager
app = Flask(__name__)
app.secret_key = 'cakebaker'
# 配置 CORS
CORS(app, resources={r"/*": {"origins": "http://localhost:9888"}}, supports_credentials=True)
userid_recorder = {} # user_id&path -> session['user_id']
'''
Backboard
'''
from backboardManager import BackBoardManager
backboard_manager = BackBoardManager()
@app.route('/backboard')
def backboard():
return jsonify({'status': 'success'})
'''
Agent and Chat
'''
socketio = SocketIO(app, cors_allowed_origins="*")
agent_manager = AgentManager()
def realtime_response(config, realtime_action):
print("config"+str(config))
print("realtime_action"+str(realtime_action))
pass
@socketio.on('connect')
def handle_connect():
user = request.args.get('username')
folder_name = request.args.get('folder')
user_id = userid_recorder[user+'&'+folder_name]
session['user_id'] = user_id
print(f'User connected with session user_id: {user_id}')
# 从markdown、markdown_prompts、score_prompts中各读取数据folder.md并进行new_agent
with open(f'books/markdown/{folder_name}.md','r') as fmd,\
open(f'books/markdown_prompts/{folder_name}.md','r')as fmdp,\
open(f'books/score_prompts/{folder_name}.md','r') as fsp:
markdown = fmd.read()
markdown_prompts = fmdp.read()
score_prompts = fsp.read()
print('-----------------------')
print(user_id)
user_id, agent = agent_manager.new_agent(markdown, markdown_prompts, score_prompts, id=user_id)
join_room(user_id) # 将该用户加入以user_id为名的room
# 当接收到前端的消息时，触发此事件
@socketio.on('send_message')
def handle_message(data):
print(f"Message from client: {data}")
id = session.get('user_id')
# 构造回复消息
res = agent_manager.invoke(id, data)
reply = f"AI: {res.content['speak']}"
# 将回复发送回前端
emit('receive_message', reply)
'''
Markdown to HTML
'''
# 配置文件路径
MARKDOWN_DIR = 'books/markdown' # 存放 markdown 文件的文件夹
STATIC_DIR = 'static' # 存放生成的 HTML 和图片资源的文件夹
IMAGE_DIR = 'image' # 图片资源相对于 markdown 的位置
@app.route('/<filename>-markdown', methods=['GET'])
def convert_md(filename):
md_file_path = os.path.join(MARKDOWN_DIR, f'{filename}.md')
if not os.path.exists(md_file_path):
return jsonify({'error': 'Markdown file not found'}), 404
# 读取 markdown 文件内容
with open(md_file_path, 'r', encoding='utf-8') as file:
md_content = file.read()
# 将 markdown 转换为 HTML
html_content = markdown.markdown(md_content)
# 插入 CSS 样式，限制图片最大宽度为 100%，高度自动调整
html_with_styles = f"""
<html>
<head>
<style>
img {{
max-width: 100%;
height: auto;
}}
</style>
</head>
<body>
{html_content}
</body>
</html>
"""
# 保存 HTML 文件到 static 文件夹
html_output_path = os.path.join(STATIC_DIR, f'{filename}.html')
with open(html_output_path, 'w', encoding='utf-8') as html_file:
html_file.write(html_with_styles)
# 处理图片资源，将 image/xxx 文件夹拷贝到 static/image/xxx
image_source_dir = os.path.join(MARKDOWN_DIR, IMAGE_DIR, filename)
image_target_dir = os.path.join(STATIC_DIR, IMAGE_DIR, filename)
print(f"Copying image resources from {image_source_dir} to {image_target_dir}")
if os.path.exists(image_source_dir):
# 确保 static/image/xxx 目录存在
if not os.path.exists(image_target_dir):
os.makedirs(image_target_dir)
# 拷贝图片资源
for image_file in os.listdir(image_source_dir):
full_image_file_path = os.path.join(image_source_dir, image_file)
if os.path.isfile(full_image_file_path):
shutil.copy(full_image_file_path, image_target_dir)
return jsonify({'html_url': f'/static/{filename}.html'})
# 静态文件（HTML 和图片资源）的访问
@app.route('/static/<path:filename>')
def serve_static(filename):
return send_from_directory(STATIC_DIR, filename)
'''
Vscode
'''
@app.route('/desktop/<user_id>/<path>')
def hello_world(user_id, path):
if 'user_id' not in session:
session['user_id'] = 'user_' + str(uuid.uuid4())
userid_recorder[user_id+'&'+path] = session['user_id']
# 在上层 目录下创建一个名为 user_id_path 的文件夹
path_dir = os.path.join(parent_dir, user_id, path)
os.makedirs(path_dir, exist_ok=True)
# 在此文件夹内部创建一个.config文件，并写入 user_id=user_id\n path=path
config_path = os.path.join(path_dir, '.config')
with open(config_path, 'w') as f:
f.write(f"user_id={user_id}\npath={path}")
return render_template('index.html', user_id=user_id, path=path)
@app.route('/vscode_data', methods=['POST'])
def vscode_data():
data = request.json
config = data['config']
realtime_response(config,data)
print(f"Received data from VSCode: {data}")
return jsonify({"status": "success", "received": data})
@app.route('/get_session')
def get_session():
user_session = session.get('user_id', 'default_session')
return jsonify({"session": user_session})
if __name__ == '__main__':
socketio.run(app, host='0.0.0.0', port=5500)

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import time
class BackBoardManager:
def __init__(self):
self.backboards = {} # user_id: Backboard
def add_backboard(self, user_id, folder):
self.backboards[user_id] = Backboard(user_id, folder)
def get_backboard(self, user_id):
if user_id not in self.backboards:
return None
return self.backboards[user_id]
class Backboard:
def __init__(self, user_id, folder):
self.user_id = user_id
self.folder = folder
self.create_time = time.time()
self.enter_chapter_time = time.time()
def next_chapter(self):
self.enter_chapter_time = time.time()
def get_info_prompt(self):
return f"###Global Info:###\n\
User is {self.user_id}\n\
User's total study time is {self.get_deltatime_mmss(self.create_time, time.time())}\n\
User's current chapter study time is {self.get_deltatime_mmss(self.enter_chapter_time, time.time())}\n\
\n\n\
"
def get_deltatime_mmss(self, start_time, end_time):
elapsed_time = end_time - start_time
hours = int(elapsed_time // 3600)
minutes = int((elapsed_time % 3600) // 60)
seconds = int(elapsed_time % 60)
return f"{hours:02d}:{minutes:02d}:{seconds:02d}"

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1
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@@ -1 +0,0 @@
2024-11-14 22:13:39.282 | INFO | agentscope.models:read_model_configs:186 - Load configs for model wrapper: openai_cfg

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@@ -1,7 +0,0 @@
{
"project": "Dyp9cf",
"name": "rqje7y",
"run_id": "run_20241114-221631_rqje7y",
"timestamp": "2024-11-14 22:16:31",
"pid": 31289
}

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from flask import Flask, session, request, jsonify, render_template, send_from_directory
from flask_cors import CORS
from flask_socketio import SocketIO, join_room, emit
import markdown
import os
import uuid
import shutil
import sys
import json
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.insert(0, parent_dir)
current_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '.'))
sys.path.insert(0, current_dir)
from AlgoriAgent.projects.algoriAgent.agent_manager import AgentManager
app = Flask(__name__)
app.secret_key = 'cakebaker'
# 配置 CORS
CORS(app, resources={r"/*": {"origins": "http://localhost:9888"}}, supports_credentials=True)
userid_recorder = {} # user_id&path -> session['user_id']
'''
Backboard
'''
from backboardManager import BackBoardManager
backboard_manager = BackBoardManager()
@app.route('/backboard')
def backboard():
return jsonify({'status': 'success'})
'''
Agent and Chat
'''
socketio = SocketIO(app, cors_allowed_origins="*")
agent_manager = AgentManager()
def realtime_response(config, realtime_action):
print("config"+str(config))
print("realtime_action"+str(realtime_action))
pass
@socketio.on('connect')
def handle_connect():
user = request.args.get('username')
folder_name = request.args.get('folder')
user_id = userid_recorder[user+'&'+folder_name]
session['user_id'] = user_id
print(f'User connected with session user_id: {user_id}')
# 从markdown、markdown_prompts、score_prompts中各读取数据folder.md并进行new_agent
with open(f'books/markdown/{folder_name}.md','r') as fmd,\
open(f'books/markdown_prompts/{folder_name}.md','r')as fmdp,\
open(f'books/score_prompts/{folder_name}.md','r') as fsp:
markdown = fmd.read()
markdown_prompts = fmdp.read()
score_prompts = fsp.read()
print('-----------------------')
print(user_id)
user_id, agent = agent_manager.new_agent(markdown, markdown_prompts, score_prompts, id=user_id)
join_room(user_id) # 将该用户加入以user_id为名的room
# 当接收到前端的消息时，触发此事件
@socketio.on('send_message')
def handle_message(data):
print(f"Message from client: {data}")
id = session.get('user_id')
# 构造回复消息
res = agent_manager.invoke(id, data)
reply = f"AI: {res.content['speak']}"
# 将回复发送回前端
emit('receive_message', reply)
'''
Markdown to HTML
'''
# 配置文件路径
MARKDOWN_DIR = 'books/markdown' # 存放 markdown 文件的文件夹
STATIC_DIR = 'static' # 存放生成的 HTML 和图片资源的文件夹
IMAGE_DIR = 'image' # 图片资源相对于 markdown 的位置
@app.route('/<filename>-markdown', methods=['GET'])
def convert_md(filename):
md_file_path = os.path.join(MARKDOWN_DIR, f'{filename}.md')
if not os.path.exists(md_file_path):
return jsonify({'error': 'Markdown file not found'}), 404
# 读取 markdown 文件内容
with open(md_file_path, 'r', encoding='utf-8') as file:
md_content = file.read()
# 将 markdown 转换为 HTML
html_content = markdown.markdown(md_content)
# 插入 CSS 样式，限制图片最大宽度为 100%，高度自动调整
html_with_styles = f"""
<html>
<head>
<style>
img {{
max-width: 100%;
height: auto;
}}
</style>
</head>
<body>
{html_content}
</body>
</html>
"""
# 保存 HTML 文件到 static 文件夹
html_output_path = os.path.join(STATIC_DIR, f'{filename}.html')
with open(html_output_path, 'w', encoding='utf-8') as html_file:
html_file.write(html_with_styles)
# 处理图片资源，将 image/xxx 文件夹拷贝到 static/image/xxx
image_source_dir = os.path.join(MARKDOWN_DIR, IMAGE_DIR, filename)
image_target_dir = os.path.join(STATIC_DIR, IMAGE_DIR, filename)
print(f"Copying image resources from {image_source_dir} to {image_target_dir}")
if os.path.exists(image_source_dir):
# 确保 static/image/xxx 目录存在
if not os.path.exists(image_target_dir):
os.makedirs(image_target_dir)
# 拷贝图片资源
for image_file in os.listdir(image_source_dir):
full_image_file_path = os.path.join(image_source_dir, image_file)
if os.path.isfile(full_image_file_path):
shutil.copy(full_image_file_path, image_target_dir)
return jsonify({'html_url': f'/static/{filename}.html'})
# 静态文件（HTML 和图片资源）的访问
@app.route('/static/<path:filename>')
def serve_static(filename):
return send_from_directory(STATIC_DIR, filename)
'''
Vscode
'''
@app.route('/desktop/<user_id>/<path>')
def hello_world(user_id, path):
if 'user_id' not in session:
session['user_id'] = 'user_' + str(uuid.uuid4())
userid_recorder[user_id+'&'+path] = session['user_id']
# 在上层 目录下创建一个名为 user_id_path 的文件夹
path_dir = os.path.join(parent_dir, user_id, path)
os.makedirs(path_dir, exist_ok=True)
# 在此文件夹内部创建一个.config文件，并写入 user_id=user_id\n path=path
config_path = os.path.join(path_dir, '.config')
with open(config_path, 'w') as f:
f.write(f"user_id={user_id}\npath={path}")
return render_template('index.html', user_id=user_id, path=path)
@app.route('/vscode_data', methods=['POST'])
def vscode_data():
data = request.json
config = data['config']
realtime_response(config,data)
print(f"Received data from VSCode: {data}")
return jsonify({"status": "success", "received": data})
@app.route('/get_session')
def get_session():
user_session = session.get('user_id', 'default_session')
return jsonify({"session": user_session})
if __name__ == '__main__':
socketio.run(app, host='0.0.0.0', port=5500)

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@@ -1,51 +0,0 @@
import time
class BackBoardManager:
def __init__(self):
self.backboards = {} # user_id: Backboard
def add_backboard(self, user_id, folder):
self.backboards[user_id] = Backboard(user_id, folder)
def get_backboard(self, user_id):
if user_id not in self.backboards:
return None
return self.backboards[user_id]
class Backboard:
def __init__(self, user_id, folder):
self.user_id = user_id
self.folder = folder
self.create_time = time.time()
self.enter_chapter_time = time.time()
def next_chapter(self):
self.enter_chapter_time = time.time()
def get_info_prompt(self):
return f"###Global Info:###\n\
User is {self.user_id}\n\
User's total study time is {self.get_deltatime_mmss(self.create_time, time.time())}\n\
User's current chapter study time is {self.get_deltatime_mmss(self.enter_chapter_time, time.time())}\n\
\n\n\
"
def get_deltatime_mmss(self, start_time, end_time):
elapsed_time = end_time - start_time
hours = int(elapsed_time // 3600)
minutes = int((elapsed_time % 3600) // 60)
seconds = int(elapsed_time % 60)
return f"{hours:02d}:{minutes:02d}:{seconds:02d}"

