feat: cp_app首次提交
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commit
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dist/*
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from flask import Flask, jsonify, request, send_from_directory
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from flask_cors import CORS
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import json
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from docx import Document
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import io
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import fitz # PyMuPDF
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import requests
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import os
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import re
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from flask_jwt_extended import JWTManager, create_access_token, jwt_required, get_jwt_identity
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app = Flask(__name__, static_folder='dist/assets', static_url_path='/assets')
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CORS(app)
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VUE_DIST_DIR = os.path.join(os.path.dirname(__file__), 'dist')
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app.config['JWT_SECRET_KEY'] = 'carbon'
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jwt = JWTManager(app)
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LLM_URL = "http://106.0.4.200:9000/v1/chat/completions"
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API_KEY = "JJVAide0hw3eaugGmxecyYYFw45FX2LfhnYJtC+W2rw"
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MODEL = "Qwen/QwQ-32B"
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HEADERS = {
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"Authorization": f"Bearer {API_KEY}",
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"Content-Type": "application/json"
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}
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OCR_URL = "http://127.0.0.1:3402/ocr_full"
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def get_standard():
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with open("./standard.json", "r", encoding="utf-8") as f:
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standard = json.load(f)
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return standard
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@app.route("/api/standard/", methods=["GET"])
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def get_s():
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return get_standard()
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@app.route('/')
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def index():
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return send_from_directory(VUE_DIST_DIR, 'index.html')
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def get_users():
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with open("./users.json", "r", encoding="utf-8") as f:
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users = json.load(f)
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return users
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@app.route('/api/login/', methods=["POST"])
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def login():
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username = request.json.get("username", "unknown")
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password = request.json.get("password", "unknown")
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users = get_users()
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if username in users and users[username]["password"] == password:
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access = create_access_token(identity=username)
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return jsonify({"access": access, "userInfo": {"username": username, "name": users[username]["name"]}}), 200
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return jsonify({"err_msg": "用户名或密码错误"}), 400
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@app.route("/api/cal/", methods=["POST"])
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@jwt_required()
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def cal():
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data = get_standard()
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file1 = request.files.get("file1", None)
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file2 = request.files.get("file2", None)
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file3 = request.files.get("file3", None)
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file4 = request.files.get("file4", None)
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file5 = request.files.get("file5", None)
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file6 = request.files.get("file6", None)
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if file1 or file2 or file3 or file4 or file5 or file6:
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pass
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else:
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return jsonify({"err_msg": "请至少上传一个文件"}), 400
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total_score = 0
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if file1:
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for item in data:
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if item["thirdLevel"] in [
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"碳中和路线图",
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"短期/中期/长期减碳目标",
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"设立碳管理相关部门",
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"气候相关风险评估机制",
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"内部碳定价机制",
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"碳管理数字化平台建设",
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"碳交易与履约能力",
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"CCER等减排项目开发管理",
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"数字化碳管理平台",
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]:
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item["result"] = item["scoringCriteria"][0]["选项"]
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item["score"] = item["fullScore"]
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total_score += item["score"]
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if file2:
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for item in data:
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if item["thirdLevel"] in [
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"能源与碳排放管理体系",
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"碳排放数据监测、报告与核查",
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"参与权威信息平台披露",
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"碳中和目标与进展经第三方认证",
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"碳排放实时监测覆盖率达标",
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"数据自动化采集比例达标",
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"数据质量与校验机制",
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]:
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item["result"] = item["scoringCriteria"][0]["选项"]
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item["score"] = item["fullScore"]
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total_score += item["score"]
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if file3:
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for item in data:
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if item["thirdLevel"] in [
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"ESG报告",
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"工业固废/生物质资源利用率数据",
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"硫化物减排措施",
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"氮氧化物减排措施",
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"其他污染物减排措施",
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"项目选址生态避让与保护",
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"矿山生态修复与复垦方案",
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"厂区绿化与生态碳汇措施",
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"低碳产品认证与标识",
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"产品耐久性与回收性设计",
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"无环保处罚与信访记录",
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"环境应急管理体系",
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"员工健康安全管理体系与制度",
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"符合标准的物理环境与防护措施",
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"员工心理健康支持计划",
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"社区沟通与透明度机制",
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"社区经济与发展贡献措施",
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"社区负面影响缓解措施",
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"供应商行为准则",
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"供应商筛查与评估机制",
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"供应商审核与改进机制",
