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54 lines
1.6 KiB
54 lines
1.6 KiB
from flask import Flask, request, jsonify
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from flask_cors import CORS
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import asyncio
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from qc_engine import QCManager
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app = Flask(__name__)
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CORS(app, supports_credentials=True)
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# 初始化质控管理器
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qc_manager = QCManager()
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@app.route('/')
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def hello():
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return 'Ultrasound AI QC Engine is running.'
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@app.route('/h', methods=["POST"])
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def zhipu():
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"""
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对接前端的质控接口
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"""
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user_info = request.get_json()
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# 构造传递给 QCManager 的数据
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report_data = {
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"report": user_info.get("report", ""),
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"patient_info": user_info.get("patient_info", {}),
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"examinePart": user_info.get("examinePart", ""),
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"image_urls": user_info.get("image_urls", [])
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}
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# 运行异步质控逻辑
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try:
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# 在 Flask 同步路由中运行异步方法
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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final_analysis = loop.run_until_complete(qc_manager.run_qc(report_data))
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loop.close()
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# 为了兼容旧前端代码，将结果放入 error_report 字段
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# 此时 final_analysis 已经是包含 Markdown 和 JSON 的字符串
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return jsonify({
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'error_report': final_analysis,
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'corrected_report': '建议查看下方的原地纠错建议。'
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})
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except Exception as e:
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print(f"质控运行出错: {e}")
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return jsonify({
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'error_report': f"AI 质控分析暂时不可用: {str(e)}",
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'corrected_report': ''
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}), 500
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if __name__ == '__main__':
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# 注意：生产环境建议使用 gunicorn
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app.run(host='0.0.0.0', port=5000)
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