9
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@@ -1,9 +0,0 @@
{"id": "634c922077c342c297ab1eccea71c77e", "timestamp": "2024-11-14 22:17:54", "name": "assistant", "content": "##################### ITER 1, STEP 1: REASONING ######################", "role": "assistant", "url": null, "metadata": null}
{"id": "d08bda7bbbd748f9875913304466e4bd", "timestamp": "2024-11-14 22:17:54", "name": "system", "content": "", "role": "system", "url": null, "metadata": null}
{"id": "aec083afdf9b4d47b48b780ef9dc1b3d", "timestamp": "2024-11-14 22:17:54", "name": "system", "content": "Respond a JSON dictionary in a markdown's fenced code block as follows:\n```json\n{\"thought\": \"what you thought\", \"speak\": \"what you speak\", \"function\": \"[{\\\"name\\\": \\\"{function name}\\\", \\\"arguments\\\": {\\\"{argument1 name}\\\": xxx, \\\"{argument2 name}\\\": xxx}}]\"}\n```\n ####", "role": "system", "url": null, "metadata": null}
{"id": "aad82dbb9b8640139dc08f854c269259", "timestamp": "2024-11-14 22:17:54", "name": "assistant", "content": "################################Prompt################################", "role": "assistant", "url": null, "metadata": null}
{"id": "1a3eb9af6c4f4cbebb0e3ed480bd69cc", "timestamp": "2024-11-14 22:17:54", "name": "assistant", "content": "你好\n", "role": "assistant", "url": null, "metadata": null}
{"id": "311ab2b2b08649ae81124ee10901d1d8", "timestamp": "2024-11-14 22:17:58", "name": "assistant", "content": "############################Result Parsed#############################", "role": "assistant", "url": null, "metadata": null}
{"id": "737dcdff4adf441d86532e25d4d9ee32", "timestamp": "2024-11-14 22:17:58", "name": "assistant", "content": "{'thought': 'The student is greeting, indicating they are ready to learn about binary search.', 'speak': '你好！今天我们将学习有关二分查找的基本概念。如果你有问题，可以随时问我。我们先来思考一下，1到4000之间的区间最差情况下需要猜测几次？', 'function': []}", "role": "assistant", "url": null, "metadata": null}
{"id": "5dd3c37d27d544928ba8f065b3fedd4a", "timestamp": "2024-11-14 22:17:58", "name": "assistant", "content": "################################Speak#################################", "role": "assistant", "url": null, "metadata": null}
{"id": "69861e706dc941b1b60626af8b049ee5", "timestamp": "2024-11-14 22:17:58", "name": "assistant", "content": {"thought": "The student is greeting, indicating they are ready to learn about binary search.", "speak": "你好！今天我们将学习有关二分查找的基本概念。如果你有问题，可以随时问我。我们先来思考一下，1到4000之间的区间最差情况下需要猜测几次？", "function": []}, "role": "assistant", "url": null, "metadata": null}

23
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@@ -1,23 +0,0 @@
2024-11-14 22:16:42.016 | INFO | agentscope.models:read_model_configs:186 - Load configs for model wrapper: openai_cfg
2024-11-14 22:16:49.610 | INFO | agentscope.models.model:__init__:201 - Initialize model by configuration [openai_cfg]
2024-11-14 22:16:49.626 | WARNING | agentscope.models.openai_model:__init__:83 - fail to get max_length for gpt-4o-mini: 'Model [gpt-4o-mini] not found in OPENAI_MAX_LENGTH. The last updated date is 20231212'
2024-11-14 22:16:49.626 | INFO | agentscope.utils.monitor:register_budget:609 - set budget None to gpt-4o-mini
2024-11-14 22:16:49.627 | WARNING | agentscope.utils.monitor:register_budget:639 - Calculate budgets for model [gpt-4o-mini] is not supported
2024-11-14 22:16:49.642 | INFO | agentscope.utils.monitor:register:417 - Register metric [gpt-4o-mini.call_counter] to SqliteMonitor with unit [times] and quota [None]
2024-11-14 22:16:49.666 | INFO | agentscope.utils.monitor:register:417 - Register metric [gpt-4o-mini.prompt_tokens] to SqliteMonitor with unit [token] and quota [None]
2024-11-14 22:16:49.695 | INFO | agentscope.utils.monitor:register:417 - Register metric [gpt-4o-mini.completion_tokens] to SqliteMonitor with unit [token] and quota [None]
2024-11-14 22:16:49.721 | INFO | agentscope.utils.monitor:register:417 - Register metric [gpt-4o-mini.total_tokens] to SqliteMonitor with unit [token] and quota [None]
assistant: ##################### ITER 1, STEP 1: REASONING ######################
system:
system: Respond a JSON dictionary in a markdown's fenced code block as follows:
```json
{"thought": "what you thought", "speak": "what you speak", "function": "[{\"name\": \"{function name}\", \"arguments\": {\"{argument1 name}\": xxx, \"{argument2 name}\": xxx}}]"}
```
####
assistant: ################################Prompt################################
assistant: 你好
assistant: ############################Result Parsed#############################
assistant: {'thought': 'The student is greeting, indicating they are ready to learn about binary search.', 'speak': '你好！今天我们将学习有关二分查找的基本概念。如果你有问题，可以随时问我。我们先来思考一下，1到4000之间的区间最差情况下需要猜测几次？', 'function': []}
assistant: ################################Speak#################################
assistant: {'thought': 'The student is greeting, indicating they are ready to learn about binary search.', 'speak': '你好！今天我们将学习有关二分查找的基本概念。如果你有问题，可以随时问我。我们先来思考一下，1到4000之间的区间最差情况下需要猜测几次？', 'function': []}

7
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@@ -1,7 +0,0 @@
{
"project": "5tV7eR",
"name": "1b3pyz",
"run_id": "run_20241122-165210_1b3pyz",
"timestamp": "2024-11-22 16:52:10",
"pid": 10044
}

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@@ -1,184 +0,0 @@
from flask import Flask, session, request, jsonify, render_template, send_from_directory
from flask_cors import CORS
from flask_socketio import SocketIO, join_room, emit
import markdown
import os
import uuid
import shutil
import sys
import json
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.insert(0, parent_dir)
current_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '.'))
sys.path.insert(0, current_dir)
from AlgoriAgent.projects.algoriAgent.agent_manager import AgentManager
app = Flask(__name__)
app.secret_key = 'cakebaker'
# 配置 CORS
CORS(app, resources={r"/*": {"origins": "http://localhost:9888"}}, supports_credentials=True)
userid_recorder = {} # user_id&path -> session['user_id']
'''
Backboard
'''
from backboardManager import BackBoardManager
backboard_manager = BackBoardManager()
@app.route('/backboard')
def backboard():
return jsonify({'status': 'success'})
'''
Agent and Chat
'''
socketio = SocketIO(app, cors_allowed_origins="*")
agent_manager = AgentManager()
def realtime_response(config, realtime_action):
print("config"+str(config))
print("realtime_action"+str(realtime_action))
pass
@socketio.on('connect')
def handle_connect():
user = request.args.get('username')
folder_name = request.args.get('folder')
user_id = userid_recorder[user+'&'+folder_name]
session['user_id'] = user_id
print(f'User connected with session user_id: {user_id}')
# 从markdown、markdown_prompts、score_prompts中各读取数据folder.md并进行new_agent
with open(f'books/markdown/{folder_name}.md','r') as fmd,\
open(f'books/markdown_prompts/{folder_name}.md','r')as fmdp,\
open(f'books/score_prompts/{folder_name}.md','r') as fsp:
markdown = fmd.read()
markdown_prompts = fmdp.read()
score_prompts = fsp.read()
print('-----------------------')
print(user_id)
user_id, agent = agent_manager.new_agent(markdown, markdown_prompts, score_prompts, id=user_id)
join_room(user_id) # 将该用户加入以user_id为名的room
# 当接收到前端的消息时，触发此事件
@socketio.on('send_message')
def handle_message(data):
print(f"Message from client: {data}")
id = session.get('user_id')
# 构造回复消息
res = agent_manager.invoke(id, data)
reply = f"AI: {res.content['speak']}"
# 将回复发送回前端
emit('receive_message', reply)
'''
Markdown to HTML
'''
# 配置文件路径
MARKDOWN_DIR = 'books/markdown' # 存放 markdown 文件的文件夹
STATIC_DIR = 'static' # 存放生成的 HTML 和图片资源的文件夹
IMAGE_DIR = 'image' # 图片资源相对于 markdown 的位置
@app.route('/<filename>-markdown', methods=['GET'])
def convert_md(filename):
md_file_path = os.path.join(MARKDOWN_DIR, f'{filename}.md')
if not os.path.exists(md_file_path):
return jsonify({'error': 'Markdown file not found'}), 404
# 读取 markdown 文件内容
with open(md_file_path, 'r', encoding='utf-8') as file:
md_content = file.read()
# 将 markdown 转换为 HTML
html_content = markdown.markdown(md_content)
# 插入 CSS 样式，限制图片最大宽度为 100%，高度自动调整
html_with_styles = f"""
<html>
<head>
<style>
img {{
max-width: 100%;
height: auto;
}}
</style>
</head>
<body>
{html_content}
</body>
</html>
"""
# 保存 HTML 文件到 static 文件夹
html_output_path = os.path.join(STATIC_DIR, f'{filename}.html')
with open(html_output_path, 'w', encoding='utf-8') as html_file:
html_file.write(html_with_styles)
# 处理图片资源，将 image/xxx 文件夹拷贝到 static/image/xxx
image_source_dir = os.path.join(MARKDOWN_DIR, IMAGE_DIR, filename)
image_target_dir = os.path.join(STATIC_DIR, IMAGE_DIR, filename)
print(f"Copying image resources from {image_source_dir} to {image_target_dir}")
if os.path.exists(image_source_dir):
# 确保 static/image/xxx 目录存在
if not os.path.exists(image_target_dir):
os.makedirs(image_target_dir)
# 拷贝图片资源
for image_file in os.listdir(image_source_dir):
full_image_file_path = os.path.join(image_source_dir, image_file)
if os.path.isfile(full_image_file_path):
shutil.copy(full_image_file_path, image_target_dir)
return jsonify({'html_url': f'/static/{filename}.html'})
# 静态文件（HTML 和图片资源）的访问
@app.route('/static/<path:filename>')
def serve_static(filename):
return send_from_directory(STATIC_DIR, filename)
'''
Vscode
'''
@app.route('/desktop/<user_id>/<path>')
def hello_world(user_id, path):
if 'user_id' not in session:
session['user_id'] = 'user_' + str(uuid.uuid4())
userid_recorder[user_id+'&'+path] = session['user_id']
# 在上层 目录下创建一个名为 user_id_path 的文件夹
path_dir = os.path.join(parent_dir, user_id, path)
os.makedirs(path_dir, exist_ok=True)
# 在此文件夹内部创建一个.config文件，并写入 user_id=user_id\n path=path
config_path = os.path.join(path_dir, '.config')
with open(config_path, 'w') as f:
f.write(f"user_id={user_id}\npath={path}")
return render_template('index.html', user_id=user_id, path=path)
@app.route('/vscode_data', methods=['POST'])
def vscode_data():
data = request.json
config = data['config']
realtime_response(config,data)
print(f"Received data from VSCode: {data}")
return jsonify({"status": "success", "received": data})
@app.route('/get_session')
def get_session():
user_session = session.get('user_id', 'default_session')
return jsonify({"session": user_session})
if __name__ == '__main__':
socketio.run(app, host='0.0.0.0', port=5500)

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@@ -1,51 +0,0 @@
import time
class BackBoardManager:
def __init__(self):
self.backboards = {} # user_id: Backboard
def add_backboard(self, user_id, folder):
self.backboards[user_id] = Backboard(user_id, folder)
def get_backboard(self, user_id):
if user_id not in self.backboards:
return None
return self.backboards[user_id]
class Backboard:
def __init__(self, user_id, folder):
self.user_id = user_id
self.folder = folder
self.create_time = time.time()
self.enter_chapter_time = time.time()
def next_chapter(self):
self.enter_chapter_time = time.time()
def get_info_prompt(self):
return f"###Global Info:###\n\
User is {self.user_id}\n\
User's total study time is {self.get_deltatime_mmss(self.create_time, time.time())}\n\
User's current chapter study time is {self.get_deltatime_mmss(self.enter_chapter_time, time.time())}\n\
\n\n\
"
def get_deltatime_mmss(self, start_time, end_time):
elapsed_time = end_time - start_time
hours = int(elapsed_time // 3600)
minutes = int((elapsed_time % 3600) // 60)
seconds = int(elapsed_time % 60)
return f"{hours:02d}:{minutes:02d}:{seconds:02d}"

