mirror of
https://github.com/Zippland/Snap-Solver.git
synced 2026-01-19 01:21:13 +08:00
884 lines
32 KiB
Python
884 lines
32 KiB
Python
from flask import Flask, jsonify, render_template, request, send_from_directory
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from flask_socketio import SocketIO
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import pyautogui
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import base64
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from io import BytesIO
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import socket
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from threading import Thread, Event
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import threading
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from PIL import Image
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import pyperclip
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from models import ModelFactory
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import time
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import os
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import json
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import traceback
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import requests
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from datetime import datetime
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import sys
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app = Flask(__name__)
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socketio = SocketIO(
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app,
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cors_allowed_origins="*",
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ping_timeout=30,
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ping_interval=5,
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max_http_buffer_size=50 * 1024 * 1024,
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async_mode='threading', # 使用threading模式提高兼容性
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engineio_logger=True, # 启用引擎日志，便于调试
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logger=True # 启用Socket.IO日志
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)
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# 常量定义
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CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
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CONFIG_DIR = os.path.join(CURRENT_DIR, 'config')
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STATIC_DIR = os.path.join(CURRENT_DIR, 'static')
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# 确保配置目录存在
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os.makedirs(CONFIG_DIR, exist_ok=True)
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# 密钥和其他配置文件路径
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API_KEYS_FILE = os.path.join(CONFIG_DIR, 'api_keys.json')
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VERSION_FILE = os.path.join(CONFIG_DIR, 'version.json')
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UPDATE_INFO_FILE = os.path.join(CONFIG_DIR, 'update_info.json')
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PROMPT_FILE = os.path.join(CONFIG_DIR, 'prompts.json') # 新增提示词配置文件路径
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# 跟踪用户生成任务的字典
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generation_tasks = {}
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# 初始化模型工厂
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ModelFactory.initialize()
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def get_local_ip():
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try:
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# Get local IP address
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s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
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s.connect(("8.8.8.8", 80))
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ip = s.getsockname()[0]
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s.close()
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return ip
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except Exception:
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return "127.0.0.1"
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@app.route('/')
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def index():
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local_ip = get_local_ip()
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# 检查更新
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try:
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update_info = check_for_updates()
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except:
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update_info = {'has_update': False}
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return render_template('index.html', local_ip=local_ip, update_info=update_info)
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@socketio.on('connect')
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def handle_connect():
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print('Client connected')
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@socketio.on('disconnect')
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def handle_disconnect():
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print('Client disconnected')
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def create_model_instance(model_id, settings, is_reasoning=False):
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"""创建模型实例"""
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# 提取API密钥
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api_keys = settings.get('apiKeys', {})
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# 确定需要哪个API密钥
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api_key_id = None
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# 特殊情况：o3-mini使用OpenAI API密钥
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if model_id.lower() == "o3-mini":
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api_key_id = "OpenaiApiKey"
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# 其他Anthropic/Claude模型
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elif "claude" in model_id.lower() or "anthropic" in model_id.lower():
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api_key_id = "AnthropicApiKey"
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elif any(keyword in model_id.lower() for keyword in ["gpt", "openai"]):
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api_key_id = "OpenaiApiKey"
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elif "deepseek" in model_id.lower():
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api_key_id = "DeepseekApiKey"
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elif "qvq" in model_id.lower() or "alibaba" in model_id.lower() or "qwen" in model_id.lower():
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api_key_id = "AlibabaApiKey"
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# 首先尝试从本地配置获取API密钥
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api_key = get_api_key(api_key_id)
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# 如果本地没有配置，尝试使用前端传递的密钥（向后兼容）
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if not api_key:
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api_key = api_keys.get(api_key_id)
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if not api_key:
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raise ValueError(f"API key is required for the selected model (keyId: {api_key_id})")
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# 获取maxTokens参数，默认为8192
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max_tokens = int(settings.get('maxTokens', 8192))
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# 创建模型实例
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model_instance = ModelFactory.create_model(
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model_name=model_id,
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api_key=api_key,
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temperature=None if is_reasoning else float(settings.get('temperature', 0.7)),
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system_prompt=settings.get('systemPrompt'),
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language=settings.get('language', '中文')
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)
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# 设置最大输出Token，但不为阿里巴巴模型设置（它们有自己内部的处理逻辑）
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is_alibaba_model = "qvq" in model_id.lower() or "alibaba" in model_id.lower() or "qwen" in model_id.lower()
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if not is_alibaba_model:
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model_instance.max_tokens = max_tokens
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return model_instance
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def stream_model_response(response_generator, sid, model_name=None):
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"""Stream model responses to the client"""
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try:
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print("Starting response streaming...")
