Merge branch 'main' of https://gitea.theaken.com/donald/DIT_C
This commit is contained in:
19
.env.example
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19
.env.example
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@@ -0,0 +1,19 @@
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# Flask
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FLASK_SECRET_KEY=your-secret-key-change-in-production
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FLASK_DEBUG=True
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# Database (MySQL)
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DB_HOST=your-db-host
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DB_PORT=3306
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DB_NAME=your-db-name
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DB_USER=your-db-user
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DB_PASSWORD=your-db-password
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# Ollama API
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OLLAMA_API_URL=https://your-ollama-api-url
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OLLAMA_DEFAULT_MODEL=qwen2.5:3b
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# Gitea
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GITEA_URL=https://your-gitea-url
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GITEA_USER=your-username
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GITEA_TOKEN=your-token
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34
README.md
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34
README.md
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# DIT_C
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## 快速開始
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```bash
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# 安裝依賴
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pip install -r requirements.txt
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# 設定環境變數
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cp .env.example .env
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# 編輯 .env 填入正確的資料庫與 API 資訊
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# 啟動服務
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python app.py
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```
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## 測試端點
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- 健康檢查: `GET /health`
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- LLM 連線測試: `GET /test-llm`
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## 專案結構
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```
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DIT_C/
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├── app.py # 主程式入口
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├── config.py # 設定檔
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├── models/ # 資料庫模型
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├── routes/ # 路由模組
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├── services/ # 商業邏輯 (含 LLM 服務)
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├── utils/ # 工具函式
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├── templates/ # HTML 模板
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└── static/ # 靜態資源
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```
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46
app.py
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46
app.py
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"""
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DIT_C 主程式入口
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"""
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from flask import Flask, jsonify
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from config import Config
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from models import db
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def create_app(config_class=Config):
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"""應用程式工廠"""
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app = Flask(__name__)
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app.config.from_object(config_class)
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# 初始化擴展
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db.init_app(app)
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# 註冊 Blueprint
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from routes.api import api_bp
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app.register_blueprint(api_bp)
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# 健康檢查端點
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@app.route('/health')
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def health_check():
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return jsonify({"status": "ok", "message": "DIT_C is running"})
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# 測試 LLM 連線
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@app.route('/test-llm')
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def test_llm():
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from services.llm_service import llm_service, LLMServiceError
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try:
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models = llm_service.get_available_models()
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return jsonify({
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"status": "ok",
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"available_models": models,
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"default_model": Config.OLLAMA_DEFAULT_MODEL
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})
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except LLMServiceError as e:
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return jsonify({"status": "error", "message": str(e)}), 500
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return app
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if __name__ == '__main__':
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app = create_app()
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app.run(host='0.0.0.0', port=5000, debug=Config.DEBUG)
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37
config.py
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37
config.py
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import os
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from dotenv import load_dotenv
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load_dotenv()
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class Config:
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"""應用程式設定"""
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# Flask
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SECRET_KEY = os.getenv('FLASK_SECRET_KEY', 'dev-secret-key')
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DEBUG = os.getenv('FLASK_DEBUG', 'False').lower() == 'true'
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# Database (MySQL)
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DB_HOST = os.getenv('DB_HOST', 'localhost')
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DB_PORT = int(os.getenv('DB_PORT', 3306))
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DB_NAME = os.getenv('DB_NAME', 'database')
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DB_USER = os.getenv('DB_USER', 'root')
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DB_PASSWORD = os.getenv('DB_PASSWORD', '')
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# SQLAlchemy 連線字串
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SQLALCHEMY_DATABASE_URI = (
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f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}:{DB_PORT}/{DB_NAME}"
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)
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SQLALCHEMY_TRACK_MODIFICATIONS = False
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# Table 前綴
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TABLE_PREFIX = 'DIT_C_'
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# Ollama API
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OLLAMA_API_URL = os.getenv('OLLAMA_API_URL', 'https://ollama_pjapi.theaken.com')
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OLLAMA_DEFAULT_MODEL = os.getenv('OLLAMA_DEFAULT_MODEL', 'qwen2.5:3b')
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# Gitea
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GITEA_URL = os.getenv('GITEA_URL', '')
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GITEA_USER = os.getenv('GITEA_USER', '')
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GITEA_TOKEN = os.getenv('GITEA_TOKEN', '')
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3
models/__init__.py
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3
models/__init__.py
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@@ -0,0 +1,3 @@
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from flask_sqlalchemy import SQLAlchemy
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db = SQLAlchemy()
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10
requirements.txt
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10
requirements.txt
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@@ -0,0 +1,10 @@
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flask>=3.0.0
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flask-sqlalchemy>=3.1.0
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flask-login>=0.6.3
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python-dotenv>=1.0.0
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pymysql>=1.1.0
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cryptography>=41.0.0
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requests>=2.31.0
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pandas>=2.0.0
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numpy>=1.24.0
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werkzeug>=3.0.0
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1
routes/__init__.py
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1
routes/__init__.py
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@@ -0,0 +1 @@
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# Routes module
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155
routes/api.py
Normal file
155
routes/api.py
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"""