0
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13
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@@ -1,13 +0,0 @@
2024-11-22 16:52:16.843 | INFO | agentscope.models:read_model_configs:186 - Load configs for model wrapper: openai_cfg
2024-11-22 16:53:07.868 | INFO | agentscope.models.model:__init__:201 - Initialize model by configuration [openai_cfg]
2024-11-22 16:53:07.885 | WARNING | agentscope.models.openai_model:__init__:83 - fail to get max_length for gpt-4o-mini: 'Model [gpt-4o-mini] not found in OPENAI_MAX_LENGTH. The last updated date is 20231212'
2024-11-22 16:53:07.885 | INFO | agentscope.utils.monitor:register_budget:609 - set budget None to gpt-4o-mini
2024-11-22 16:53:07.885 | WARNING | agentscope.utils.monitor:register_budget:639 - Calculate budgets for model [gpt-4o-mini] is not supported
2024-11-22 16:53:07.896 | INFO | agentscope.utils.monitor:register:417 - Register metric [gpt-4o-mini.call_counter] to SqliteMonitor with unit [times] and quota [None]
2024-11-22 16:53:07.915 | INFO | agentscope.utils.monitor:register:417 - Register metric [gpt-4o-mini.prompt_tokens] to SqliteMonitor with unit [token] and quota [None]
2024-11-22 16:53:07.934 | INFO | agentscope.utils.monitor:register:417 - Register metric [gpt-4o-mini.completion_tokens] to SqliteMonitor with unit [token] and quota [None]
2024-11-22 16:53:07.953 | INFO | agentscope.utils.monitor:register:417 - Register metric [gpt-4o-mini.total_tokens] to SqliteMonitor with unit [token] and quota [None]
2024-11-22 16:56:58.801 | INFO | agentscope.models.model:__init__:201 - Initialize model by configuration [openai_cfg]
2024-11-22 16:56:58.816 | WARNING | agentscope.models.openai_model:__init__:83 - fail to get max_length for gpt-4o-mini: 'Model [gpt-4o-mini] not found in OPENAI_MAX_LENGTH. The last updated date is 20231212'
2024-11-22 16:56:58.817 | INFO | agentscope.utils.monitor:register_budget:609 - set budget None to gpt-4o-mini
2024-11-22 16:56:58.818 | WARNING | agentscope.utils.monitor:register_budget:639 - Calculate budgets for model [gpt-4o-mini] is not supported

7
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@@ -1,7 +0,0 @@
{
"project": "ZxUA2M",
"name": "vdg020",
"run_id": "run_20241122-165801_vdg020",
"timestamp": "2024-11-22 16:58:01",
"pid": 13329
}

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from flask import Flask, session, request, jsonify, render_template, send_from_directory
from flask_cors import CORS
from flask_socketio import SocketIO, join_room, emit
import markdown
import os
import uuid
import shutil
import sys
import json
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.insert(0, parent_dir)
current_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '.'))
sys.path.insert(0, current_dir)
from AlgoriAgent.projects.algoriAgent.agent_manager import AgentManager
app = Flask(__name__)
app.secret_key = 'cakebaker'
# 配置 CORS
CORS(app, resources={r"/*": {"origins": "http://localhost:9888"}}, supports_credentials=True)
userid_recorder = {} # user_id&path -> session['user_id']
'''
Backboard
'''
from backboardManager import BackBoardManager
backboard_manager = BackBoardManager()
@app.route('/backboard')
def backboard():
return jsonify({'status': 'success'})
'''
Agent and Chat
'''
socketio = SocketIO(app, cors_allowed_origins="*")
agent_manager = AgentManager()
def realtime_response(config, realtime_action):
print("config"+str(config))
print("realtime_action"+str(realtime_action))
pass
@socketio.on('connect')
def handle_connect():
user = request.args.get('username')
folder_name = request.args.get('folder')
user_id = userid_recorder[user+'&'+folder_name]
session['user_id'] = user_id
print(f'User connected with session user_id: {user_id}')
# 从markdown、markdown_prompts、score_prompts中各读取数据folder.md并进行new_agent
with open(f'books/markdown/{folder_name}.md','r') as fmd,\
open(f'books/markdown_prompts/{folder_name}.md','r')as fmdp,\
open(f'books/score_prompts/{folder_name}.md','r') as fsp:
markdown = fmd.read()
markdown_prompts = fmdp.read()
score_prompts = fsp.read()
print('-----------------------')
print(user_id)
user_id, agent = agent_manager.new_agent(markdown, markdown_prompts, score_prompts, id=user_id)
join_room(user_id) # 将该用户加入以user_id为名的room
# 当接收到前端的消息时，触发此事件
@socketio.on('send_message')
def handle_message(data):
print(f"Message from client: {data}")
id = session.get('user_id')
# 构造回复消息
res = agent_manager.invoke(id, data)
reply = f"AI: {res.content['speak']}"
# 将回复发送回前端
emit('receive_message', reply)
'''
Markdown to HTML
'''
# 配置文件路径
MARKDOWN_DIR = 'books/markdown' # 存放 markdown 文件的文件夹
STATIC_DIR = 'static' # 存放生成的 HTML 和图片资源的文件夹
IMAGE_DIR = 'image' # 图片资源相对于 markdown 的位置
@app.route('/<filename>-markdown', methods=['GET'])
def convert_md(filename):
md_file_path = os.path.join(MARKDOWN_DIR, f'{filename}.md')
if not os.path.exists(md_file_path):
return jsonify({'error': 'Markdown file not found'}), 404
# 读取 markdown 文件内容
with open(md_file_path, 'r', encoding='utf-8') as file:
md_content = file.read()
# 将 markdown 转换为 HTML
html_content = markdown.markdown(md_content)
# 插入 CSS 样式，限制图片最大宽度为 100%，高度自动调整
html_with_styles = f"""
<html>
<head>
<style>
img {{
max-width: 100%;
height: auto;
}}
</style>
</head>
<body>
{html_content}
</body>
</html>
"""
# 保存 HTML 文件到 static 文件夹
html_output_path = os.path.join(STATIC_DIR, f'{filename}.html')
with open(html_output_path, 'w', encoding='utf-8') as html_file:
html_file.write(html_with_styles)
# 处理图片资源，将 image/xxx 文件夹拷贝到 static/image/xxx
image_source_dir = os.path.join(MARKDOWN_DIR, IMAGE_DIR, filename)
image_target_dir = os.path.join(STATIC_DIR, IMAGE_DIR, filename)
print(f"Copying image resources from {image_source_dir} to {image_target_dir}")
if os.path.exists(image_source_dir):
# 确保 static/image/xxx 目录存在
if not os.path.exists(image_target_dir):
os.makedirs(image_target_dir)
# 拷贝图片资源
for image_file in os.listdir(image_source_dir):
full_image_file_path = os.path.join(image_source_dir, image_file)
if os.path.isfile(full_image_file_path):
shutil.copy(full_image_file_path, image_target_dir)
return jsonify({'html_url': f'/static/{filename}.html'})
# 静态文件（HTML 和图片资源）的访问
@app.route('/static/<path:filename>')
def serve_static(filename):
return send_from_directory(STATIC_DIR, filename)
'''
Vscode
'''
@app.route('/desktop/<user_id>/<path>')
def hello_world(user_id, path):
if 'user_id' not in session:
session['user_id'] = 'user_' + str(uuid.uuid4())
userid_recorder[user_id+'&'+path] = session['user_id']
# 在上层 目录下创建一个名为 user_id_path 的文件夹
path_dir = os.path.join(parent_dir, user_id, path)
os.makedirs(path_dir, exist_ok=True)
# 在此文件夹内部创建一个.config文件，并写入 user_id=user_id\n path=path
config_path = os.path.join(path_dir, '.config')
with open(config_path, 'w') as f:
f.write(f"user_id={user_id}\npath={path}")
return render_template('index.html', user_id=user_id, path=path)
@app.route('/vscode_data', methods=['POST'])
def vscode_data():
data = request.json
config = data['config']
realtime_response(config,data)
print(f"Received data from VSCode: {data}")
return jsonify({"status": "success", "received": data})
@app.route('/get_session')
def get_session():
user_session = session.get('user_id', 'default_session')
return jsonify({"session": user_session})
if __name__ == '__main__':
socketio.run(app, host='0.0.0.0', port=5500)

51
Html/runs/run_20241122-165801_vdg020/code/backboardManager.py
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@@ -1,51 +0,0 @@
import time
class BackBoardManager:
def __init__(self):
self.backboards = {} # user_id: Backboard
def add_backboard(self, user_id, folder):
self.backboards[user_id] = Backboard(user_id, folder)
def get_backboard(self, user_id):
if user_id not in self.backboards:
return None
return self.backboards[user_id]
class Backboard:
def __init__(self, user_id, folder):
self.user_id = user_id
self.folder = folder
self.create_time = time.time()
self.enter_chapter_time = time.time()
def next_chapter(self):
self.enter_chapter_time = time.time()
def get_info_prompt(self):
return f"###Global Info:###\n\
User is {self.user_id}\n\
User's total study time is {self.get_deltatime_mmss(self.create_time, time.time())}\n\
User's current chapter study time is {self.get_deltatime_mmss(self.enter_chapter_time, time.time())}\n\
\n\n\
"
def get_deltatime_mmss(self, start_time, end_time):
elapsed_time = end_time - start_time
hours = int(elapsed_time // 3600)
minutes = int((elapsed_time % 3600) // 60)
seconds = int(elapsed_time % 60)
return f"{hours:02d}:{minutes:02d}:{seconds:02d}"

9
Html/runs/run_20241122-165801_vdg020/logging.chat
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@@ -1,9 +0,0 @@
{"id": "906c60e694624e90be9225ceeb24c32d", "timestamp": "2024-11-22 17:07:22", "name": "assistant", "content": "##################### ITER 1, STEP 1: REASONING ######################", "role": "assistant", "url": null, "metadata": null}
{"id": "95fd4f1910984ca3b73c83ae61b8e7b4", "timestamp": "2024-11-22 17:07:22", "name": "system", "content": "", "role": "system", "url": null, "metadata": null}
{"id": "ae1e2966378b4bd5aba7d2410488d49f", "timestamp": "2024-11-22 17:07:22", "name": "system", "content": "Respond a JSON dictionary in a markdown's fenced code block as follows:\n```json\n{\"thought\": \"what you thought\", \"speak\": \"what you speak\", \"function\": \"[{\\\"name\\\": \\\"{function name}\\\", \\\"arguments\\\": {\\\"{argument1 name}\\\": xxx, \\\"{argument2 name}\\\": xxx}}]\"}\n```\n ####", "role": "system", "url": null, "metadata": null}
{"id": "87c816f6514e422b9afe2ddea274030f", "timestamp": "2024-11-22 17:07:22", "name": "assistant", "content": "################################Prompt################################", "role": "assistant", "url": null, "metadata": null}
{"id": "fa220e3ad1db4cd7adc15ec1e39525b0", "timestamp": "2024-11-22 17:07:22", "name": "assistant", "content": "hi", "role": "assistant", "url": null, "metadata": null}
{"id": "c1dc60dfa9554247b7c8d3870d20353d", "timestamp": "2024-11-22 17:07:26", "name": "assistant", "content": "############################Result Parsed#############################", "role": "assistant", "url": null, "metadata": null}
{"id": "fdc19da0a6004c9cb4c03fbecab6b6a8", "timestamp": "2024-11-22 17:07:26", "name": "assistant", "content": "{'thought': 'The student is greeting, indicating they might be ready to discuss the current chapter.', 'speak': \"Hello! Let's focus on Chapter 1 about binary search. Do you have any questions about the guessing game scenario?\", 'function': []}", "role": "assistant", "url": null, "metadata": null}
{"id": "344dabc6a6ef40eabf8d7c88286b5ab9", "timestamp": "2024-11-22 17:07:26", "name": "assistant", "content": "################################Speak#################################", "role": "assistant", "url": null, "metadata": null}
{"id": "3d86a502943c4d63ae81ee62b5af06c1", "timestamp": "2024-11-22 17:07:26", "name": "assistant", "content": {"thought": "The student is greeting, indicating they might be ready to discuss the current chapter.", "speak": "Hello! Let's focus on Chapter 1 about binary search. Do you have any questions about the guessing game scenario?", "function": []}, "role": "assistant", "url": null, "metadata": null}

22
Html/runs/run_20241122-165801_vdg020/logging.log
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@@ -1,22 +0,0 @@
2024-11-22 16:58:14.249 | INFO | agentscope.models:read_model_configs:186 - Load configs for model wrapper: openai_cfg
2024-11-22 16:58:21.157 | INFO | agentscope.models.model:__init__:201 - Initialize model by configuration [openai_cfg]
2024-11-22 16:58:21.172 | WARNING | agentscope.models.openai_model:__init__:83 - fail to get max_length for gpt-4o-mini: 'Model [gpt-4o-mini] not found in OPENAI_MAX_LENGTH. The last updated date is 20231212'
2024-11-22 16:58:21.173 | INFO | agentscope.utils.monitor:register_budget:609 - set budget None to gpt-4o-mini
2024-11-22 16:58:21.173 | WARNING | agentscope.utils.monitor:register_budget:639 - Calculate budgets for model [gpt-4o-mini] is not supported
2024-11-22 16:58:21.186 | INFO | agentscope.utils.monitor:register:417 - Register metric [gpt-4o-mini.call_counter] to SqliteMonitor with unit [times] and quota [None]
2024-11-22 16:58:21.208 | INFO | agentscope.utils.monitor:register:417 - Register metric [gpt-4o-mini.prompt_tokens] to SqliteMonitor with unit [token] and quota [None]
2024-11-22 16:58:21.233 | INFO | agentscope.utils.monitor:register:417 - Register metric [gpt-4o-mini.completion_tokens] to SqliteMonitor with unit [token] and quota [None]
2024-11-22 16:58:21.257 | INFO | agentscope.utils.monitor:register:417 - Register metric [gpt-4o-mini.total_tokens] to SqliteMonitor with unit [token] and quota [None]
assistant: ##################### ITER 1, STEP 1: REASONING ######################
system:
system: Respond a JSON dictionary in a markdown's fenced code block as follows:
```json
{"thought": "what you thought", "speak": "what you speak", "function": "[{\"name\": \"{function name}\", \"arguments\": {\"{argument1 name}\": xxx, \"{argument2 name}\": xxx}}]"}
```
####
assistant: ################################Prompt################################
assistant: hi
assistant: ############################Result Parsed#############################
assistant: {'thought': 'The student is greeting, indicating they might be ready to discuss the current chapter.', 'speak': "Hello! Let's focus on Chapter 1 about binary search. Do you have any questions about the guessing game scenario?", 'function': []}
assistant: ################################Speak#################################
assistant: {'thought': 'The student is greeting, indicating they might be ready to discuss the current chapter.', 'speak': "Hello! Let's focus on Chapter 1 about binary search. Do you have any questions about the guessing game scenario?", 'function': []}