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# 判断模型是否为推理模型
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is_reasoning = model_name and ModelFactory.is_reasoning(model_name)
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if is_reasoning:
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print(f"使用推理模型 {model_name}，将显示思考过程")
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# 初始化：发送开始状态
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socketio.emit('ai_response', {
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'status': 'started',
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'content': '',
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'is_reasoning': is_reasoning
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}, room=sid)
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print("Sent initial status to client")
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# 维护服务端缓冲区以累积完整内容
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response_buffer = ""
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thinking_buffer = ""
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# 上次发送的时间戳，用于控制发送频率
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last_emit_time = time.time()
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# 流式处理响应
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for response in response_generator:
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# 处理Mathpix响应
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if isinstance(response.get('content', ''), str) and 'mathpix' in response.get('model', ''):
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socketio.emit('text_extracted', {
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'content': response['content']
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}, room=sid)
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continue
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# 获取状态和内容
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status = response.get('status', '')
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content = response.get('content', '')
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# 根据不同的状态进行处理
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if status == 'thinking':
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# 仅对推理模型处理思考过程
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if is_reasoning:
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# 直接使用模型提供的完整思考内容
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thinking_buffer = content
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# 控制发送频率，至少间隔0.3秒
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current_time = time.time()
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if current_time - last_emit_time >= 0.3:
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socketio.emit('ai_response', {
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'status': 'thinking',
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'content': thinking_buffer,
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'is_reasoning': True
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}, room=sid)
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last_emit_time = current_time
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elif status == 'thinking_complete':
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# 仅对推理模型处理思考过程
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if is_reasoning:
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# 直接使用完整的思考内容
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thinking_buffer = content
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print(f"Thinking complete, total length: {len(thinking_buffer)} chars")
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socketio.emit('ai_response', {
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'status': 'thinking_complete',
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'content': thinking_buffer,
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'is_reasoning': True
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}, room=sid)
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elif status == 'streaming':
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# 直接使用模型提供的完整内容
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response_buffer = content
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# 控制发送频率，至少间隔0.3秒
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current_time = time.time()
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if current_time - last_emit_time >= 0.3:
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socketio.emit('ai_response', {
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'status': 'streaming',
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'content': response_buffer,
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'is_reasoning': is_reasoning
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}, room=sid)
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last_emit_time = current_time
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elif status == 'completed':
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# 确保发送最终完整内容
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socketio.emit('ai_response', {
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'status': 'completed',
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'content': content or response_buffer,
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'is_reasoning': is_reasoning
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}, room=sid)
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print("Response completed")
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elif status == 'error':
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# 错误状态直接转发
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response['is_reasoning'] = is_reasoning
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socketio.emit('ai_response', response, room=sid)
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print(f"Error: {response.get('error', 'Unknown error')}")
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# 其他状态直接转发
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else:
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response['is_reasoning'] = is_reasoning
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socketio.emit('ai_response', response, room=sid)
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except Exception as e:
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error_msg = f"Streaming error: {str(e)}"
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print(error_msg)
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socketio.emit('ai_response', {
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'status': 'error',
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'error': error_msg,
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'is_reasoning': model_name and ModelFactory.is_reasoning(model_name)
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}, room=sid)
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@socketio.on('request_screenshot')
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def handle_screenshot_request():
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try:
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# Capture the screen
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screenshot = pyautogui.screenshot()
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# Convert the image to base64 string
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buffered = BytesIO()
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screenshot.save(buffered, format="PNG")
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img_str = base64.b64encode(buffered.getvalue()).decode()
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# Emit the screenshot back to the client
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socketio.emit('screenshot_response', {
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'success': True,
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'image': img_str
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})
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except Exception as e:
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socketio.emit('screenshot_response', {
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'success': False,
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'error': str(e)
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})
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@socketio.on('extract_text')
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def handle_text_extraction(data):
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try:
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print("Starting text extraction...")