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DIT 分析 API 路由
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"""
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import os
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import json
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from flask import Blueprint, request, jsonify, current_app
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from werkzeug.utils import secure_filename
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from services.dit_analyzer import DITAnalyzer, DITAnalyzerError
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api_bp = Blueprint('api', __name__, url_prefix='/api')
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ALLOWED_EXTENSIONS = {'csv'}
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UPLOAD_FOLDER = 'uploads'
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def allowed_file(filename: str) -> bool:
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"""檢查檔案副檔名"""
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return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
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@api_bp.route('/analyze', methods=['POST'])
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def analyze_dit():
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"""
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分析 DIT CSV 檔案
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接受 multipart/form-data 上傳 CSV
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回傳 JSON 格式分析結果
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"""
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# 檢查檔案
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if 'file' not in request.files:
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return jsonify({"error": "未上傳檔案", "code": "NO_FILE"}), 400
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file = request.files['file']
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if file.filename == '':
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return jsonify({"error": "未選擇檔案", "code": "NO_FILENAME"}), 400
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||||
if not allowed_file(file.filename):
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return jsonify({"error": "僅支援 CSV 檔案", "code": "INVALID_TYPE"}), 400
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# 取得參數
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top_percent = float(request.form.get('top_percent', 0.2))
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low_win_rate = float(request.form.get('low_win_rate', 0.1))
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threshold_days = int(request.form.get('threshold_days', 60))
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try:
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# 確保上傳目錄存在
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os.makedirs(UPLOAD_FOLDER, exist_ok=True)
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# 儲存檔案
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filename = secure_filename(file.filename)
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filepath = os.path.join(UPLOAD_FOLDER, filename)
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file.save(filepath)
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# 執行分析
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analyzer = DITAnalyzer(filepath)
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report = analyzer.generate_report(
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top_percent=top_percent,
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low_win_rate=low_win_rate,
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threshold_days=threshold_days
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)
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# 清理暫存檔
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os.remove(filepath)
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return jsonify({
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"status": "success",
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"data": report
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})
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except DITAnalyzerError as e:
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return jsonify({"error": str(e), "code": "ANALYZER_ERROR"}), 400
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except Exception as e:
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return jsonify({"error": f"分析失敗: {str(e)}", "code": "INTERNAL_ERROR"}), 500
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@api_bp.route('/analyze/resource-allocation', methods=['POST'])
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def analyze_resource_allocation():
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"""
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僅執行 Feature 6.2: 高價值資源分配分析
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"""
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if 'file' not in request.files:
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return jsonify({"error": "未上傳檔案", "code": "NO_FILE"}), 400
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|