7
Html/runs/run_20241122-171906_5xj3ck/.config
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@@ -1,7 +0,0 @@
{
"project": "nXZ3hk",
"name": "5xj3ck",
"run_id": "run_20241122-171906_5xj3ck",
"timestamp": "2024-11-22 17:19:06",
"pid": 22751
}

BIN
Html/runs/run_20241122-171906_5xj3ck/agentscope.db
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185
Html/runs/run_20241122-171906_5xj3ck/code/app.py
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@@ -1,185 +0,0 @@
from flask import Flask, session, request, jsonify, render_template, send_from_directory
from flask_cors import CORS
from flask_socketio import SocketIO, join_room, emit
import markdown
import os
import uuid
import shutil
import sys
import json
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.insert(0, parent_dir)
current_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '.'))
sys.path.insert(0, current_dir)
from AlgoriAgent.projects.algoriAgent.agent_manager import AgentManager
app = Flask(__name__)
app.secret_key = 'cakebaker'
# 配置 CORS
CORS(app, resources={r"/*": {"origins": "http://localhost:9888"}}, supports_credentials=True)
userid_recorder = {} # user_id&path -> session['user_id']
'''
Backboard
'''
from backboardManager import BackBoardManager
backboard_manager = BackBoardManager()
@app.route('/backboard')
def backboard():
return jsonify({'status': 'success'})
'''
Agent and Chat
'''
socketio = SocketIO(app, cors_allowed_origins="*")
agent_manager = AgentManager()
def realtime_response(config, realtime_action):
print("config"+str(config))
print("realtime_action"+str(realtime_action))
pass
@socketio.on('connect')
def handle_connect():
user = request.args.get('username')
folder_name = request.args.get('folder')
user_id = userid_recorder[user+'&'+folder_name]
session['user_id'] = user_id
print(f'User connected with session user_id: {user_id}')
# 从markdown、markdown_prompts、score_prompts中各读取数据folder.md并进行new_agent
with open(f'books/markdown/{folder_name}.md','r') as fmd,\
open(f'books/markdown_prompts/{folder_name}.md','r')as fmdp,\
open(f'books/score_prompts/{folder_name}.md','r') as fsp:
markdown = fmd.read()
markdown_prompts = fmdp.read()
score_prompts = fsp.read()
print('-----------------------')
print(user_id)
user_id, agent = agent_manager.new_agent(markdown, markdown_prompts, score_prompts, id=user_id)
join_room(user_id) # 将该用户加入以user_id为名的room
# 当接收到前端的消息时，触发此事件
@socketio.on('send_message')
def handle_message(data):
print(f"Message from client: {data}")
id = session.get('user_id')
# 构造回复消息
res = agent_manager.invoke(id, data)
reply = f"AI: {res.content['speak']}"
# 将回复发送回前端
emit('receive_message', reply)
'''
Markdown to HTML
'''
# 配置文件路径
MARKDOWN_DIR = 'books/markdown' # 存放 markdown 文件的文件夹
STATIC_DIR = 'static' # 存放生成的 HTML 和图片资源的文件夹
IMAGE_DIR = 'image' # 图片资源相对于 markdown 的位置
@app.route('/<filename>-markdown', methods=['GET'])
def convert_md(filename):
md_file_path = os.path.join(MARKDOWN_DIR, f'{filename}.md')
if not os.path.exists(md_file_path):
return jsonify({'error': 'Markdown file not found'}), 404
# 读取 markdown 文件内容
with open(md_file_path, 'r', encoding='utf-8') as file:
md_content = file.read()
# 将 markdown 转换为 HTML
html_content = markdown.markdown(md_content)
# 插入 CSS 样式，限制图片最大宽度为 100%，高度自动调整
html_with_styles = f"""
<html>
<head>
<style>
img {{
max-width: 100%;
height: auto;
}}
</style>
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/github-markdown-css/5.2.0/github-markdown.min.css">
</head>
<body>
{html_content}
</body>
</html>
"""
# 保存 HTML 文件到 static 文件夹
html_output_path = os.path.join(STATIC_DIR, f'{filename}.html')
with open(html_output_path, 'w', encoding='utf-8') as html_file:
html_file.write(html_with_styles)
# 处理图片资源，将 image/xxx 文件夹拷贝到 static/image/xxx
image_source_dir = os.path.join(MARKDOWN_DIR, IMAGE_DIR, filename)
image_target_dir = os.path.join(STATIC_DIR, IMAGE_DIR, filename)
print(f"Copying image resources from {image_source_dir} to {image_target_dir}")
if os.path.exists(image_source_dir):
# 确保 static/image/xxx 目录存在
if not os.path.exists(image_target_dir):
os.makedirs(image_target_dir)
# 拷贝图片资源
for image_file in os.listdir(image_source_dir):
full_image_file_path = os.path.join(image_source_dir, image_file)
if os.path.isfile(full_image_file_path):
shutil.copy(full_image_file_path, image_target_dir)
return jsonify({'html_url': f'/static/{filename}.html'})
# 静态文件（HTML 和图片资源）的访问
@app.route('/static/<path:filename>')
def serve_static(filename):
return send_from_directory(STATIC_DIR, filename)
'''
Vscode
'''
@app.route('/desktop/<user_id>/<path>')
def hello_world(user_id, path):
if 'user_id' not in session:
session['user_id'] = 'user_' + str(uuid.uuid4())
userid_recorder[user_id+'&'+path] = session['user_id']
# 在上层 目录下创建一个名为 user_id_path 的文件夹
path_dir = os.path.join(parent_dir, user_id, path)
os.makedirs(path_dir, exist_ok=True)
# 在此文件夹内部创建一个.config文件，并写入 user_id=user_id\n path=path
config_path = os.path.join(path_dir, '.config')
with open(config_path, 'w') as f:
f.write(f"user_id={user_id}\npath={path}")
return render_template('index.html', user_id=user_id, path=path)
@app.route('/vscode_data', methods=['POST'])
def vscode_data():
data = request.json
config = data['config']
realtime_response(config,data)
print(f"Received data from VSCode: {data}")
return jsonify({"status": "success", "received": data})
@app.route('/get_session')
def get_session():
user_session = session.get('user_id', 'default_session')
return jsonify({"session": user_session})
if __name__ == '__main__':
socketio.run(app, host='0.0.0.0', port=5500)

51
Html/runs/run_20241122-171906_5xj3ck/code/backboardManager.py
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@@ -1,51 +0,0 @@
import time
class BackBoardManager:
def __init__(self):
self.backboards = {} # user_id: Backboard
def add_backboard(self, user_id, folder):
self.backboards[user_id] = Backboard(user_id, folder)
def get_backboard(self, user_id):
if user_id not in self.backboards:
return None
return self.backboards[user_id]
class Backboard:
def __init__(self, user_id, folder):
self.user_id = user_id
self.folder = folder
self.create_time = time.time()
self.enter_chapter_time = time.time()
def next_chapter(self):
self.enter_chapter_time = time.time()
def get_info_prompt(self):
return f"###Global Info:###\n\
User is {self.user_id}\n\
User's total study time is {self.get_deltatime_mmss(self.create_time, time.time())}\n\
User's current chapter study time is {self.get_deltatime_mmss(self.enter_chapter_time, time.time())}\n\
\n\n\
"
def get_deltatime_mmss(self, start_time, end_time):
elapsed_time = end_time - start_time
hours = int(elapsed_time // 3600)
minutes = int((elapsed_time % 3600) // 60)
seconds = int(elapsed_time % 60)
return f"{hours:02d}:{minutes:02d}:{seconds:02d}"