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# Validate input data
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if not data or not isinstance(data, dict):
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raise ValueError("Invalid request data")
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if 'image' not in data:
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raise ValueError("No image data provided")
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image_data = data['image']
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if not isinstance(image_data, str):
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raise ValueError("Invalid image data format")
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# 检查图像大小，避免处理过大的图像导致断开连接
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image_size_bytes = len(image_data) * 3 / 4 # 估算base64的实际大小
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if image_size_bytes > 10 * 1024 * 1024: # 10MB
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raise ValueError("Image too large, please crop to a smaller area")
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settings = data.get('settings', {})
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if not isinstance(settings, dict):
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raise ValueError("Invalid settings format")
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# 尝试从本地配置获取Mathpix API密钥
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mathpix_app_id = get_api_key('MathpixAppId')
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mathpix_app_key = get_api_key('MathpixAppKey')
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# 构建完整的Mathpix API密钥（格式：app_id:app_key）
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mathpix_key = f"{mathpix_app_id}:{mathpix_app_key}" if mathpix_app_id and mathpix_app_key else None
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# 如果本地没有配置，尝试使用前端传递的密钥（向后兼容）
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if not mathpix_key:
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mathpix_key = settings.get('mathpixApiKey')
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if not mathpix_key:
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raise ValueError("Mathpix API key is required")
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# 先回复客户端，确认已收到请求，防止超时断开
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# 注意：这里不能使用return，否则后续代码不会执行
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socketio.emit('request_acknowledged', {
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'status': 'received',
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'message': 'Image received, text extraction in progress'
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}, room=request.sid)
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try:
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app_id, app_key = mathpix_key.split(':')
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if not app_id.strip() or not app_key.strip():
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raise ValueError()
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except ValueError:
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raise ValueError("Invalid Mathpix API key format. Expected format: 'app_id:app_key'")
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print("Creating Mathpix model instance...")
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# 只传递必需的参数，ModelFactory.create_model会处理不同模型类型
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model = ModelFactory.create_model(
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model_name='mathpix',
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api_key=mathpix_key
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)
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print("Starting text extraction...")
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# 使用新的extract_full_text方法直接提取完整文本
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extracted_text = model.extract_full_text(image_data)
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# 直接返回文本结果
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socketio.emit('text_extracted', {
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'content': extracted_text
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}, room=request.sid)
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except ValueError as e:
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error_msg = str(e)
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print(f"Validation error: {error_msg}")
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socketio.emit('text_extracted', {
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'error': error_msg
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}, room=request.sid)
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except Exception as e:
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error_msg = f"Text extraction error: {str(e)}"
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print(f"Unexpected error: {error_msg}")
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print(f"Error details: {type(e).__name__}")
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socketio.emit('text_extracted', {
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'error': error_msg
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}, room=request.sid)
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@socketio.on('stop_generation')
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def handle_stop_generation():
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"""处理停止生成请求"""
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sid = request.sid
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print(f"接收到停止生成请求: {sid}")
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if sid in generation_tasks:
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# 设置停止标志
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stop_event = generation_tasks[sid]
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stop_event.set()
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# 发送已停止状态
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socketio.emit('ai_response', {
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'status': 'stopped',
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'content': '生成已停止'
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}, room=sid)
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print(f"已停止用户 {sid} 的生成任务")
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else:
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print(f"未找到用户 {sid} 的生成任务")
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@socketio.on('analyze_text')
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def handle_analyze_text(data):
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try:
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text = data.get('text', '')
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settings = data.get('settings', {})
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# 获取推理配置
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reasoning_config = settings.get('reasoningConfig', {})
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# 获取maxTokens
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max_tokens = int(settings.get('maxTokens', 8192))
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print(f"Debug - 文本分析请求: {text[:50]}...")
