mirror of
https://github.com/Zippland/Snap-Solver.git
synced 2026-01-19 17:51:12 +08:00
421 lines
14 KiB
Python
421 lines
14 KiB
Python
from flask import Flask, jsonify, render_template, request
|
||
from flask_socketio import SocketIO
|
||
import pyautogui
|
||
import base64
|
||
from io import BytesIO
|
||
import socket
|
||
from threading import Thread
|
||
import pystray
|
||
from PIL import Image, ImageDraw
|
||
import pyperclip
|
||
from models import ModelFactory
|
||
app = Flask(__name__)
|
||
socketio = SocketIO(app, cors_allowed_origins="*", ping_timeout=30, ping_interval=5, max_http_buffer_size=50 * 1024 * 1024)
|
||
|
||
# Commented out due to model file issues
|
||
# from pix2text import Pix2Text
|
||
|
||
def get_local_ip():
|
||
try:
|
||
# Get local IP address
|
||
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
|
||
s.connect(("8.8.8.8", 80))
|
||
ip = s.getsockname()[0]
|
||
s.close()
|
||
return ip
|
||
except Exception:
|
||
return "127.0.0.1"
|
||
|
||
def create_tray_icon():
|
||
# Create a simple icon (a colored circle)
|
||
icon_size = 64
|
||
icon_image = Image.new('RGB', (icon_size, icon_size), color='white')
|
||
draw = ImageDraw.Draw(icon_image)
|
||
draw.ellipse([4, 4, icon_size-4, icon_size-4], fill='#2196F3') # Using the primary color from our CSS
|
||
|
||
# Get server URL
|
||
ip_address = get_local_ip()
|
||
server_url = f"http://{ip_address}:5000"
|
||
|
||
# Create menu
|
||
menu = pystray.Menu(
|
||
pystray.MenuItem(server_url, lambda icon, item: None, enabled=False),
|
||
pystray.MenuItem("Exit", lambda icon, item: icon.stop())
|
||
)
|
||
|
||
# Create icon
|
||
icon = pystray.Icon(
|
||
"SnapSolver",
|
||
icon_image,
|
||
"Snap Solver",
|
||
menu
|
||
)
|
||
|
||
return icon
|
||
|
||
@app.route('/')
|
||
def index():
|
||
local_ip = get_local_ip()
|
||
return render_template('index.html', local_ip=local_ip)
|
||
|
||
@socketio.on('connect')
|
||
def handle_connect():
|
||
print('Client connected')
|
||
|
||
@socketio.on('disconnect')
|
||
def handle_disconnect():
|
||
print('Client disconnected')
|
||
|
||
# Commented out due to model file issues
|
||
# p2t = Pix2Text()
|
||
|
||
def stream_model_response(response_generator, sid):
|
||
"""Stream model responses to the client"""
|
||
try:
|
||
print("Starting response streaming...")
|
||
|
||
# 初始化：发送开始状态
|
||
socketio.emit('claude_response', {
|
||
'status': 'started',
|
||
'content': ''
|
||
}, room=sid)
|
||
print("Sent initial status to client")
|
||
|
||
# 维护服务端缓冲区以累积完整内容
|
||
response_buffer = ""
|
||
thinking_buffer = ""
|
||
|
||
# 流式处理响应
|
||
for response in response_generator:
|
||
# 处理Mathpix响应
|
||
if isinstance(response.get('content', ''), str) and 'mathpix' in response.get('model', ''):
|
||
socketio.emit('text_extracted', {
|
||
'content': response['content']
|
||
}, room=sid)
|
||
continue
|
||
|
||
# 获取状态和内容
|
||
status = response.get('status', '')
|
||
content = response.get('content', '')
|
||
|
||
# 根据不同的状态进行处理
|
||
if status == 'thinking':
|
||
# 累积思考内容到缓冲区
|
||
thinking_buffer += content
|
||
|
||
# 发送完整的思考内容
|
||
socketio.emit('claude_response', {
|
||
'status': 'thinking',
|
||
'content': thinking_buffer
|
||
}, room=sid)
|
||
|
||
elif status == 'thinking_complete':
|
||
# 直接使用完整的思考内容
|
||
thinking_buffer = content # 使用服务器提供的完整内容
|
||
|
||
print(f"Thinking complete, total length: {len(thinking_buffer)} chars")
|
||
socketio.emit('claude_response', {
|
||
'status': 'thinking_complete',
|
||
'content': thinking_buffer
|
||
}, room=sid)
|
||
|
||
elif status == 'streaming':
|
||
# 流式输出正常内容
|
||
if content:
|
||
# 累积到服务端缓冲区
|
||
response_buffer += content
|
||
|
||
# 发送完整的内容
|
||
# print(f"Streaming response content: {len(response_buffer)} chars")
|
||
socketio.emit('claude_response', {
|
||
'status': 'streaming',
|
||
'content': response_buffer
|
||
}, room=sid)
|
||
|
||
else:
|
||
# 其他状态直接转发
|
||
socketio.emit('claude_response', response, room=sid)
|
||
|
||
# 调试信息
|
||
if status == 'completed':
|
||
print("Response completed")
|
||
elif status == 'error':
|
||
print(f"Error: {response.get('error', 'Unknown error')}")
|
||
|
||
except Exception as e:
|
||
error_msg = f"Streaming error: {str(e)}"
|
||
print(error_msg)
|
||
socketio.emit('claude_response', {
|
||
'status': 'error',
|
||
'error': error_msg
|
||
}, room=sid)
|
||
|
||
@socketio.on('request_screenshot')
|
||
def handle_screenshot_request():
|
||
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_response', {
|
||
'success': True,
|
||
'image': img_str
|
||
})
|
||
except Exception as e:
|
||
socketio.emit('screenshot_response', {
|
||
'success': False,
|
||
'error': str(e)
|
||
})
|
||
|
||
@socketio.on('extract_text')
|
||
def handle_text_extraction(data):
|
||
try:
|
||
print("Starting text extraction...")