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"完善的治理结构",
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"商业道德与反腐败制度",
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]:
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item["result"] = item["scoringCriteria"][0]["选项"]
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item["score"] = item["fullScore"]
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total_score += item["score"]
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if file4:
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for item in data:
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if item["thirdLevel"] in [
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"资金分配明细",
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"资本金比例与到位证明",
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"融资渠道多样性",
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"成本效益分析",
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"碳减排收益量化",
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"社会效益评估",
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"风险管控方案",
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"关键风险应对策略与预案",
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"金融机构或第三方风险分担机制",
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"绿色金融资质认证与资金用途",
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"融资条款与ESG绩效挂钩",
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"国际合作资金申请与利用",
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"应急响应与能力建设机制",
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]:
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item["result"] = item["scoringCriteria"][0]["选项"]
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item["score"] = item["fullScore"]
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total_score += item["score"]
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if file5:
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for item in data:
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if item["thirdLevel"] in [
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"AI预测减碳潜力应用",
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"智能优化控制算法应用",
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"ERP/EMS/MES系统集成度达标",
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"IoT设备覆盖率达标",
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"跨系统数据协同能力",
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"碳数据安全管理措施",
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"系统抗攻击能力达标",
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"数据合规性与审计追踪机制",
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]:
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item["result"] = item["scoringCriteria"][0]["选项"]
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item["score"] = item["fullScore"]
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total_score += item["score"]
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e_filename = None
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e_content, e_err_msg = None, None
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if file6:
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# 获取文件名和类型
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filename = file6.filename
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e_filename = filename
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file_type = filename.rsplit('.', 1)[1].lower() if '.' in filename else None
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content, err_msg = parse_file(file6, file_type)
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e_content = content
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e_err_msg = err_msg
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if content:
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res = ask(f'以下内容为用户报告: {content}', "tec")
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if res == "是":
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for item in data:
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if item["firstLevel"] == "二、技术路径（35 分）":
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item["result"] = item["scoringCriteria"][0]["选项"]
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item["score"] = item["fullScore"]
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total_score += item["score"]
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else:
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return jsonify({"err_msg": err_msg}), 400
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if file1:
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filename = file1.filename
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file_type = filename.rsplit('.', 1)[1].lower() if '.' in filename else None
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if filename == e_filename:
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content, err_msg = e_content, e_err_msg
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else:
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content, err_msg = parse_file(file1, file_type)
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if content:
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if bool(re.search(r'碳?减排目标', content)):
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data[3]["result"] = "有"
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data[3]["score"] = data[3]["fullScore"]
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total_score += data[3]["score"]
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def cal_percent(decline_patterns, content, data, index, total_score):
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decline_percent = None
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for pattern in decline_patterns:
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match = re.search(pattern, content, re.DOTALL)
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if match:
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decline_percent = float(match.group(1))
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break
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if decline_percent:
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if decline_percent >= 10:
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data[index]["result"] = 3
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data[index]["score"] = 5
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elif decline_percent >= 5:
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data[index]["result"] = 2
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data[index]["score"] = 2.5
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elif decline_percent > 0:
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data[index]["result"] = 1
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data[index]["score"] = 1.5
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total_score += data[index].get("score", 0)
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return total_score
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# 碳排放总量
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decline_patterns1 = [
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r'碳排放总量[^，。]*?下降\s*([\d.]+)%',
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r'碳排放[^，。]*?总量[^，。]*?下降\s*([\d.]+)%',
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r'碳总量[^，。]*?下降\s*([\d.]+)%',
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r'排放总量[^，。]*?下降\s*([\d.]+)%',
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r'排放[^，。]*?下降\s*([\d.]+)%'
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]
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total_score = cal_percent(decline_patterns1, content, data, 0, total_score)
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# 碳排放强度
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decline_patterns2 = [
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r'碳排放强度[^，。]*?下降\s*([\d.]+)%',
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r'碳强度[^，。]*?总量[^，。]*?下降\s*([\d.]+)%',
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r'排放强度[^，。]*?下降\s*([\d.]+)%'
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]
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total_score = cal_percent(decline_patterns2, content, data, 1, total_score)
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# 产品碳足迹
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decline_patterns3 = [
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r'产品碳足迹[^，。]*?下降\s*([\d.]+)%',
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r'碳足迹[^，。]*?下降\s*([\d.]+)%',
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r'产品足迹[^，。]*?下降\s*([\d.]+)%'
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]
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total_score = cal_percent(decline_patterns3, content, data, 2, total_score)
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else:
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return jsonify({"err_msg": err_msg}), 400