||||
file = request.files['file']
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||||
if not allowed_file(file.filename):
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return jsonify({"error": "僅支援 CSV 檔案", "code": "INVALID_TYPE"}), 400
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top_percent = float(request.form.get('top_percent', 0.2))
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low_win_rate = float(request.form.get('low_win_rate', 0.1))
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try:
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||||
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
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||||
filename = secure_filename(file.filename)
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filepath = os.path.join(UPLOAD_FOLDER, filename)
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file.save(filepath)
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analyzer = DITAnalyzer(filepath)
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results = analyzer.analyze_resource_allocation(top_percent, low_win_rate)
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os.remove(filepath)
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return jsonify({
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||||
"status": "success",
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||||
"data": {
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||||
"type": "resource_allocation",
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||||
"count": len(results),
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"action_cards": results
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||||
}
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||||
})
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||||
|
||||
except DITAnalyzerError as e:
|
||||
return jsonify({"error": str(e), "code": "ANALYZER_ERROR"}), 400
|
||||
except Exception as e:
|
||||
return jsonify({"error": f"分析失敗: {str(e)}", "code": "INTERNAL_ERROR"}), 500
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||||
|
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@api_bp.route('/analyze/stagnant-deals', methods=['POST'])
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def analyze_stagnant_deals():
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"""
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僅執行 Feature 6.3: 呆滯案件警示
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||||
"""
|
||||
if 'file' not in request.files:
|
||||
return jsonify({"error": "未上傳檔案", "code": "NO_FILE"}), 400
|
||||
|
||||
file = request.files['file']
|
||||
if not allowed_file(file.filename):
|
||||
return jsonify({"error": "僅支援 CSV 檔案", "code": "INVALID_TYPE"}), 400
|
||||
|
||||
threshold_days = int(request.form.get('threshold_days', 60))
|
||||
|
||||
try:
|
||||
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
|
||||
filename = secure_filename(file.filename)
|
||||
filepath = os.path.join(UPLOAD_FOLDER, filename)
|
||||
file.save(filepath)
|
||||
|
||||
analyzer = DITAnalyzer(filepath)
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||||
results = analyzer.analyze_stagnant_deals(threshold_days)
|
||||
|
||||
os.remove(filepath)
|
||||
|
||||
return jsonify({
|
||||
"status": "success",
|
||||
"data": {
|
||||
"type": "stagnant_deals",
|
||||
"count": len(results),
|
||||
"action_cards": results
|
||||
}
|
||||
})
|
||||
|
||||
except DITAnalyzerError as e:
|
||||
return jsonify({"error": str(e), "code": "ANALYZER_ERROR"}), 400
|
||||
except Exception as e:
|
||||
return jsonify({"error": f"分析失敗: {str(e)}", "code": "INTERNAL_ERROR"}), 500
|
||||
1
services/__init__.py
Normal file
1
services/__init__.py
Normal file
@@ -0,0 +1 @@
|
||||
# Services module
|
||||
323
services/dit_analyzer.py
Normal file
323
services/dit_analyzer.py
Normal file
@@ -0,0 +1,323 @@
|
||||
"""
|
||||
DIT 智能分析模組
|
||||
解析 DIT CSV 報表，產出行動建議卡片 (Action Cards)
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from datetime import datetime
|
||||
from typing import List, Dict, Optional, Any
|
||||
|
||||
|
||||
class DITAnalyzer:
|
||||
"""DIT 報表分析器"""
|
||||
|
||||
def __init__(self, file_path: Optional[str] = None, dataframe: Optional[pd.DataFrame] = None):
|
||||
"""
|
||||
初始化分析器
|
||||
|
||||
Args:
|
||||
file_path: CSV 檔案路徑
|
||||
dataframe: 或直接傳入 DataFrame
|
||||
"""
|
||||
self.df: Optional[pd.DataFrame] = None
|
||||
self.processed: bool = False
|
||||
|
||||
if file_path:
|
||||
self.load_data(file_path)
|
||||
elif dataframe is not None:
|
||||
self.df = dataframe.copy()
|
||||
self._preprocess()
|
||||
|
||||
def load_data(self, file_path: str) -> 'DITAnalyzer':
|
||||
"""
|
||||
載入 CSV 資料
|
||||
|
||||
Args:
|
||||
file_path: CSV 檔案路徑
|
||||
|
||||
Returns:
|
||||
self (支援鏈式呼叫)
|
||||
"""
|
||||
try:
|
||||
self.df = pd.read_csv(file_path, encoding='utf-8')
|
||||
except UnicodeDecodeError:
|
||||
self.df = pd.read_csv(file_path, encoding='cp950')
|
||||
except Exception as e:
|
||||
raise DITAnalyzerError(f"無法載入檔案: {e}")
|
||||
|
||||
self._preprocess()
|
||||
return self
|
||||
|
||||
def _preprocess(self) -> None:
|
||||
"""執行資料清洗與預處理"""
|
||||
if self.df is None:
|
||||
raise DITAnalyzerError("尚未載入資料")
|
||||
|
||||