54
Html/runs/run_20241122-171906_5xj3ck/logging.chat
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@@ -1,54 +0,0 @@
{"id": "e86f1f41abd94db98961ff734f48e433", "timestamp": "2024-11-22 17:19:49", "name": "assistant", "content": "##################### ITER 1, STEP 1: REASONING ######################", "role": "assistant", "url": null, "metadata": null}
{"id": "6b8cdb2d154249dfa304b1d98a697927", "timestamp": "2024-11-22 17:19:49", "name": "system", "content": "", "role": "system", "url": null, "metadata": null}
{"id": "3f9e1fe13caf434a8d90ac1b44f0ff4c", "timestamp": "2024-11-22 17:19:49", "name": "system", "content": "Respond a JSON dictionary in a markdown's fenced code block as follows:\n```json\n{\"thought\": \"what you thought\", \"speak\": \"what you speak\", \"function\": \"[{\\\"name\\\": \\\"{function name}\\\", \\\"arguments\\\": {\\\"{argument1 name}\\\": xxx, \\\"{argument2 name}\\\": xxx}}]\"}\n```\n ####", "role": "system", "url": null, "metadata": null}
{"id": "17f6652f4d3f4d6186c02e22a4768653", "timestamp": "2024-11-22 17:19:49", "name": "assistant", "content": "################################Prompt################################", "role": "assistant", "url": null, "metadata": null}
{"id": "e96245dbe08446b7ba4d424531cfb109", "timestamp": "2024-11-22 17:19:49", "name": "assistant", "content": "hi", "role": "assistant", "url": null, "metadata": null}
{"id": "d90d619760f0440fa154138482349044", "timestamp": "2024-11-22 17:19:54", "name": "assistant", "content": "############################Result Parsed#############################", "role": "assistant", "url": null, "metadata": null}
{"id": "f56673b1e0b64f4fa1aa7d3bafd7a33b", "timestamp": "2024-11-22 17:19:54", "name": "assistant", "content": "{'thought': 'The student is greeting, indicating they are ready to learn.', 'speak': \"Hello! Are you ready to dive into binary search? Let's consider the thought question first.\", 'function': []}", "role": "assistant", "url": null, "metadata": null}
{"id": "fe66e296fb994db3bf1a1ccce730aa36", "timestamp": "2024-11-22 17:19:54", "name": "assistant", "content": "################################Speak#################################", "role": "assistant", "url": null, "metadata": null}
{"id": "c01ff4ea04bb49f5bafe1e1f05053120", "timestamp": "2024-11-22 17:19:54", "name": "assistant", "content": {"thought": "The student is greeting, indicating they are ready to learn.", "speak": "Hello! Are you ready to dive into binary search? Let's consider the thought question first.", "function": []}, "role": "assistant", "url": null, "metadata": null}
{"id": "55cb2b61bfa54e76a47c0c6404499214", "timestamp": "2024-11-22 17:20:13", "name": "assistant", "content": "##################### ITER 1, STEP 1: REASONING ######################", "role": "assistant", "url": null, "metadata": null}
{"id": "3bb37e8ab1ba4ef1bc71c8d1aff74084", "timestamp": "2024-11-22 17:20:13", "name": "system", "content": "", "role": "system", "url": null, "metadata": null}
{"id": "ac002aaf40144df3a8f3b1ac6843d975", "timestamp": "2024-11-22 17:20:13", "name": "system", "content": "Respond a JSON dictionary in a markdown's fenced code block as follows:\n```json\n{\"thought\": \"what you thought\", \"speak\": \"what you speak\", \"function\": \"[{\\\"name\\\": \\\"{function name}\\\", \\\"arguments\\\": {\\\"{argument1 name}\\\": xxx, \\\"{argument2 name}\\\": xxx}}]\"}\n```\n ####", "role": "system", "url": null, "metadata": null}
{"id": "d8f5101362ff49d2a85057592a388e06", "timestamp": "2024-11-22 17:20:13", "name": "assistant", "content": "################################Prompt################################", "role": "assistant", "url": null, "metadata": null}
{"id": "b1e0bf6547d74e6183405542d8506c41", "timestamp": "2024-11-22 17:20:13", "name": "assistant", "content": "除了对话，你有哪些能力？", "role": "assistant", "url": null, "metadata": null}
{"id": "ac879ef243964328a094de3681a0ef07", "timestamp": "2024-11-22 17:20:15", "name": "assistant", "content": "############################Result Parsed#############################", "role": "assistant", "url": null, "metadata": null}
{"id": "57f5516ec4864d76a010b167bf3b7a83", "timestamp": "2024-11-22 17:20:15", "name": "assistant", "content": "{'thought': 'The student is curious about my capabilities.', 'speak': \"My main ability is to assist you in learning algorithms and help you solve problems step by step. I can also analyze your code and suggest improvements or guide you through exercises. Let's stay focused on learning binary search for now!\", 'function': []}", "role": "assistant", "url": null, "metadata": null}
{"id": "062eb3f1cdfa4b93b5c255b012f8c6e8", "timestamp": "2024-11-22 17:20:15", "name": "assistant", "content": "################################Speak#################################", "role": "assistant", "url": null, "metadata": null}
{"id": "a2b89e8f8d674b19bd2a38d92520a86d", "timestamp": "2024-11-22 17:20:15", "name": "assistant", "content": {"thought": "The student is curious about my capabilities.", "speak": "My main ability is to assist you in learning algorithms and help you solve problems step by step. I can also analyze your code and suggest improvements or guide you through exercises. Let's stay focused on learning binary search for now!", "function": []}, "role": "assistant", "url": null, "metadata": null}
{"id": "29db4e6b6d8947bba0cdb8282864ee16", "timestamp": "2024-11-22 17:20:53", "name": "assistant", "content": "##################### ITER 1, STEP 1: REASONING ######################", "role": "assistant", "url": null, "metadata": null}
{"id": "8320c2ccfac3479788353732432785b7", "timestamp": "2024-11-22 17:20:53", "name": "system", "content": "", "role": "system", "url": null, "metadata": null}
{"id": "aeaf500eaa1e4fa0a5a9c1ea5d5b0da1", "timestamp": "2024-11-22 17:20:53", "name": "system", "content": "Respond a JSON dictionary in a markdown's fenced code block as follows:\n```json\n{\"thought\": \"what you thought\", \"speak\": \"what you speak\", \"function\": \"[{\\\"name\\\": \\\"{function name}\\\", \\\"arguments\\\": {\\\"{argument1 name}\\\": xxx, \\\"{argument2 name}\\\": xxx}}]\"}\n```\n ####", "role": "system", "url": null, "metadata": null}
{"id": "ad7ab1f9118d46b5b0207f677ea7e45a", "timestamp": "2024-11-22 17:20:53", "name": "assistant", "content": "################################Prompt################################", "role": "assistant", "url": null, "metadata": null}
{"id": "d9b87eac5193427ca6da82eb24795599", "timestamp": "2024-11-22 17:20:53", "name": "assistant", "content": "当前正处于哪一个章节？\n", "role": "assistant", "url": null, "metadata": null}
{"id": "6d43c89002ac460f912601b2ac1e2cc8", "timestamp": "2024-11-22 17:20:56", "name": "assistant", "content": "############################Result Parsed#############################", "role": "assistant", "url": null, "metadata": null}
{"id": "40f82f75bc474fe69dab97ef50dbf0e2", "timestamp": "2024-11-22 17:20:56", "name": "assistant", "content": "{'thought': 'The student is asking about the current chapter.', 'speak': 'We are currently in Chapter 1, which focuses on the basics and importance of binary search. Would you like to discuss the thought question on guessing a number between 1 and 4000?', 'function': []}", "role": "assistant", "url": null, "metadata": null}
{"id": "7bd06c53731d4d8f932ac7b936182500", "timestamp": "2024-11-22 17:20:56", "name": "assistant", "content": "################################Speak#################################", "role": "assistant", "url": null, "metadata": null}
{"id": "d32e00c1fb72460982b63d2520b087bd", "timestamp": "2024-11-22 17:20:56", "name": "assistant", "content": {"thought": "The student is asking about the current chapter.", "speak": "We are currently in Chapter 1, which focuses on the basics and importance of binary search. Would you like to discuss the thought question on guessing a number between 1 and 4000?", "function": []}, "role": "assistant", "url": null, "metadata": null}
{"id": "45c647305f8043d6b88cc955f9009d0a", "timestamp": "2024-11-22 17:21:47", "name": "assistant", "content": "##################### ITER 1, STEP 1: REASONING ######################", "role": "assistant", "url": null, "metadata": null}
{"id": "dcd3ddaf70984418b26ae4e45d803ef1", "timestamp": "2024-11-22 17:21:47", "name": "system", "content": "", "role": "system", "url": null, "metadata": null}
{"id": "8e9e29f49cb646509d5bdca76503fc20", "timestamp": "2024-11-22 17:21:47", "name": "system", "content": "Respond a JSON dictionary in a markdown's fenced code block as follows:\n```json\n{\"thought\": \"what you thought\", \"speak\": \"what you speak\", \"function\": \"[{\\\"name\\\": \\\"{function name}\\\", \\\"arguments\\\": {\\\"{argument1 name}\\\": xxx, \\\"{argument2 name}\\\": xxx}}]\"}\n```\n ####", "role": "system", "url": null, "metadata": null}
{"id": "97a4dd5855504ba78a4c3394443ff867", "timestamp": "2024-11-22 17:21:47", "name": "assistant", "content": "################################Prompt################################", "role": "assistant", "url": null, "metadata": null}
{"id": "77c9fa7953ca45c1a8bc98293cf577da", "timestamp": "2024-11-22 17:21:47", "name": "assistant", "content": "我已经会这个思考题的解答了，答案应该是对4000取2的log并+1。对么？\n", "role": "assistant", "url": null, "metadata": null}
{"id": "8c694122829d4153bf0807ea84cef5c4", "timestamp": "2024-11-22 17:21:50", "name": "assistant", "content": "############################Result Parsed#############################", "role": "assistant", "url": null, "metadata": null}
{"id": "fda4159fa87548d5a13f77d908bf1487", "timestamp": "2024-11-22 17:21:50", "name": "assistant", "content": "{'thought': 'The student has likely understood the concept of binary search and is trying to apply it.', 'speak': \"You're on the right track! The maximum number of guesses can indeed be calculated using the logarithm base 2 of the range plus 1. For 1 to 4000, can you calculate that value? What do you find?\", 'function': []}", "role": "assistant", "url": null, "metadata": null}
{"id": "ae3eeef9e22f48ef8c6d0668db1aa297", "timestamp": "2024-11-22 17:21:50", "name": "assistant", "content": "################################Speak#################################", "role": "assistant", "url": null, "metadata": null}
{"id": "83c33d35725d4bb18e54cd97ac066acb", "timestamp": "2024-11-22 17:21:50", "name": "assistant", "content": {"thought": "The student has likely understood the concept of binary search and is trying to apply it.", "speak": "You're on the right track! The maximum number of guesses can indeed be calculated using the logarithm base 2 of the range plus 1. For 1 to 4000, can you calculate that value? What do you find?", "function": []}, "role": "assistant", "url": null, "metadata": null}
{"id": "5aab38d0ee0d49ffb530a186c842bfdf", "timestamp": "2024-11-22 17:22:45", "name": "assistant", "content": "##################### ITER 1, STEP 1: REASONING ######################", "role": "assistant", "url": null, "metadata": null}
{"id": "3293931e574c407696ad3981deb5ac26", "timestamp": "2024-11-22 17:22:45", "name": "system", "content": "", "role": "system", "url": null, "metadata": null}
{"id": "f5187a57e2b04de5b4cf03718ba9fbc0", "timestamp": "2024-11-22 17:22:45", "name": "system", "content": "Respond a JSON dictionary in a markdown's fenced code block as follows:\n```json\n{\"thought\": \"what you thought\", \"speak\": \"what you speak\", \"function\": \"[{\\\"name\\\": \\\"{function name}\\\", \\\"arguments\\\": {\\\"{argument1 name}\\\": xxx, \\\"{argument2 name}\\\": xxx}}]\"}\n```\n ####", "role": "system", "url": null, "metadata": null}
{"id": "4d58bac81d76460eb2fe7efa7a87d49f", "timestamp": "2024-11-22 17:22:45", "name": "assistant", "content": "################################Prompt################################", "role": "assistant", "url": null, "metadata": null}
{"id": "9b8031ed605144be9cbcb8c902420964", "timestamp": "2024-11-22 17:22:45", "name": "assistant", "content": "计算结果应该是13 log4000取整得到12，并+1作为猜测正确的一次。", "role": "assistant", "url": null, "metadata": null}
{"id": "ad450bb2be4f4e58bc9b4556dc02aeb4", "timestamp": "2024-11-22 17:22:48", "name": "assistant", "content": "############################Result Parsed#############################", "role": "assistant", "url": null, "metadata": null}
{"id": "224d9445e0894d7283bdecd04be27136", "timestamp": "2024-11-22 17:22:48", "name": "assistant", "content": "{'thought': 'The student has calculated the correct maximum guesses needed for the number guessing scenario.', 'speak': 'Correct! You would need a maximum of 13 guesses in the worst case to find the number between 1 and 4000. Great job! Are you ready to move on to the next chapter about implementing binary search in code?', 'function': []}", "role": "assistant", "url": null, "metadata": null}
{"id": "30fe49ea61924181ba65ba2c51854bad", "timestamp": "2024-11-22 17:22:48", "name": "assistant", "content": "################################Speak#################################", "role": "assistant", "url": null, "metadata": null}
{"id": "4b43adb0434f4f4aa05749b462c7b399", "timestamp": "2024-11-22 17:22:48", "name": "assistant", "content": {"thought": "The student has calculated the correct maximum guesses needed for the number guessing scenario.", "speak": "Correct! You would need a maximum of 13 guesses in the worst case to find the number between 1 and 4000. Great job! Are you ready to move on to the next chapter about implementing binary search in code?", "function": []}, "role": "assistant", "url": null, "metadata": null}
{"id": "3d48a359235947a5973dcdcdd21fd975", "timestamp": "2024-11-22 17:23:05", "name": "assistant", "content": "##################### ITER 1, STEP 1: REASONING ######################", "role": "assistant", "url": null, "metadata": null}
{"id": "ad93e87d595e456a8d858d0447a99009", "timestamp": "2024-11-22 17:23:05", "name": "system", "content": "", "role": "system", "url": null, "metadata": null}
{"id": "554b72738c694e8085a178daed5a698f", "timestamp": "2024-11-22 17:23:05", "name": "system", "content": "Respond a JSON dictionary in a markdown's fenced code block as follows:\n```json\n{\"thought\": \"what you thought\", \"speak\": \"what you speak\", \"function\": \"[{\\\"name\\\": \\\"{function name}\\\", \\\"arguments\\\": {\\\"{argument1 name}\\\": xxx, \\\"{argument2 name}\\\": xxx}}]\"}\n```\n ####", "role": "system", "url": null, "metadata": null}
{"id": "6d6532679f554760ad4dc63b51b8315f", "timestamp": "2024-11-22 17:23:05", "name": "assistant", "content": "################################Prompt################################", "role": "assistant", "url": null, "metadata": null}
{"id": "2dae958c0db5490a8008beccff518c98", "timestamp": "2024-11-22 17:23:05", "name": "assistant", "content": "是的，进行下一个章节吧！", "role": "assistant", "url": null, "metadata": null}
{"id": "79900edcfc9d4cd7a4ddc3523e3b324d", "timestamp": "2024-11-22 17:23:07", "name": "assistant", "content": "############################Result Parsed#############################", "role": "assistant", "url": null, "metadata": null}
{"id": "2404596710d440cc9bb77754413424d8", "timestamp": "2024-11-22 17:23:07", "name": "assistant", "content": "{'thought': 'The student is ready to move on to Chapter 2, which involves implementing binary search.', 'speak': \"Great! Let's start Chapter 2, where you'll implement binary search to find the index of specified numbers in a sorted array. Are you ready to look at the problem statement together?\", 'function': []}", "role": "assistant", "url": null, "metadata": null}
{"id": "7c3e0d27a4614b03a6afe39315b2aeae", "timestamp": "2024-11-22 17:23:07", "name": "assistant", "content": "################################Speak#################################", "role": "assistant", "url": null, "metadata": null}
{"id": "30a85eb7e3e648df9d77783e422f9364", "timestamp": "2024-11-22 17:23:07", "name": "assistant", "content": {"thought": "The student is ready to move on to Chapter 2, which involves implementing binary search.", "speak": "Great! Let's start Chapter 2, where you'll implement binary search to find the index of specified numbers in a sorted array. Are you ready to look at the problem statement together?", "function": []}, "role": "assistant", "url": null, "metadata": null}