|
||
|
||
# Validate input data
|
||
if not data or not isinstance(data, dict):
|
||
raise ValueError("Invalid request data")
|
||
|
||
if 'image' not in data:
|
||
raise ValueError("No image data provided")
|
||
|
||
image_data = data['image']
|
||
if not isinstance(image_data, str):
|
||
raise ValueError("Invalid image data format")
|
||
|
||
# 检查图像大小，避免处理过大的图像导致断开连接
|
||
image_size_bytes = len(image_data) * 3 / 4 # 估算base64的实际大小
|
||
if image_size_bytes > 10 * 1024 * 1024: # 10MB
|
||
raise ValueError("Image too large, please crop to a smaller area")
|
||
|
||
settings = data.get('settings', {})
|
||
if not isinstance(settings, dict):
|
||
raise ValueError("Invalid settings format")
|
||
|
||
mathpix_key = settings.get('mathpixApiKey')
|
||
if not mathpix_key:
|
||
raise ValueError("Mathpix API key is required")
|
||
|
||
# 先回复客户端，确认已收到请求，防止超时断开
|
||
# 注意：这里不能使用return，否则后续代码不会执行
|
||
socketio.emit('request_acknowledged', {
|
||
'status': 'received',
|
||
'message': 'Image received, text extraction in progress'
|
||
}, room=request.sid)
|
||
|
||
try:
|
||
app_id, app_key = mathpix_key.split(':')
|
||
if not app_id.strip() or not app_key.strip():
|
||
raise ValueError()
|
||
except ValueError:
|
||
raise ValueError("Invalid Mathpix API key format. Expected format: 'app_id:app_key'")
|
||
|
||
print("Creating Mathpix model instance...")
|
||
model = ModelFactory.create_model(
|
||
model_name='mathpix',
|
||
api_key=mathpix_key
|
||
)
|
||
|
||
print("Starting text extraction...")
|
||
# 使用新的extract_full_text方法直接提取完整文本
|
||
extracted_text = model.extract_full_text(image_data)
|
||
|
||
# 直接返回文本结果
|
||
socketio.emit('text_extracted', {
|
||
'content': extracted_text
|
||
}, room=request.sid)
|
||
|
||
except ValueError as e:
|
||
error_msg = str(e)
|
||
print(f"Validation error: {error_msg}")
|
||
socketio.emit('text_extracted', {
|
||
'error': error_msg
|
||
}, room=request.sid)
|
||
except Exception as e:
|
||
error_msg = f"Text extraction error: {str(e)}"
|
||
print(f"Unexpected error: {error_msg}")
|
||
print(f"Error details: {type(e).__name__}")
|
||
socketio.emit('text_extracted', {
|
||
'error': error_msg
|
||
}, room=request.sid)
|
||
|
||
@socketio.on('analyze_text')
|
||
def handle_analyze_text(data):
|
||
try:
|
||
text = data.get('text')
|
||
settings = data.get('settings', {})
|
||
sid = request.sid
|
||
|
||
print("Selected model:", settings.get('model', 'claude-3-7-sonnet-20250219'))
|
||
|
||
# Get API key and create model
|
||
model_name = settings.get('model', 'claude-3-7-sonnet-20250219')
|
||
api_key = settings.get('api_keys', {}).get(model_name)
|
||
|
||
# Validate required settings
|
||
if not api_key:
|
||
raise ValueError("API key is required for the selected model")
|
||
|
||
# Configure proxy settings if enabled
|
||
proxies = None
|
||
if settings.get('proxyEnabled', False):
|
||
proxy_host = settings.get('proxyHost', '127.0.0.1')
|
||
proxy_port = settings.get('proxyPort', '4780')
|
||
proxies = {
|
||
'http': f'http://{proxy_host}:{proxy_port}',
|
||
'https': f'http://{proxy_host}:{proxy_port}'
|
||
}
|
||
|
||
try:
|
||
# Create model instance using factory
|
||
model = ModelFactory.create_model(
|
||
model_name=model_name,
|
||
api_key=api_key,
|
||
temperature=float(settings.get('temperature', 0.7)),
|
||
system_prompt=settings.get('systemPrompt')
|
||
)
|
||
|
||
# Start streaming in a separate thread
|
||
Thread(
|
||
target=stream_model_response,
|
||