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print(f"Debug - 最大Token: {max_tokens}, 推理配置: {reasoning_config}")
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# 获取模型和API密钥
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model_id = settings.get('model', 'claude-3-7-sonnet-20250219')
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if not text:
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socketio.emit('error', {'message': '文本内容不能为空'})
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return
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# 获取模型信息，判断是否为推理模型
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model_info = settings.get('modelInfo', {})
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is_reasoning = model_info.get('isReasoning', False)
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model_instance = create_model_instance(model_id, settings, is_reasoning)
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# 将推理配置传递给模型
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if reasoning_config:
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model_instance.reasoning_config = reasoning_config
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# 如果启用代理，配置代理设置
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proxies = None
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if settings.get('proxyEnabled'):
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proxies = {
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'http': f"http://{settings.get('proxyHost')}:{settings.get('proxyPort')}",
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'https': f"http://{settings.get('proxyHost')}:{settings.get('proxyPort')}"
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}
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# 创建用于停止生成的事件
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sid = request.sid
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stop_event = Event()
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generation_tasks[sid] = stop_event
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try:
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for response in model_instance.analyze_text(text, proxies=proxies):
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# 检查是否收到停止信号
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if stop_event.is_set():
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print(f"分析文本生成被用户 {sid} 停止")
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break
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socketio.emit('ai_response', response, room=sid)
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finally:
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# 清理任务
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if sid in generation_tasks:
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del generation_tasks[sid]
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except Exception as e:
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print(f"Error in analyze_text: {str(e)}")
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traceback.print_exc()
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socketio.emit('error', {'message': f'分析文本时出错: {str(e)}'})
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@socketio.on('analyze_image')
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def handle_analyze_image(data):
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try:
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image_data = data.get('image')
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settings = data.get('settings', {})
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# 获取推理配置
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reasoning_config = settings.get('reasoningConfig', {})
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# 获取maxTokens
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max_tokens = int(settings.get('maxTokens', 8192))
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print(f"Debug - 图像分析请求")
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print(f"Debug - 最大Token: {max_tokens}, 推理配置: {reasoning_config}")
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# 获取模型和API密钥
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model_id = settings.get('model', 'claude-3-7-sonnet-20250219')
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if not image_data:
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socketio.emit('error', {'message': '图像数据不能为空'})
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return
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# 获取模型信息，判断是否为推理模型
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model_info = settings.get('modelInfo', {})
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is_reasoning = model_info.get('isReasoning', False)
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model_instance = create_model_instance(model_id, settings, is_reasoning)
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# 将推理配置传递给模型
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if reasoning_config:
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model_instance.reasoning_config = reasoning_config
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# 如果启用代理，配置代理设置
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proxies = None
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if settings.get('proxyEnabled'):
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proxies = {
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'http': f"http://{settings.get('proxyHost')}:{settings.get('proxyPort')}",
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'https': f"http://{settings.get('proxyHost')}:{settings.get('proxyPort')}"
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}
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# 创建用于停止生成的事件
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sid = request.sid
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stop_event = Event()
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generation_tasks[sid] = stop_event
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try:
|
||
for response in model_instance.analyze_image(image_data, proxies=proxies):
|
||
# 检查是否收到停止信号
|
||
if stop_event.is_set():
|
||
print(f"分析图像生成被用户 {sid} 停止")
|
||
break
|
||
|
||
socketio.emit('ai_response', response, room=sid)
|
||
finally:
|
||
# 清理任务
|
||
if sid in generation_tasks:
|
||
del generation_tasks[sid]
|
||
|
||
except Exception as e:
|
||
print(f"Error in analyze_image: {str(e)}")
|
||
traceback.print_exc()
|
||
socketio.emit('error', {'message': f'分析图像时出错: {str(e)}'})
|
||
|
||
@socketio.on('capture_screenshot')
|
||
def handle_capture_screenshot(data):
|
||
try:
|
||
# Capture the screen
|
||
screenshot = pyautogui.screenshot()
|
||
|
||
# Convert the image to base64 string
|
||
buffered = BytesIO()
|
||
screenshot.save(buffered, format="PNG")
|
||
img_str = base64.b64encode(buffered.getvalue()).decode()
|
||
|
||
# Emit the screenshot back to the client
|
||
socketio.emit('screenshot_complete', {
|
||
'success': True,
|
||
'image': img_str
|
||
}, room=request.sid)
|
||
except Exception as e:
|
||
error_msg = f"Screenshot error: {str(e)}"
|
||
print(f"Error capturing screenshot: {error_msg}")
|
||
socketio.emit('screenshot_complete', {
|
||
'success': False,
|
||
'error': error_msg
|
||
}, room=request.sid)
|
||
|
||
def load_model_config():
|
||
"""加载模型配置信息"""
|
||
try:
|
||
config_path = os.path.join(CONFIG_DIR, 'models.json')
|
||
with open(config_path, 'r', encoding='utf-8') as f:
|
||
config = json.load(f)
|
||
return config
|
||
except Exception as e:
|
||
print(f"加载模型配置失败: {e}")
|
||
return {
|
||
"providers": {},
|
||
"models": {}
|
||
}
|
||
|
||
def load_prompts():
|
||
"""加载系统提示词配置"""
|
||
try:
|
||
if os.path.exists(PROMPT_FILE):
|
||
with open(PROMPT_FILE, 'r', encoding='utf-8') as f:
|
||
return json.load(f)
|
||
else:
|
||
# 如果文件不存在，创建默认提示词配置
|
||
default_prompts = {
|
||
"default": {
|
||
"name": "默认提示词",
|
||
"content": "您是一位专业的问题解决专家。请逐步分析问题，找出问题所在，并提供详细的解决方案。始终使用用户偏好的语言回答。",
|
||
"description": "通用问题解决提示词"
|
||
}
|
||
}
|
||