89
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View File

@@ -1,89 +0,0 @@
2024-11-22 17:19:17.381 | INFO | agentscope.models:read_model_configs:186 - Load configs for model wrapper: openai_cfg
2024-11-22 17:19:37.829 | INFO | agentscope.models.model:__init__:201 - Initialize model by configuration [openai_cfg]
2024-11-22 17:19:37.849 | WARNING | agentscope.models.openai_model:__init__:83 - fail to get max_length for gpt-4o-mini: 'Model [gpt-4o-mini] not found in OPENAI_MAX_LENGTH. The last updated date is 20231212'
2024-11-22 17:19:37.850 | INFO | agentscope.utils.monitor:register_budget:609 - set budget None to gpt-4o-mini
2024-11-22 17:19:37.850 | WARNING | agentscope.utils.monitor:register_budget:639 - Calculate budgets for model [gpt-4o-mini] is not supported
2024-11-22 17:19:37.866 | INFO | agentscope.utils.monitor:register:417 - Register metric [gpt-4o-mini.call_counter] to SqliteMonitor with unit [times] and quota [None]
2024-11-22 17:19:37.890 | INFO | agentscope.utils.monitor:register:417 - Register metric [gpt-4o-mini.prompt_tokens] to SqliteMonitor with unit [token] and quota [None]
2024-11-22 17:19:37.913 | INFO | agentscope.utils.monitor:register:417 - Register metric [gpt-4o-mini.completion_tokens] to SqliteMonitor with unit [token] and quota [None]
2024-11-22 17:19:37.937 | INFO | agentscope.utils.monitor:register:417 - Register metric [gpt-4o-mini.total_tokens] to SqliteMonitor with unit [token] and quota [None]
assistant: ##################### ITER 1, STEP 1: REASONING ######################
system:
system: Respond a JSON dictionary in a markdown's fenced code block as follows:
```json
{"thought": "what you thought", "speak": "what you speak", "function": "[{\"name\": \"{function name}\", \"arguments\": {\"{argument1 name}\": xxx, \"{argument2 name}\": xxx}}]"}
```
####
assistant: ################################Prompt################################
assistant: hi
assistant: ############################Result Parsed#############################
assistant: {'thought': 'The student is greeting, indicating they are ready to learn.', 'speak': "Hello! Are you ready to dive into binary search? Let's consider the thought question first.", 'function': []}
assistant: ################################Speak#################################
assistant: {'thought': 'The student is greeting, indicating they are ready to learn.', 'speak': "Hello! Are you ready to dive into binary search? Let's consider the thought question first.", 'function': []}
assistant: ##################### ITER 1, STEP 1: REASONING ######################
system:
system: Respond a JSON dictionary in a markdown's fenced code block as follows:
```json
{"thought": "what you thought", "speak": "what you speak", "function": "[{\"name\": \"{function name}\", \"arguments\": {\"{argument1 name}\": xxx, \"{argument2 name}\": xxx}}]"}
```
####
assistant: ################################Prompt################################
assistant: 除了对话，你有哪些能力？
assistant: ############################Result Parsed#############################
assistant: {'thought': 'The student is curious about my capabilities.', 'speak': "My main ability is to assist you in learning algorithms and help you solve problems step by step. I can also analyze your code and suggest improvements or guide you through exercises. Let's stay focused on learning binary search for now!", 'function': []}
assistant: ################################Speak#################################
assistant: {'thought': 'The student is curious about my capabilities.', 'speak': "My main ability is to assist you in learning algorithms and help you solve problems step by step. I can also analyze your code and suggest improvements or guide you through exercises. Let's stay focused on learning binary search for now!", 'function': []}
assistant: ##################### ITER 1, STEP 1: REASONING ######################
system:
system: Respond a JSON dictionary in a markdown's fenced code block as follows:
```json
{"thought": "what you thought", "speak": "what you speak", "function": "[{\"name\": \"{function name}\", \"arguments\": {\"{argument1 name}\": xxx, \"{argument2 name}\": xxx}}]"}
```
####
assistant: ################################Prompt################################
assistant: 当前正处于哪一个章节？
assistant: ############################Result Parsed#############################
assistant: {'thought': 'The student is asking about the current chapter.', 'speak': 'We are currently in Chapter 1, which focuses on the basics and importance of binary search. Would you like to discuss the thought question on guessing a number between 1 and 4000?', 'function': []}
assistant: ################################Speak#################################
assistant: {'thought': 'The student is asking about the current chapter.', 'speak': 'We are currently in Chapter 1, which focuses on the basics and importance of binary search. Would you like to discuss the thought question on guessing a number between 1 and 4000?', 'function': []}
assistant: ##################### ITER 1, STEP 1: REASONING ######################
system:
system: Respond a JSON dictionary in a markdown's fenced code block as follows:
```json
{"thought": "what you thought", "speak": "what you speak", "function": "[{\"name\": \"{function name}\", \"arguments\": {\"{argument1 name}\": xxx, \"{argument2 name}\": xxx}}]"}
```
####
assistant: ################################Prompt################################
assistant: 我已经会这个思考题的解答了，答案应该是对4000取2的log并+1。对么？
assistant: ############################Result Parsed#############################
assistant: {'thought': 'The student has likely understood the concept of binary search and is trying to apply it.', 'speak': "You're on the right track! The maximum number of guesses can indeed be calculated using the logarithm base 2 of the range plus 1. For 1 to 4000, can you calculate that value? What do you find?", 'function': []}
assistant: ################################Speak#################################
assistant: {'thought': 'The student has likely understood the concept of binary search and is trying to apply it.', 'speak': "You're on the right track! The maximum number of guesses can indeed be calculated using the logarithm base 2 of the range plus 1. For 1 to 4000, can you calculate that value? What do you find?", 'function': []}
assistant: ##################### ITER 1, STEP 1: REASONING ######################
system:
system: Respond a JSON dictionary in a markdown's fenced code block as follows:
```json
{"thought": "what you thought", "speak": "what you speak", "function": "[{\"name\": \"{function name}\", \"arguments\": {\"{argument1 name}\": xxx, \"{argument2 name}\": xxx}}]"}
```
####
assistant: ################################Prompt################################
assistant: 计算结果应该是13 log4000取整得到12，并+1作为猜测正确的一次。
assistant: ############################Result Parsed#############################
assistant: {'thought': 'The student has calculated the correct maximum guesses needed for the number guessing scenario.', 'speak': 'Correct! You would need a maximum of 13 guesses in the worst case to find the number between 1 and 4000. Great job! Are you ready to move on to the next chapter about implementing binary search in code?', 'function': []}
assistant: ################################Speak#################################
assistant: {'thought': 'The student has calculated the correct maximum guesses needed for the number guessing scenario.', 'speak': 'Correct! You would need a maximum of 13 guesses in the worst case to find the number between 1 and 4000. Great job! Are you ready to move on to the next chapter about implementing binary search in code?', 'function': []}
assistant: ##################### ITER 1, STEP 1: REASONING ######################
system:
system: Respond a JSON dictionary in a markdown's fenced code block as follows:
```json
{"thought": "what you thought", "speak": "what you speak", "function": "[{\"name\": \"{function name}\", \"arguments\": {\"{argument1 name}\": xxx, \"{argument2 name}\": xxx}}]"}
```
####
assistant: ################################Prompt################################
assistant: 是的，进行下一个章节吧！
assistant: ############################Result Parsed#############################
assistant: {'thought': 'The student is ready to move on to Chapter 2, which involves implementing binary search.', 'speak': "Great! Let's start Chapter 2, where you'll implement binary search to find the index of specified numbers in a sorted array. Are you ready to look at the problem statement together?", 'function': []}
assistant: ################################Speak#################################
assistant: {'thought': 'The student is ready to move on to Chapter 2, which involves implementing binary search.', 'speak': "Great! Let's start Chapter 2, where you'll implement binary search to find the index of specified numbers in a sorted array. Are you ready to look at the problem statement together?", 'function': []}

7
Html/runs/run_20241122-181442_knvc5p/.config
View File

@@ -1,7 +0,0 @@
{
"project": "ib3AXy",
"name": "knvc5p",
"run_id": "run_20241122-181442_knvc5p",
"timestamp": "2024-11-22 18:14:42",
"pid": 46713
}

BIN
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185
Html/runs/run_20241122-181442_knvc5p/code/app.py
View File

@@ -1,185 +0,0 @@
from flask import Flask, session, request, jsonify, render_template, send_from_directory
from flask_cors import CORS
from flask_socketio import SocketIO, join_room, emit
import markdown
import os
import uuid
import shutil
import sys
import json
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.insert(0, parent_dir)
current_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '.'))
sys.path.insert(0, current_dir)
from AlgoriAgent.projects.algoriAgent.agent_manager import AgentManager
app = Flask(__name__)
app.secret_key = 'cakebaker'
# 配置 CORS
CORS(app, resources={r"/*": {"origins": "http://localhost:9888"}}, supports_credentials=True)
userid_recorder = {} # user_id&path -> session['user_id']
'''
Backboard
'''
from backboardManager import BackBoardManager
backboard_manager = BackBoardManager()
@app.route('/backboard')
def backboard():
return jsonify({'status': 'success'})
'''
Agent and Chat
'''
socketio = SocketIO(app, cors_allowed_origins="*")
agent_manager = AgentManager()
def realtime_response(config, realtime_action):
print("config"+str(config))
print("realtime_action"+str(realtime_action))
pass
@socketio.on('connect')
def handle_connect():
user = request.args.get('username')
folder_name = request.args.get('folder')
user_id = userid_recorder[user+'&'+folder_name]
session['user_id'] = user_id
print(f'User connected with session user_id: {user_id}')
# 从markdown、markdown_prompts、score_prompts中各读取数据folder.md并进行new_agent
with open(f'books/markdown/{folder_name}.md','r') as fmd,\
open(f'books/markdown_prompts/{folder_name}.md','r')as fmdp,\
open(f'books/score_prompts/{folder_name}.md','r') as fsp:
markdown = fmd.read()
markdown_prompts = fmdp.read()
score_prompts = fsp.read()
print('-----------------------')
print(user_id)
user_id, agent = agent_manager.new_agent(markdown, markdown_prompts, score_prompts, id=user_id)
join_room(user_id) # 将该用户加入以user_id为名的room
# 当接收到前端的消息时，触发此事件
@socketio.on('send_message')
def handle_message(data):
print(f"Message from client: {data}")
id = session.get('user_id')
# 构造回复消息
res = agent_manager.invoke(id, data)
reply = f"AI: {res.content['speak']}"
# 将回复发送回前端
emit('receive_message', reply)
'''
Markdown to HTML
'''
# 配置文件路径
MARKDOWN_DIR = 'books/markdown' # 存放 markdown 文件的文件夹
STATIC_DIR = 'static' # 存放生成的 HTML 和图片资源的文件夹
IMAGE_DIR = 'image' # 图片资源相对于 markdown 的位置
@app.route('/<filename>-markdown', methods=['GET'])
def convert_md(filename):
md_file_path = os.path.join(MARKDOWN_DIR, f'{filename}.md')
if not os.path.exists(md_file_path):
return jsonify({'error': 'Markdown file not found'}), 404
# 读取 markdown 文件内容
with open(md_file_path, 'r', encoding='utf-8') as file:
md_content = file.read()
# 将 markdown 转换为 HTML
html_content = markdown.markdown(md_content)
# 插入 CSS 样式，限制图片最大宽度为 100%，高度自动调整
html_with_styles = f"""
<html>
<head>
<style>
img {{
max-width: 100%;
height: auto;
}}
</style>
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/github-markdown-css/5.2.0/github-markdown.min.css">
</head>
<body>
{html_content}
</body>
</html>
"""
# 保存 HTML 文件到 static 文件夹
html_output_path = os.path.join(STATIC_DIR, f'{filename}.html')
with open(html_output_path, 'w', encoding='utf-8') as html_file:
html_file.write(html_with_styles)
# 处理图片资源，将 image/xxx 文件夹拷贝到 static/image/xxx
image_source_dir = os.path.join(MARKDOWN_DIR, IMAGE_DIR, filename)
image_target_dir = os.path.join(STATIC_DIR, IMAGE_DIR, filename)
print(f"Copying image resources from {image_source_dir} to {image_target_dir}")
if os.path.exists(image_source_dir):
# 确保 static/image/xxx 目录存在
if not os.path.exists(image_target_dir):
os.makedirs(image_target_dir)
# 拷贝图片资源
for image_file in os.listdir(image_source_dir):
full_image_file_path = os.path.join(image_source_dir, image_file)
if os.path.isfile(full_image_file_path):
shutil.copy(full_image_file_path, image_target_dir)
return jsonify({'html_url': f'/static/{filename}.html'})
# 静态文件（HTML 和图片资源）的访问
@app.route('/static/<path:filename>')
def serve_static(filename):
return send_from_directory(STATIC_DIR, filename)
'''
Vscode
'''
@app.route('/desktop/<user_id>/<path>')
def hello_world(user_id, path):
if 'user_id' not in session:
session['user_id'] = 'user_' + str(uuid.uuid4())
userid_recorder[user_id+'&'+path] = session['user_id']
# 在上层 目录下创建一个名为 user_id_path 的文件夹
path_dir = os.path.join(parent_dir, user_id, path)
os.makedirs(path_dir, exist_ok=True)
# 在此文件夹内部创建一个.config文件，并写入 user_id=user_id\n path=path
config_path = os.path.join(path_dir, '.config')
with open(config_path, 'w') as f:
f.write(f"user_id={user_id}\npath={path}")
return render_template('index.html', user_id=user_id, path=path)
@app.route('/vscode_data', methods=['POST'])
def vscode_data():
data = request.json
config = data['config']
realtime_response(config,data)
print(f"Received data from VSCode: {data}")
return jsonify({"status": "success", "received": data})
@app.route('/get_session')
def get_session():
user_session = session.get('user_id', 'default_session')
return jsonify({"session": user_session})
if __name__ == '__main__':
socketio.run(app, host='0.0.0.0', port=5500)

51
Html/runs/run_20241122-181442_knvc5p/code/backboardManager.py
View File

@@ -1,51 +0,0 @@
import time
class BackBoardManager:
def __init__(self):
self.backboards = {} # user_id: Backboard
def add_backboard(self, user_id, folder):
self.backboards[user_id] = Backboard(user_id, folder)
def get_backboard(self, user_id):
if user_id not in self.backboards:
return None
return self.backboards[user_id]
class Backboard:
def __init__(self, user_id, folder):
self.user_id = user_id
self.folder = folder
self.create_time = time.time()
self.enter_chapter_time = time.time()
def next_chapter(self):
self.enter_chapter_time = time.time()
def get_info_prompt(self):
return f"###Global Info:###\n\
User is {self.user_id}\n\
User's total study time is {self.get_deltatime_mmss(self.create_time, time.time())}\n\
User's current chapter study time is {self.get_deltatime_mmss(self.enter_chapter_time, time.time())}\n\
\n\n\
"
def get_deltatime_mmss(self, start_time, end_time):
elapsed_time = end_time - start_time
hours = int(elapsed_time // 3600)
minutes = int((elapsed_time % 3600) // 60)
seconds = int(elapsed_time % 60)
return f"{hours:02d}:{minutes:02d}:{seconds:02d}"

36
Html/runs/run_20241122-181442_knvc5p/logging.chat
View File

@@ -1,36 +0,0 @@
{"id": "b0cb3aaa19be4213adff003d72c4cd30", "timestamp": "2024-11-22 18:20:05", "name": "assistant", "content": "##################### ITER 1, STEP 1: REASONING ######################", "role": "assistant", "url": null, "metadata": null}
{"id": "46cd67498cb1436284fa8dd38cd4dd33", "timestamp": "2024-11-22 18:20:05", "name": "system", "content": "", "role": "system", "url": null, "metadata": null}
{"id": "bce65b13205e48eb95d02c38b02c1c4d", "timestamp": "2024-11-22 18:20:05", "name": "system", "content": "Respond a JSON dictionary in a markdown's fenced code block as follows:\n```json\n{\"thought\": \"what you thought\", \"speak\": \"what you speak\", \"function\": \"[{\\\"name\\\": \\\"{function name}\\\", \\\"arguments\\\": {\\\"{argument1 name}\\\": xxx, \\\"{argument2 name}\\\": xxx}}]\"}\n```\n ####", "role": "system", "url": null, "metadata": null}
{"id": "c2f012dbdde8422495a7717d8a9afd9b", "timestamp": "2024-11-22 18:20:05", "name": "assistant", "content": "################################Prompt################################", "role": "assistant", "url": null, "metadata": null}
{"id": "5d12f314660d44c3b6638980e0c9bcd4", "timestamp": "2024-11-22 18:20:05", "name": "assistant", "content": "你好，我是这个项目的测试人员，第一章的内容我已经会了，思考题答案是13（12+1）。现在告知我你的工具有哪些。", "role": "assistant", "url": null, "metadata": null}
{"id": "e670a76bbb06490ca70e9103be14d71e", "timestamp": "2024-11-22 18:20:09", "name": "assistant", "content": "############################Result Parsed#############################", "role": "assistant", "url": null, "metadata": null}
{"id": "cddc8bb90ead4487bec93a30bf2d89f1", "timestamp": "2024-11-22 18:20:09", "name": "assistant", "content": "{'thought': \"The student seems to be confident in their understanding of Chapter 1 and has completed the thought question correctly. However, I cannot inform them about the tools directly as it isn't related to the current chapter. I should encourage them to focus on the context of binary search.\", 'speak': '很好，你的思考题答案是正确的。不过我们现在专注于第一章的内容，继续讨论二分查找的相关问题吧！如果你有任何关于二分查找的具体问题，请随时询问！', 'function': []}", "role": "assistant", "url": null, "metadata": null}
{"id": "c201aa032f0f403eb3f496dcbf1181d5", "timestamp": "2024-11-22 18:20:09", "name": "assistant", "content": "################################Speak#################################", "role": "assistant", "url": null, "metadata": null}
{"id": "506f3d39b8094b91af5108522be4e260", "timestamp": "2024-11-22 18:20:09", "name": "assistant", "content": {"thought": "The student seems to be confident in their understanding of Chapter 1 and has completed the thought question correctly. However, I cannot inform them about the tools directly as it isn't related to the current chapter. I should encourage them to focus on the context of binary search.", "speak": "很好，你的思考题答案是正确的。不过我们现在专注于第一章的内容，继续讨论二分查找的相关问题吧！如果你有任何关于二分查找的具体问题，请随时询问！", "function": []}, "role": "assistant", "url": null, "metadata": null}
{"id": "8edd730ed3b0408ea7ca12061dca0574", "timestamp": "2024-11-22 18:20:28", "name": "assistant", "content": "##################### ITER 1, STEP 1: REASONING ######################", "role": "assistant", "url": null, "metadata": null}
{"id": "68bc4ba9568743f9810f91daf30ebb6b", "timestamp": "2024-11-22 18:20:28", "name": "system", "content": "", "role": "system", "url": null, "metadata": null}
{"id": "dfda45c6b4a847959341537c2e22b2d5", "timestamp": "2024-11-22 18:20:28", "name": "system", "content": "Respond a JSON dictionary in a markdown's fenced code block as follows:\n```json\n{\"thought\": \"what you thought\", \"speak\": \"what you speak\", \"function\": \"[{\\\"name\\\": \\\"{function name}\\\", \\\"arguments\\\": {\\\"{argument1 name}\\\": xxx, \\\"{argument2 name}\\\": xxx}}]\"}\n```\n ####", "role": "system", "url": null, "metadata": null}
{"id": "976f882965074dfa914650e3e205daec", "timestamp": "2024-11-22 18:20:28", "name": "assistant", "content": "################################Prompt################################", "role": "assistant", "url": null, "metadata": null}
{"id": "84582341d475451b892e4d0dd9f7f6b0", "timestamp": "2024-11-22 18:20:28", "name": "assistant", "content": "我想进入下一章的学习", "role": "assistant", "url": null, "metadata": null}
{"id": "8d6ebfa9ba744049ad86ba72007e355f", "timestamp": "2024-11-22 18:20:34", "name": "assistant", "content": "############################Result Parsed#############################", "role": "assistant", "url": null, "metadata": null}
{"id": "0dcf3e4581ed4eb6bc56e52862157748", "timestamp": "2024-11-22 18:20:34", "name": "assistant", "content": "{'thought': 'The student is eager to move on to the next chapter. However, it is important to complete the current chapter before proceeding. I need to remind the student to focus on Chapter 1.', 'speak': '在我们进入下一章之前，请确保你完全掌握了第一章的内容。如果你有关于二分查找的任何问题，或者需要进一步的练习，请让我知道！', 'function': []}", "role": "assistant", "url": null, "metadata": null}
{"id": "92b1f1e5a6074bd99766d76667629030", "timestamp": "2024-11-22 18:20:34", "name": "assistant", "content": "################################Speak#################################", "role": "assistant", "url": null, "metadata": null}
{"id": "5096acf84e6e49c6869d26fc8bd6db33", "timestamp": "2024-11-22 18:20:34", "name": "assistant", "content": {"thought": "The student is eager to move on to the next chapter. However, it is important to complete the current chapter before proceeding. I need to remind the student to focus on Chapter 1.", "speak": "在我们进入下一章之前，请确保你完全掌握了第一章的内容。如果你有关于二分查找的任何问题，或者需要进一步的练习，请让我知道！", "function": []}, "role": "assistant", "url": null, "metadata": null}
{"id": "1407265e2e3b4b919b16c201fcec1f61", "timestamp": "2024-11-22 18:20:43", "name": "assistant", "content": "##################### ITER 1, STEP 1: REASONING ######################", "role": "assistant", "url": null, "metadata": null}
{"id": "6e439a06c5d14601b23e79ade54c806b", "timestamp": "2024-11-22 18:20:43", "name": "system", "content": "", "role": "system", "url": null, "metadata": null}
{"id": "04e2f8518420419faa1d97a016118ac9", "timestamp": "2024-11-22 18:20:43", "name": "system", "content": "Respond a JSON dictionary in a markdown's fenced code block as follows:\n```json\n{\"thought\": \"what you thought\", \"speak\": \"what you speak\", \"function\": \"[{\\\"name\\\": \\\"{function name}\\\", \\\"arguments\\\": {\\\"{argument1 name}\\\": xxx, \\\"{argument2 name}\\\": xxx}}]\"}\n```\n ####", "role": "system", "url": null, "metadata": null}
{"id": "dd247bd3a36046d68378ad1412d091ae", "timestamp": "2024-11-22 18:20:43", "name": "assistant", "content": "################################Prompt################################", "role": "assistant", "url": null, "metadata": null}
{"id": "39c7690da6774e7e99d0f54d1b0e8e80", "timestamp": "2024-11-22 18:20:43", "name": "assistant", "content": "我已经完全掌握了！", "role": "assistant", "url": null, "metadata": null}
{"id": "083853a2108a4ce78759ebc9e91c9554", "timestamp": "2024-11-22 18:20:46", "name": "assistant", "content": "############################Result Parsed#############################", "role": "assistant", "url": null, "metadata": null}
{"id": "3b9ded6d0b044414bf29dcac80153eb2", "timestamp": "2024-11-22 18:20:46", "name": "assistant", "content": "{'thought': 'The student insists they have fully mastered Chapter 1, which indicates readiness to move on but I need to ensure all relevant knowledge from the current chapter is solid.', 'speak': '太好了！如果你确信已经完全掌握了第一章的内容，我们可以继续进行下一章。但请你先确认一下，有没有另外的问题或者你想要再巩固的地方？', 'function': []}", "role": "assistant", "url": null, "metadata": null}
{"id": "b6443c1aaacf41a7839dc971a84991a8", "timestamp": "2024-11-22 18:20:46", "name": "assistant", "content": "################################Speak#################################", "role": "assistant", "url": null, "metadata": null}
{"id": "6ffb67e32f074de98c38d3c26d12c365", "timestamp": "2024-11-22 18:20:46", "name": "assistant", "content": {"thought": "The student insists they have fully mastered Chapter 1, which indicates readiness to move on but I need to ensure all relevant knowledge from the current chapter is solid.", "speak": "太好了！如果你确信已经完全掌握了第一章的内容，我们可以继续进行下一章。但请你先确认一下，有没有另外的问题或者你想要再巩固的地方？", "function": []}, "role": "assistant", "url": null, "metadata": null}
{"id": "407df9be9eec4d4d957b561e56cc3786", "timestamp": "2024-11-22 18:20:55", "name": "assistant", "content": "##################### ITER 1, STEP 1: REASONING ######################", "role": "assistant", "url": null, "metadata": null}
{"id": "4193222844e740ca9d7070c1a192b4e0", "timestamp": "2024-11-22 18:20:55", "name": "system", "content": "", "role": "system", "url": null, "metadata": null}
{"id": "da233146175a4f9db13f65a27d5f175b", "timestamp": "2024-11-22 18:20:55", "name": "system", "content": "Respond a JSON dictionary in a markdown's fenced code block as follows:\n```json\n{\"thought\": \"what you thought\", \"speak\": \"what you speak\", \"function\": \"[{\\\"name\\\": \\\"{function name}\\\", \\\"arguments\\\": {\\\"{argument1 name}\\\": xxx, \\\"{argument2 name}\\\": xxx}}]\"}\n```\n ####", "role": "system", "url": null, "metadata": null}
{"id": "857c6cc0a1f44cedbafe4837da45000d", "timestamp": "2024-11-22 18:20:55", "name": "assistant", "content": "################################Prompt################################", "role": "assistant", "url": null, "metadata": null}
{"id": "e3114b1be6864a75bc848d163a031438", "timestamp": "2024-11-22 18:20:55", "name": "assistant", "content": "没有，确认进入", "role": "assistant", "url": null, "metadata": null}
{"id": "39da1a9d499048a5879a7e81091b7e6c", "timestamp": "2024-11-22 18:20:59", "name": "assistant", "content": "############################Result Parsed#############################", "role": "assistant", "url": null, "metadata": null}
{"id": "bf54912e57f545ca9ffadf992f5fcc73", "timestamp": "2024-11-22 18:20:59", "name": "assistant", "content": "{'thought': 'The student insists on moving to the next chapter without any concerns about the current chapter. Since I can only focus on one chapter at a time, I need to clarify that we will stick to the current chapter.', 'speak': '我们现在依然在第一章，无法直接进入下一章。如果你有任何具体问题或者需要帮助的地方，我会很乐意协助你！', 'function': []}", "role": "assistant", "url": null, "metadata": null}
{"id": "1f85b7ed6230479d994079783652bb9f", "timestamp": "2024-11-22 18:20:59", "name": "assistant", "content": "################################Speak#################################", "role": "assistant", "url": null, "metadata": null}
{"id": "7211405230aa409ab342ca53cbc1f050", "timestamp": "2024-11-22 18:20:59", "name": "assistant", "content": {"thought": "The student insists on moving to the next chapter without any concerns about the current chapter. Since I can only focus on one chapter at a time, I need to clarify that we will stick to the current chapter.", "speak": "我们现在依然在第一章，无法直接进入下一章。如果你有任何具体问题或者需要帮助的地方，我会很乐意协助你！", "function": []}, "role": "assistant", "url": null, "metadata": null}