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return jsonify({"total_score": round(total_score, 2), "data": data})
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def ask(input:str, p_name:str, stream=False):
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with open (f"promot/{p_name}.md", "r", encoding="utf-8") as f:
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promot_str = f.read()
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his = [{"role":"system", "content": promot_str}]
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his.append({"role":"user", "content": input})
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payload = {
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"model": MODEL,
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"messages": his,
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"temperature": 0,
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"stream": stream,
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"chat_template_kwargs": {"enable_thinking": False}
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}
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response = requests.post(LLM_URL, headers=HEADERS, json=payload, stream=stream, timeout=(60, 240))
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print(response.json())
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if not stream:
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return response.json()["choices"][0]["message"]["content"]
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def parse_file(file_content, file_type):
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try:
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if file_type == "pdf":
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# 将文件内容转换为字节流
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pdf_bytes = file_content.read()
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pdf_stream = io.BytesIO(pdf_bytes)
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doc = fitz.open(stream=pdf_stream, filetype="pdf")
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text_content = ""
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# 首先尝试直接提取文本
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for page_num in range(len(doc)):
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page = doc[page_num]
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text_content += page.get_text() + "\n"
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t_plain = text_content.strip()
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if t_plain:
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doc.close()
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return t_plain, None
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else:
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# 直接转发字节流
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# resp = requests.post(OCR_URL,
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# files={"pdf": (file_content.filename,
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# pdf_stream,
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# "application/pdf")},
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# timeout=120) # 大文件酌情加长
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# resp.raise_for_status()
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# return resp.json()["full_text"], None
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return None, "无法直接提取文本，请使用OCR处理"
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elif file_type == "docx":
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# 将文件内容转换为字节流
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doc_stream = io.BytesIO(file_content.read())
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doc = Document(doc_stream)
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# 提取所有段落的文本
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text_content = ""
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for paragraph in doc.paragraphs:
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text_content += paragraph.text + "\n"
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# 提取表格中的文本
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for table in doc.tables:
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for row in table.rows:
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for cell in row.cells:
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text_content += cell.text + " "
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text_content += "\n"
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return text_content, None
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# 如果需要支持其他文件类型，可以在这里添加处理逻辑
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elif file_type == "txt":
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text_content = file_content.read().decode("utf-8")
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return text_content, None
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return None, "不支持的文件类型"
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except Exception as e:
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return None, f"文件解析错误: {str(e)}"
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if __name__ == "__main__":
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# get_ocr_engine()
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app.run(debug=True, port=3401)
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@ -0,0 +1,32 @@
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from flask import Flask, request, jsonify
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from paddleocr import PaddleOCR
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import fitz
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from concurrent.futures import ThreadPoolExecutor
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import numpy as np
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from PIL import Image
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import io
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import paddle
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app = Flask(__name__)
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paddle.set_device("gpu")
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ocr = PaddleOCR(use_textline_orientation=True, lang="ch") # PaddleOCR初始化
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executor = ThreadPoolExecutor(max_workers=4) # 并行页
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@app.route("/ocr_full", methods=["POST"])
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def ocr_full():
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pdf_bytes = request.files["pdf"].read()
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doc = fitz.open(stream=pdf_bytes, filetype="pdf")
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results = list(executor.map(_ocr_page, doc))
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doc.close()
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print(results)
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return jsonify({"full_text": "\n\n".join(results)})
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def _ocr_page(page: fitz.Page):
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pix = page.get_pixmap(matrix=fitz.Matrix(300/72, 300/72))
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img = Image.open(io.BytesIO(pix.tobytes("png")))
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result = ocr.predict(np.array(img))
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texts = [line[1][0] for line in result[0]]
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return f"--- 第 {page.number+1} 页 ---\n" + " ".join(texts)
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if __name__ == '__main__':
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app.run(host='127.0.0.1', port=3402, threaded=False)
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# 角色
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你是一位低碳技术专家，具备深厚的专业知识和丰富的实践经验。你能够精准理解用户的报告。
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# 技能
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1. 仔细分析用户提供的技术报告，获取里面的技术关键点。
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2. 根据技术关键点，判断报告中的技术是否属于低碳技术。
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3 回答 是否采用所列绿色能源、原燃料替代、能效提升、低碳水泥、碳捕集等任一技术（关键技术词汇包括石灰石原料替代减碳技术、熟料烧成化石能源替代减碳技术、水泥工厂近零购电改造技术、水泥低碳烧成技术与装备、节能低碳粉磨工艺及装备、水泥窑炉系统隔热保温整体提升技术、矿渣粉磨制备水泥技术、水泥窑富氧燃烧技术、水泥工厂数字化、智能化技术、高贝利特低热/中热硅酸盐水泥及其制备技术、硫铝酸盐和铁铝酸盐水泥及其制备技术、贝利特-硫铝酸钙-硫硅酸钙新型低碳熟料及其制备技术、大掺量固废制备少熟料低碳水泥技术、镁质低碳水泥及其制备技术、热活化高岭土制备低碳水泥技术、全氧燃烧耦合低能耗碳捕集技术、水泥窑炉烟气捕集CO2技术、水泥窑全氧燃烧耦合CO2纯化捕集技术、水泥工业固体胺碳捕集技术、建材矿山均化开采配矿技术、建材矿山生态修复、绿色改造技术、绿色清洁运输技术、水泥超低排放改造、建设碳排放和污染物排放全过程智能管控与评估平台）
|
||||
# 回答要求
|
||||
仅回答 是 或者 否 不要回复其他内容
|
||||
# 以下是用户报告内容
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,10 @@
|
|||
{
|
||||
"user1": {
|
||||
"name": "用户1",
|
||||
"password": "user1234"
|
||||
},
|
||||
"user2": {
|
||||
"name": "用户2",
|
||||
"password": "user5678"
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue