mirror of
https://github.com/Zippland/Snap-Solver.git
synced 2026-01-19 09:41:15 +08:00
577 lines
21 KiB
Python
577 lines
21 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
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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(app, cors_allowed_origins="*", ping_timeout=30, ping_interval=5, max_http_buffer_size=50 * 1024 * 1024)
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# 添加配置文件路径
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CONFIG_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'config')
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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, api_keys, settings):
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"""创建模型实例并配置参数"""
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# 获取模型信息
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model_info = settings.get('modelInfo', {})
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is_reasoning = model_info.get('isReasoning', False)
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provider = model_info.get('provider', '').lower()
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# 确定API密钥ID
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api_key_id = None
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if provider == 'anthropic':
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api_key_id = "AnthropicApiKey"
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elif provider == 'openai':
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api_key_id = "OpenaiApiKey"
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elif provider == 'deepseek':
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api_key_id = "DeepseekApiKey"
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else:
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# 根据模型名称
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if "claude" 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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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
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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('claude_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('claude_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('claude_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('claude_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('claude_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('claude_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('claude_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('claude_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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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('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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api_keys = settings.get('apiKeys', {})
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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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model_instance = create_model_instance(model_id, api_keys, settings)
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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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for response in model_instance.analyze_text(text, proxies=proxies):
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socketio.emit('claude_response', response)
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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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api_keys = settings.get('apiKeys', {})
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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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model_instance = create_model_instance(model_id, api_keys, settings)
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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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for response in model_instance.analyze_image(image_data, proxies=proxies):
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socketio.emit('claude_response', response)
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except Exception as e:
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print(f"Error in analyze_image: {str(e)}")
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traceback.print_exc()
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socketio.emit('error', {'message': f'分析图像时出错: {str(e)}'})
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@socketio.on('capture_screenshot')
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def handle_capture_screenshot(data):
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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_complete', {
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'success': True,
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'image': img_str
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}, room=request.sid)
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except Exception as e:
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error_msg = f"Screenshot error: {str(e)}"
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print(f"Error capturing screenshot: {error_msg}")
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socketio.emit('screenshot_complete', {
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'success': False,
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'error': error_msg
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}, room=request.sid)
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def load_model_config():
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"""加载模型配置信息"""
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try:
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config_path = os.path.join(CONFIG_DIR, 'models.json')
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with open(config_path, 'r', encoding='utf-8') as f:
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config = json.load(f)
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return config
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except Exception as e:
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print(f"加载模型配置失败: {e}")
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return {
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"providers": {},
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"models": {}
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}
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# 替换 before_first_request 装饰器
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def init_model_config():
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"""初始化模型配置"""
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try:
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model_config = load_model_config()
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# 更新ModelFactory的模型信息
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if hasattr(ModelFactory, 'update_model_capabilities'):
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ModelFactory.update_model_capabilities(model_config)
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print("已加载模型配置")
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except Exception as e:
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print(f"初始化模型配置失败: {e}")
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# 在请求处理前注册初始化函数
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@app.before_request
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def before_request_handler():
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# 使用全局变量跟踪是否已初始化
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if not getattr(app, '_model_config_initialized', False):
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init_model_config()
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app._model_config_initialized = True
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# 版本检查函数
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def check_for_updates():
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"""检查GitHub上是否有新版本"""
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try:
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# 读取当前版本信息
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version_file = os.path.join(CONFIG_DIR, 'version.json')
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with open(version_file, 'r', encoding='utf-8') as f:
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version_info = json.load(f)
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current_version = version_info.get('version', '0.0.0')
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repo = version_info.get('github_repo', 'Zippland/Snap-Solver')
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# 请求GitHub API获取最新发布版本
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api_url = f"https://api.github.com/repos/{repo}/releases/latest"
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# 添加User-Agent以符合GitHub API要求
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headers = {'User-Agent': 'Snap-Solver-Update-Checker'}
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response = requests.get(api_url, headers=headers, timeout=5)
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if response.status_code == 200:
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latest_release = response.json()
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latest_version = latest_release.get('tag_name', '').lstrip('v')
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# 如果版本号为空，尝试从名称中提取
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if not latest_version and 'name' in latest_release:
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import re
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version_match = re.search(r'v?(\d+\.\d+\.\d+)', latest_release['name'])
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||
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)
|
||
|
||
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)
|