args=(model.analyze_text(text, proxies), sid)
|
||
).start()
|
||
|
||
except Exception as e:
|
||
socketio.emit('claude_response', {
|
||
'status': 'error',
|
||
'error': f'API error: {str(e)}'
|
||
}, room=sid)
|
||
|
||
except Exception as e:
|
||
print(f"Analysis error: {str(e)}")
|
||
socketio.emit('claude_response', {
|
||
'status': 'error',
|
||
'error': f'Analysis error: {str(e)}'
|
||
}, room=request.sid)
|
||
|
||
@socketio.on('analyze_image')
|
||
def handle_analyze_image(data):
|
||
try:
|
||
# 检查数据是否有效
|
||
if not data or not isinstance(data, dict):
|
||
raise ValueError("Invalid request data")
|
||
|
||
image_data = data.get('image')
|
||
if not image_data:
|
||
raise ValueError("No image data provided")
|
||
|
||
# 检查图像大小，避免处理过大的图像导致断开连接
|
||
image_size_bytes = len(image_data) * 3 / 4 # 估算base64的实际大小
|
||
if image_size_bytes > 10 * 1024 * 1024: # 10MB
|
||
raise ValueError("Image too large, please crop to a smaller area or use text extraction")
|
||
|
||
settings = data.get('settings', {})
|
||
|
||
# 不需要分割了，因为前端已经做了分割
|
||
# _, base64_data = image_data_url.split(',', 1)
|
||
base64_data = image_data
|
||
|
||
# Get API key and create model
|
||
model_name = settings.get('model', 'claude-3-7-sonnet-20250219')
|
||
api_key = settings.get('api_keys', {}).get(model_name)
|
||
|
||
# Validate required settings
|
||
if not api_key:
|
||
raise ValueError(f"API key is required for the selected model: {model_name}")
|
||
|
||
# Log with model name for better debugging
|
||
print(f"Using API key for {model_name}: {api_key[:6]}...")
|
||
print("Selected model:", model_name)
|
||
|
||
# Configure proxy settings if enabled
|
||
proxies = None
|
||
if settings.get('proxyEnabled', False):
|
||
proxy_host = settings.get('proxyHost', '127.0.0.1')
|
||
proxy_port = settings.get('proxyPort', '4780')
|
||
proxies = {
|
||
'http': f'http://{proxy_host}:{proxy_port}',
|
||
'https': f'http://{proxy_host}:{proxy_port}'
|
||
}
|
||
|
||
try:
|
||
# 先回复客户端，确认已收到请求，防止超时断开
|
||
# 注意：这里不能使用return，否则后续代码不会执行
|
||
socketio.emit('request_acknowledged', {
|
||
'status': 'received',
|
||
'message': 'Image received, analysis in progress'
|
||
}, room=request.sid)
|
||
|
||
# Create model instance using factory
|
||
model = ModelFactory.create_model(
|
||
model_name=model_name,
|
||
api_key=api_key,
|
||
temperature=float(settings.get('temperature', 0.7)),
|
||
system_prompt=settings.get('systemPrompt')
|
||
)
|
||
|
||
# Start streaming in a separate thread
|
||
Thread(
|
||
target=stream_model_response,
|
||
args=(model.analyze_image(base64_data, proxies), request.sid)
|
||
).start()
|
||
|
||
except Exception as e:
|
||
socketio.emit('claude_response', {
|
||
'status': 'error',
|
||
'error': f'API error: {str(e)}'
|
||
}, room=request.sid)
|
||
|
||
except Exception as e:
|
||
print(f"Analysis error: {str(e)}")
|
||
socketio.emit('claude_response', {
|
||
'status': 'error',
|
||
'error': f'Analysis error: {str(e)}'
|
||
}, room=request.sid)
|
||
|
||
@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 run_tray():
|
||
icon = create_tray_icon()
|
||
icon.run()
|
||
|
||
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")
|
||
|
||
# Run system tray icon in a separate thread
|
||
tray_thread = Thread(target=run_tray)
|
||
tray_thread.daemon = True
|
||
tray_thread.start()
|
||
|
||
# Run Flask in the main thread without debug mode
|
||
socketio.run(app, host='0.0.0.0', port=5000, allow_unsafe_werkzeug=True)
|