with open(PROMPT_FILE, 'w', encoding='utf-8') as f:
|
||
json.dump(default_prompts, f, ensure_ascii=False, indent=4)
|
||
return default_prompts
|
||
except Exception as e:
|
||
print(f"加载提示词配置失败: {e}")
|
||
return {
|
||
"default": {
|
||
"name": "默认提示词",
|
||
"content": "您是一位专业的问题解决专家。请逐步分析问题，找出问题所在，并提供详细的解决方案。始终使用用户偏好的语言回答。",
|
||
"description": "通用问题解决提示词"
|
||
}
|
||
}
|
||
|
||
def save_prompt(prompt_id, prompt_data):
|
||
"""保存单个提示词到配置文件"""
|
||
try:
|
||
prompts = load_prompts()
|
||
prompts[prompt_id] = prompt_data
|
||
with open(PROMPT_FILE, 'w', encoding='utf-8') as f:
|
||
json.dump(prompts, f, ensure_ascii=False, indent=4)
|
||
return True
|
||
except Exception as e:
|
||
print(f"保存提示词配置失败: {e}")
|
||
return False
|
||
|
||
def delete_prompt(prompt_id):
|
||
"""从配置文件中删除一个提示词"""
|
||
try:
|
||
prompts = load_prompts()
|
||
if prompt_id in prompts:
|
||
del prompts[prompt_id]
|
||
with open(PROMPT_FILE, 'w', encoding='utf-8') as f:
|
||
json.dump(prompts, f, ensure_ascii=False, indent=4)
|
||
return True
|
||
return False
|
||
except Exception as e:
|
||
print(f"删除提示词配置失败: {e}")
|
||
return False
|
||
|
||
# 替换 before_first_request 装饰器
|
||
def init_model_config():
|
||
"""初始化模型配置"""
|
||
try:
|
||
model_config = load_model_config()
|
||
# 更新ModelFactory的模型信息
|
||
if hasattr(ModelFactory, 'update_model_capabilities'):
|
||
ModelFactory.update_model_capabilities(model_config)
|
||
print("已加载模型配置")
|
||
except Exception as e:
|
||
print(f"初始化模型配置失败: {e}")
|
||
|
||
# 在请求处理前注册初始化函数
|
||
@app.before_request
|
||
def before_request_handler():
|
||
# 使用全局变量跟踪是否已初始化
|
||
if not getattr(app, '_model_config_initialized', False):
|
||
init_model_config()
|
||
app._model_config_initialized = True
|
||
|
||
# 版本检查函数
|
||
def check_for_updates():
|
||
"""检查GitHub上是否有新版本"""
|
||
try:
|
||
# 读取当前版本信息
|
||
version_file = os.path.join(CONFIG_DIR, 'version.json')
|
||
with open(version_file, 'r', encoding='utf-8') as f:
|
||
version_info = json.load(f)
|
||
|
||
current_version = version_info.get('version', '0.0.0')
|
||
repo = version_info.get('github_repo', 'Zippland/Snap-Solver')
|
||
|
||
# 请求GitHub API获取最新发布版本
|
||
api_url = f"https://api.github.com/repos/{repo}/releases/latest"
|
||
|
||
# 添加User-Agent以符合GitHub API要求
|
||
headers = {'User-Agent': 'Snap-Solver-Update-Checker'}
|
||
|
||
response = requests.get(api_url, headers=headers, timeout=5)
|
||
if response.status_code == 200:
|
||
latest_release = response.json()
|
||
latest_version = latest_release.get('tag_name', '').lstrip('v')
|
||
|
||
# 如果版本号为空，尝试从名称中提取
|
||
if not latest_version and 'name' in latest_release:
|
||
import re
|
||
version_match = re.search(r'v?(\d+\.\d+\.\d+)', latest_release['name'])
|
||
if version_match:
|
||
latest_version = version_match.group(1)
|
||
|
||
# 比较版本号（简单比较，可以改进为更复杂的语义版本比较）
|
||
has_update = compare_versions(latest_version, current_version)
|
||
|
||
update_info = {
|
||
'has_update': has_update,
|
||
'current_version': current_version,
|
||
'latest_version': latest_version,
|
||
'release_url': latest_release.get('html_url', f"https://github.com/{repo}/releases/latest"),
|
||
'release_date': latest_release.get('published_at', ''),
|
||
'release_notes': latest_release.get('body', ''),
|
||
}
|
||
|
||
# 缓存更新信息
|
||
update_info_file = os.path.join(CONFIG_DIR, 'update_info.json')
|
||
with open(update_info_file, 'w', encoding='utf-8') as f:
|
||
json.dump(update_info, f, ensure_ascii=False, indent=2)
|
||
|
||
return update_info
|
||
|
||
# 如果无法连接GitHub，尝试读取缓存的更新信息
|
||
update_info_file = os.path.join(CONFIG_DIR, 'update_info.json')
|
||
if os.path.exists(update_info_file):
|
||
with open(update_info_file, 'r', encoding='utf-8') as f:
|
||
return json.load(f)
|
||
|
||
return {'has_update': False, 'current_version': current_version}
|
||
|
||
except Exception as e:
|
||
print(f"检查更新失败: {str(e)}")
|
||
# 出错时返回一个默认的值
|
||
return {'has_update': False, 'error': str(e)}
|
||
|
||
def compare_versions(version1, version2):
|
||
"""比较两个版本号，如果version1比version2更新，则返回True"""
|
||
try:
|
||
v1_parts = [int(x) for x in version1.split('.')]
|
||
v2_parts = [int(x) for x in version2.split('.')]