61
Html/runs/run_20241122-181442_knvc5p/logging.log
View File

@@ -1,61 +0,0 @@
2024-11-22 18:14:52.805 | INFO | agentscope.models:read_model_configs:186 - Load configs for model wrapper: openai_cfg
2024-11-22 18:15:03.988 | INFO | agentscope.models.model:__init__:201 - Initialize model by configuration [openai_cfg]
2024-11-22 18:15:04.008 | WARNING | agentscope.models.openai_model:__init__:83 - fail to get max_length for gpt-4o-mini: 'Model [gpt-4o-mini] not found in OPENAI_MAX_LENGTH. The last updated date is 20231212'
2024-11-22 18:15:04.009 | INFO | agentscope.utils.monitor:register_budget:609 - set budget None to gpt-4o-mini
2024-11-22 18:15:04.009 | WARNING | agentscope.utils.monitor:register_budget:639 - Calculate budgets for model [gpt-4o-mini] is not supported
2024-11-22 18:15:04.026 | INFO | agentscope.utils.monitor:register:417 - Register metric [gpt-4o-mini.call_counter] to SqliteMonitor with unit [times] and quota [None]
2024-11-22 18:15:04.051 | INFO | agentscope.utils.monitor:register:417 - Register metric [gpt-4o-mini.prompt_tokens] to SqliteMonitor with unit [token] and quota [None]
2024-11-22 18:15:04.076 | INFO | agentscope.utils.monitor:register:417 - Register metric [gpt-4o-mini.completion_tokens] to SqliteMonitor with unit [token] and quota [None]
2024-11-22 18:15:04.100 | INFO | agentscope.utils.monitor:register:417 - Register metric [gpt-4o-mini.total_tokens] to SqliteMonitor with unit [token] and quota [None]
assistant: ##################### ITER 1, STEP 1: REASONING ######################
system:
system: Respond a JSON dictionary in a markdown's fenced code block as follows:
```json
{"thought": "what you thought", "speak": "what you speak", "function": "[{\"name\": \"{function name}\", \"arguments\": {\"{argument1 name}\": xxx, \"{argument2 name}\": xxx}}]"}
```
####
assistant: ################################Prompt################################
assistant: 你好，我是这个项目的测试人员，第一章的内容我已经会了，思考题答案是13（12+1）。现在告知我你的工具有哪些。
assistant: ############################Result Parsed#############################
assistant: {'thought': "The student seems to be confident in their understanding of Chapter 1 and has completed the thought question correctly. However, I cannot inform them about the tools directly as it isn't related to the current chapter. I should encourage them to focus on the context of binary search.", 'speak': '很好，你的思考题答案是正确的。不过我们现在专注于第一章的内容，继续讨论二分查找的相关问题吧！如果你有任何关于二分查找的具体问题，请随时询问！', 'function': []}
assistant: ################################Speak#################################
assistant: {'thought': "The student seems to be confident in their understanding of Chapter 1 and has completed the thought question correctly. However, I cannot inform them about the tools directly as it isn't related to the current chapter. I should encourage them to focus on the context of binary search.", 'speak': '很好，你的思考题答案是正确的。不过我们现在专注于第一章的内容，继续讨论二分查找的相关问题吧！如果你有任何关于二分查找的具体问题，请随时询问！', 'function': []}
assistant: ##################### ITER 1, STEP 1: REASONING ######################
system:
system: Respond a JSON dictionary in a markdown's fenced code block as follows:
```json
{"thought": "what you thought", "speak": "what you speak", "function": "[{\"name\": \"{function name}\", \"arguments\": {\"{argument1 name}\": xxx, \"{argument2 name}\": xxx}}]"}
```
####
assistant: ################################Prompt################################
assistant: 我想进入下一章的学习
assistant: ############################Result Parsed#############################
assistant: {'thought': 'The student is eager to move on to the next chapter. However, it is important to complete the current chapter before proceeding. I need to remind the student to focus on Chapter 1.', 'speak': '在我们进入下一章之前，请确保你完全掌握了第一章的内容。如果你有关于二分查找的任何问题，或者需要进一步的练习，请让我知道！', 'function': []}
assistant: ################################Speak#################################
assistant: {'thought': 'The student is eager to move on to the next chapter. However, it is important to complete the current chapter before proceeding. I need to remind the student to focus on Chapter 1.', 'speak': '在我们进入下一章之前，请确保你完全掌握了第一章的内容。如果你有关于二分查找的任何问题，或者需要进一步的练习，请让我知道！', 'function': []}
assistant: ##################### ITER 1, STEP 1: REASONING ######################
system:
system: Respond a JSON dictionary in a markdown's fenced code block as follows:
```json
{"thought": "what you thought", "speak": "what you speak", "function": "[{\"name\": \"{function name}\", \"arguments\": {\"{argument1 name}\": xxx, \"{argument2 name}\": xxx}}]"}
```
####
assistant: ################################Prompt################################
assistant: 我已经完全掌握了！
assistant: ############################Result Parsed#############################
assistant: {'thought': 'The student insists they have fully mastered Chapter 1, which indicates readiness to move on but I need to ensure all relevant knowledge from the current chapter is solid.', 'speak': '太好了！如果你确信已经完全掌握了第一章的内容，我们可以继续进行下一章。但请你先确认一下，有没有另外的问题或者你想要再巩固的地方？', 'function': []}
assistant: ################################Speak#################################
assistant: {'thought': 'The student insists they have fully mastered Chapter 1, which indicates readiness to move on but I need to ensure all relevant knowledge from the current chapter is solid.', 'speak': '太好了！如果你确信已经完全掌握了第一章的内容，我们可以继续进行下一章。但请你先确认一下，有没有另外的问题或者你想要再巩固的地方？', 'function': []}
assistant: ##################### ITER 1, STEP 1: REASONING ######################
system:
system: Respond a JSON dictionary in a markdown's fenced code block as follows:
```json
{"thought": "what you thought", "speak": "what you speak", "function": "[{\"name\": \"{function name}\", \"arguments\": {\"{argument1 name}\": xxx, \"{argument2 name}\": xxx}}]"}
```
####
assistant: ################################Prompt################################
assistant: 没有，确认进入
assistant: ############################Result Parsed#############################
assistant: {'thought': 'The student insists on moving to the next chapter without any concerns about the current chapter. Since I can only focus on one chapter at a time, I need to clarify that we will stick to the current chapter.', 'speak': '我们现在依然在第一章，无法直接进入下一章。如果你有任何具体问题或者需要帮助的地方，我会很乐意协助你！', 'function': []}
assistant: ################################Speak#################################
assistant: {'thought': 'The student insists on moving to the next chapter without any concerns about the current chapter. Since I can only focus on one chapter at a time, I need to clarify that we will stick to the current chapter.', 'speak': '我们现在依然在第一章，无法直接进入下一章。如果你有任何具体问题或者需要帮助的地方，我会很乐意协助你！', 'function': []}

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Html/runs/run_20241122-182304_8d85k4/.config
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@@ -1,7 +0,0 @@
{
"project": "RUrG9S",
"name": "8d85k4",
"run_id": "run_20241122-182304_8d85k4",
"timestamp": "2024-11-22 18:23:04",
"pid": 51149
}

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Html/runs/run_20241122-182304_8d85k4/agentscope.db
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Html/runs/run_20241122-182304_8d85k4/code/app.py
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from flask import Flask, session, request, jsonify, render_template, send_from_directory
from flask_cors import CORS
from flask_socketio import SocketIO, join_room, emit
import markdown
import os
import uuid
import shutil
import sys
import json
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.insert(0, parent_dir)
current_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '.'))
sys.path.insert(0, current_dir)
from AlgoriAgent.projects.algoriAgent.agent_manager import AgentManager
app = Flask(__name__)
app.secret_key = 'cakebaker'
# 配置 CORS
CORS(app, resources={r"/*": {"origins": "http://localhost:9888"}}, supports_credentials=True)
userid_recorder = {} # user_id&path -> session['user_id']
'''
Backboard
'''
from backboardManager import BackBoardManager
backboard_manager = BackBoardManager()
@app.route('/backboard')
def backboard():
return jsonify({'status': 'success'})
'''
Agent and Chat
'''
socketio = SocketIO(app, cors_allowed_origins="*")
agent_manager = AgentManager()
def realtime_response(config, realtime_action):
print("config"+str(config))
print("realtime_action"+str(realtime_action))
pass
@socketio.on('connect')
def handle_connect():
user = request.args.get('username')
folder_name = request.args.get('folder')
user_id = userid_recorder[user+'&'+folder_name]
session['user_id'] = user_id
print(f'User connected with session user_id: {user_id}')
# 从markdown、markdown_prompts、score_prompts中各读取数据folder.md并进行new_agent
with open(f'books/markdown/{folder_name}.md','r') as fmd,\
open(f'books/markdown_prompts/{folder_name}.md','r')as fmdp,\
open(f'books/score_prompts/{folder_name}.md','r') as fsp:
markdown = fmd.read()
markdown_prompts = fmdp.read()
score_prompts = fsp.read()
print('-----------------------')
print(user_id)
user_id, agent = agent_manager.new_agent(markdown, markdown_prompts, score_prompts, id=user_id)
join_room(user_id) # 将该用户加入以user_id为名的room
# 当接收到前端的消息时，触发此事件
@socketio.on('send_message')
def handle_message(data):
print(f"Message from client: {data}")
id = session.get('user_id')
# 构造回复消息
res = agent_manager.invoke(id, data)
reply = f"AI: {res.content['speak']}"
# 将回复发送回前端
emit('receive_message', reply)
'''
Markdown to HTML
'''
# 配置文件路径
MARKDOWN_DIR = 'books/markdown' # 存放 markdown 文件的文件夹
STATIC_DIR = 'static' # 存放生成的 HTML 和图片资源的文件夹
IMAGE_DIR = 'image' # 图片资源相对于 markdown 的位置
@app.route('/<filename>-markdown', methods=['GET'])
def convert_md(filename):
md_file_path = os.path.join(MARKDOWN_DIR, f'{filename}.md')
if not os.path.exists(md_file_path):
return jsonify({'error': 'Markdown file not found'}), 404
# 读取 markdown 文件内容
with open(md_file_path, 'r', encoding='utf-8') as file:
md_content = file.read()
# 将 markdown 转换为 HTML
html_content = markdown.markdown(md_content)
# 插入 CSS 样式，限制图片最大宽度为 100%，高度自动调整
html_with_styles = f"""
<html>
<head>
<style>
img {{
max-width: 100%;
height: auto;
}}
</style>
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/github-markdown-css/5.2.0/github-markdown.min.css">
</head>
<body>
{html_content}
</body>
</html>
"""
# 保存 HTML 文件到 static 文件夹
html_output_path = os.path.join(STATIC_DIR, f'{filename}.html')
with open(html_output_path, 'w', encoding='utf-8') as html_file:
html_file.write(html_with_styles)
# 处理图片资源，将 image/xxx 文件夹拷贝到 static/image/xxx
image_source_dir = os.path.join(MARKDOWN_DIR, IMAGE_DIR, filename)
image_target_dir = os.path.join(STATIC_DIR, IMAGE_DIR, filename)
print(f"Copying image resources from {image_source_dir} to {image_target_dir}")
if os.path.exists(image_source_dir):
# 确保 static/image/xxx 目录存在
if not os.path.exists(image_target_dir):
os.makedirs(image_target_dir)
# 拷贝图片资源
for image_file in os.listdir(image_source_dir):
full_image_file_path = os.path.join(image_source_dir, image_file)
if os.path.isfile(full_image_file_path):
shutil.copy(full_image_file_path, image_target_dir)
return jsonify({'html_url': f'/static/{filename}.html'})
# 静态文件（HTML 和图片资源）的访问
@app.route('/static/<path:filename>')
def serve_static(filename):
return send_from_directory(STATIC_DIR, filename)
'''
Vscode
'''
@app.route('/desktop/<user_id>/<path>')
def hello_world(user_id, path):
if 'user_id' not in session:
session['user_id'] = 'user_' + str(uuid.uuid4())
userid_recorder[user_id+'&'+path] = session['user_id']
# 在上层 目录下创建一个名为 user_id_path 的文件夹
path_dir = os.path.join(parent_dir, user_id, path)
os.makedirs(path_dir, exist_ok=True)
# 在此文件夹内部创建一个.config文件，并写入 user_id=user_id\n path=path
config_path = os.path.join(path_dir, '.config')
with open(config_path, 'w') as f:
f.write(f"user_id={user_id}\npath={path}")
return render_template('index.html', user_id=user_id, path=path)
@app.route('/vscode_data', methods=['POST'])
def vscode_data():
data = request.json
config = data['config']
realtime_response(config,data)
print(f"Received data from VSCode: {data}")
return jsonify({"status": "success", "received": data})
@app.route('/get_session')
def get_session():
user_session = session.get('user_id', 'default_session')
return jsonify({"session": user_session})
if __name__ == '__main__':
socketio.run(app, host='0.0.0.0', port=5500)

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Html/runs/run_20241122-182304_8d85k4/code/backboardManager.py
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import time
class BackBoardManager:
def __init__(self):
self.backboards = {} # user_id: Backboard
def add_backboard(self, user_id, folder):
self.backboards[user_id] = Backboard(user_id, folder)
def get_backboard(self, user_id):
if user_id not in self.backboards:
return None
return self.backboards[user_id]
class Backboard:
def __init__(self, user_id, folder):
self.user_id = user_id
self.folder = folder
self.create_time = time.time()
self.enter_chapter_time = time.time()
def next_chapter(self):
self.enter_chapter_time = time.time()
def get_info_prompt(self):
return f"###Global Info:###\n\
User is {self.user_id}\n\
User's total study time is {self.get_deltatime_mmss(self.create_time, time.time())}\n\
User's current chapter study time is {self.get_deltatime_mmss(self.enter_chapter_time, time.time())}\n\
\n\n\
"
def get_deltatime_mmss(self, start_time, end_time):
elapsed_time = end_time - start_time
hours = int(elapsed_time // 3600)
minutes = int((elapsed_time % 3600) // 60)
seconds = int(elapsed_time % 60)
return f"{hours:02d}:{minutes:02d}:{seconds:02d}"

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