|
||
|
||
# 确保两个版本号的组成部分长度相同
|
||
while len(v1_parts) < len(v2_parts):
|
||
v1_parts.append(0)
|
||
while len(v2_parts) < len(v1_parts):
|
||
v2_parts.append(0)
|
||
|
||
# 逐部分比较
|
||
for i in range(len(v1_parts)):
|
||
if v1_parts[i] > v2_parts[i]:
|
||
return True
|
||
elif v1_parts[i] < v2_parts[i]:
|
||
return False
|
||
|
||
# 完全相同的版本
|
||
return False
|
||
except:
|
||
# 如果解析出错，默认不更新
|
||
return False
|
||
|
||
@app.route('/api/check-update', methods=['GET'])
|
||
def api_check_update():
|
||
"""检查更新的API端点"""
|
||
update_info = check_for_updates()
|
||
return jsonify(update_info)
|
||
|
||
# 添加配置文件路由
|
||
@app.route('/config/<path:filename>')
|
||
def serve_config(filename):
|
||
return send_from_directory(CONFIG_DIR, filename)
|
||
|
||
# 添加用于获取所有模型信息的API
|
||
@app.route('/api/models', methods=['GET'])
|
||
def get_models():
|
||
"""返回可用的模型列表"""
|
||
models = ModelFactory.get_available_models()
|
||
return jsonify(models)
|
||
|
||
# 获取所有API密钥
|
||
@app.route('/api/keys', methods=['GET'])
|
||
def get_api_keys():
|
||
"""获取所有API密钥"""
|
||
api_keys = load_api_keys()
|
||
return jsonify(api_keys)
|
||
|
||
# 保存API密钥
|
||
@app.route('/api/keys', methods=['POST'])
|
||
def update_api_keys():
|
||
"""更新API密钥配置"""
|
||
try:
|
||
new_keys = request.json
|
||
if not isinstance(new_keys, dict):
|
||
return jsonify({"success": False, "message": "无效的API密钥格式"}), 400
|
||
|
||
# 加载当前密钥
|
||
current_keys = load_api_keys()
|
||
|
||
# 更新密钥
|
||
for key, value in new_keys.items():
|
||
current_keys[key] = value
|
||
|
||
# 保存回文件
|
||
if save_api_keys(current_keys):
|
||
return jsonify({"success": True, "message": "API密钥已保存"})
|
||
else:
|
||
return jsonify({"success": False, "message": "保存API密钥失败"}), 500
|
||
|
||
except Exception as e:
|
||
return jsonify({"success": False, "message": f"更新API密钥错误: {str(e)}"}), 500
|
||
|
||
# 加载API密钥配置
|
||
def load_api_keys():
|
||
"""从配置文件加载API密钥"""
|
||
try:
|
||
if os.path.exists(API_KEYS_FILE):
|
||
with open(API_KEYS_FILE, 'r', encoding='utf-8') as f:
|
||
return json.load(f)
|
||
else:
|
||
# 如果文件不存在，创建默认配置
|
||
default_keys = {
|
||
"AnthropicApiKey": "",
|
||
"OpenaiApiKey": "",
|
||
"DeepseekApiKey": "",
|
||
"AlibabaApiKey": "",
|
||
"MathpixAppId": "",
|
||
"MathpixAppKey": ""
|
||
}
|
||
save_api_keys(default_keys)
|
||
return default_keys
|
||
except Exception as e:
|
||
print(f"加载API密钥配置失败: {e}")
|
||
return {}
|
||
|
||
# 保存API密钥配置
|
||
def save_api_keys(api_keys):
|
||
"""保存API密钥到配置文件"""
|
||
try:
|
||
# 确保配置目录存在
|
||
os.makedirs(os.path.dirname(API_KEYS_FILE), exist_ok=True)
|
||
|
||
with open(API_KEYS_FILE, 'w', encoding='utf-8') as f:
|
||
json.dump(api_keys, f, ensure_ascii=False, indent=2)
|
||
return True
|
||
except Exception as e:
|
||
print(f"保存API密钥配置失败: {e}")
|
||
return False
|
||
|
||
# 获取特定API密钥
|
||
def get_api_key(key_name):
|
||
"""获取指定的API密钥"""
|
||
api_keys = load_api_keys()
|
||
return api_keys.get(key_name, "")
|
||
|
||
@app.route('/api/models')
|
||
def api_models():
|
||
"""API端点：获取可用模型列表"""
|
||
try:
|
||
# 加载模型配置
|
||
config = load_model_config()
|
||
|
||
# 转换为前端需要的格式
|
||
models = []
|
||
for model_id, model_info in config['models'].items():
|
||
models.append({
|
||
'id': model_id,
|
||
'display_name': model_info.get('name', model_id),
|
||
'is_multimodal': model_info.get('supportsMultimodal', False),
|
||
'is_reasoning': model_info.get('isReasoning', False),
|
||
'description': model_info.get('description', ''),
|
||
'version': model_info.get('version', 'latest')
|
||
})
|
||
|
||
# 返回模型列表
|
||
return jsonify(models)
|
||
except Exception as e:
|
||
print(f"获取模型列表时出错: {e}")
|
||
return jsonify([]), 500
|
||
|
||
@app.route('/api/prompts', methods=['GET'])
|
||
def get_prompts():
|
||
"""API端点：获取所有系统提示词"""
|
||
try:
|
||
prompts = load_prompts()
|
||
return jsonify(prompts)
|
||
except Exception as e:
|
||
print(f"获取提示词列表时出错: {e}")
|
||
return jsonify({"error": str(e)}), 500
|
||
|
||
@app.route('/api/prompts/<prompt_id>', methods=['GET'])
|
||
def get_prompt(prompt_id):
|
||
"""API端点：获取单个系统提示词"""
|
||
try:
|
||
prompts = load_prompts()
|
||
if prompt_id in prompts:
|
||
return jsonify(prompts[prompt_id])
|
||
else:
|
||
return jsonify({"error": "提示词不存在"}), 404
|
||
except Exception as e:
|
||
print(f"获取提示词时出错: {e}")
|
||
return jsonify({"error": str(e)}), 500
|
||
|
||
@app.route('/api/prompts', methods=['POST'])
|
||
def add_prompt():
|
||
"""API端点：添加或更新系统提示词"""
|
||
try:
|
||
data = request.json
|
||
if not data or not isinstance(data, dict):
|
||
return jsonify({"error": "无效的请求数据"}), 400
|
||
|
||
prompt_id = data.get('id')
|
||
if not prompt_id:
|
||
return jsonify({"error": "提示词ID不能为空"}), 400
|
||
|
||
prompt_data = {
|
||
"name": data.get('name', f"提示词{prompt_id}"),
|
||
"content": data.get('content', ""),
|
||
"description": data.get('description', "")
|
||
}
|
||
|
||
save_prompt(prompt_id, prompt_data)
|
||
return jsonify({"success": True, "id": prompt_id})
|
||
except Exception as e:
|
||
print(f"保存提示词时出错: {e}")
|
||
return jsonify({"error": str(e)}), 500
|
||
|
||
@app.route('/api/prompts/<prompt_id>', methods=['DELETE'])
|
||
def remove_prompt(prompt_id):
|
||
"""API端点：删除系统提示词"""
|
||
try:
|
||
success = delete_prompt(prompt_id)
|
||
if success:
|
||
return jsonify({"success": True})
|
||
else:
|
||
return jsonify({"error": "提示词不存在或删除失败"}), 404
|
||
except Exception as e:
|
||
print(f"删除提示词时出错: {e}")
|
||
return jsonify({"error": str(e)}), 500
|
||
|
||
if __name__ == '__main__':
|
||
local_ip = get_local_ip()
|
||
print(f"Local IP Address: {local_ip}")
|
||
print(f"Connect from your mobile device using: {local_ip}:5000")
|
||
|
||
# 加载模型配置
|
||
model_config = load_model_config()
|
||
if hasattr(ModelFactory, 'update_model_capabilities'):
|
||
ModelFactory.update_model_capabilities(model_config)
|
||
print("已加载模型配置信息")
|
||
|
||
# Run Flask in the main thread without debug mode
|
||
socketio.run(app, host='0.0.0.0', port=5000, allow_unsafe_werkzeug=True)
|