# 1. 欄位清洗：移除欄位名稱前後空白
|
||||
self.df.columns = self.df.columns.str.strip()
|
||||
|
||||
# 2. 日期轉換
|
||||
date_columns = ['Created Date', 'Approved date', 'Close Date']
|
||||
for col in date_columns:
|
||||
if col in self.df.columns:
|
||||
self.df[col] = pd.to_datetime(self.df[col], errors='coerce')
|
||||
|
||||
# 3. 數值轉換：Total Price
|
||||
if 'Total Price' in self.df.columns:
|
||||
self.df['Total Price'] = pd.to_numeric(
|
||||
self.df['Total Price'].astype(str).str.replace(',', ''),
|
||||
errors='coerce'
|
||||
).fillna(0)
|
||||
|
||||
# 4. 應用領域推導 (Derived_Application)
|
||||
self.df['Derived_Application'] = self._derive_application()
|
||||
|
||||
# 5. 狀態標記
|
||||
if 'Stage' in self.df.columns:
|
||||
self.df['Is_Lost'] = self.df['Stage'].str.contains(
|
||||
'Lost', case=False, na=False
|
||||
)
|
||||
self.df['Is_Active'] = ~self.df['Is_Lost']
|
||||
else:
|
||||
self.df['Is_Lost'] = False
|
||||
self.df['Is_Active'] = True
|
||||
|
||||
self.processed = True
|
||||
|
||||
def _derive_application(self) -> pd.Series:
|
||||
"""
|
||||
推導應用領域
|
||||
優先順序: Application → Application Detail → Opportunity Name → "Unknown"
|
||||
"""
|
||||
def get_app(row):
|
||||
# 檢查 Application
|
||||
if 'Application' in row.index:
|
||||
val = row.get('Application')
|
||||
if pd.notna(val) and str(val).strip():
|
||||
return str(val).strip()
|
||||
|
||||
# 檢查 Application Detail
|
||||
if 'Application Detail' in row.index:
|
||||
val = row.get('Application Detail')
|
||||
if pd.notna(val) and str(val).strip():
|
||||
return str(val).strip()
|
||||
|
||||
# 檢查 Opportunity Name
|
||||
if 'Opportunity Name' in row.index:
|
||||
val = row.get('Opportunity Name')
|
||||
if pd.notna(val) and str(val).strip():
|
||||
return str(val).strip()
|
||||
|
||||
return "Unknown"
|
||||
|
||||
return self.df.apply(get_app, axis=1)
|
||||
|
||||
def analyze_resource_allocation(
|
||||
self,
|
||||
top_percent: float = 0.2,
|
||||
low_win_rate: float = 0.1
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Feature 6.2: 高價值資源分配建議
|
||||
|
||||
找出「金礦區」— 金額大但勝率低的應用領域
|
||||
|
||||
Args:
|
||||
top_percent: 金額排名前 X% (預設 20%)
|
||||
low_win_rate: 勝率門檻 (預設 10%)
|
||||
|
||||
Returns:
|
||||
Action Cards 列表
|
||||
"""
|
||||
if not self.processed:
|
||||
raise DITAnalyzerError("資料尚未預處理")
|
||||
|
||||
# 依 Derived_Application 分組
|
||||
grouped = self.df.groupby('Derived_Application').agg({
|
||||
'Total Price': 'sum',
|
||||
'Is_Active': 'mean',
|
||||
'Account Name': lambda x: x.value_counts().head(3).index.tolist()
|
||||
}).reset_index()
|
||||
|
||||
grouped.columns = ['Application', 'Sum_Total_Price', 'Win_Rate', 'Top_Accounts']
|
||||
|
||||
# 排序並取 Top 20%
|
||||
grouped = grouped.sort_values('Sum_Total_Price', ascending=False)
|
||||
top_n = max(1, int(len(grouped) * top_percent))
|
||||
top_apps = grouped.head(top_n)
|
||||
|
||||
# 篩選勝率低於門檻的
|
||||
low_win_apps = top_apps[top_apps['Win_Rate'] < low_win_rate]
|
||||
|
||||
# 產出 Action Cards
|
||||
action_cards = []
|
||||
for _, row in low_win_apps.iterrows():
|
||||
money_formatted = f"${row['Sum_Total_Price']:,.0f}"
|
||||
win_rate_pct = f"{row['Win_Rate'] * 100:.1f}"
|
||||
top_accounts = ', '.join(row['Top_Accounts'][:3]) if row['Top_Accounts'] else '無'
|
||||
|
||||
action_cards.append({
|
||||
"type": "resource_allocation",
|
||||
"title": "高潛力市場攻堅提醒",
|
||||
"application": row['Application'],
|
||||
"money": money_formatted,
|
||||
"money_raw": row['Sum_Total_Price'],
|
||||
"win_rate": win_rate_pct,
|
||||
"win_rate_raw": row['Win_Rate'],
|
||||
"top_accounts": row['Top_Accounts'][:3] if row['Top_Accounts'] else [],
|
||||
"suggestion": (
|
||||
f"{row['Application']} 領域潛在商機巨大 ({money_formatted})，"
|
||||
f"但目前勝率偏低 ({win_rate_pct}%)。"
|
||||
f"建議指派資深 FAE 介入該領域的前三大案子 (如 {top_accounts})。"
|
||||
)
|
||||
})
|
||||
|
||||
return action_cards
|
||||
|
||||
def analyze_stagnant_deals(
|
||||
self,
|
||||
threshold_days: int = 60,
|
||||
reference_date: Optional[datetime] = None
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Feature 6.3: 呆滯案件警示
|
||||
|
||||
針對技術已承認但商務卡關的案子進行催單
|
||||
|
||||
Args:
|
||||
threshold_days: 呆滯天數門檻 (預設 60 天)
|
||||
reference_date: 參考日期 (預設為當前日期)
|
||||
|
||||
Returns:
|
||||
Action Cards 列表
|
||||
"""
|
||||
if not self.processed:
|
||||
raise DITAnalyzerError("資料尚未預處理")
|
||||
|
||||
if reference_date is None:
|
||||
reference_date = datetime.now()
|
||||
|
||||
# 檢查必要欄位
|
||||
if 'Approved date' not in self.df.columns:
|
||||
return []
|
||||
|
||||
# 篩選條件
|
||||
mask = (
|
||||
(self.df['Stage'].str.contains('Negotiation', case=False, na=False)) &
|
||||
(self.df['Approved date'].notna())
|
||||
)
|
||||
filtered = self.df[mask].copy()
|
||||
|
||||
if filtered.empty:
|
||||
return []
|
||||
|
||||
# 計算呆滯天數
|
||||
filtered['Days_Since_Approved'] = (
|
||||
reference_date - filtered['Approved date']
|
||||
).dt.days
|
||||
|
||||
# 篩選超過門檻的
|
||||
stagnant = filtered[filtered['Days_Since_Approved'] > threshold_days]
|
||||
|
||||
# 產出 Action Cards
|
||||
action_cards = []
|
||||
for _, row in stagnant.iterrows():
|
||||
days = int(row['Days_Since_Approved'])
|
||||
months = days // 30
|
||||
account = row.get('Account Name', 'Unknown')
|
||||
project = row.get('Opportunity Name', 'Unknown')
|
||||
approved_date = row['Approved date'].strftime('%Y-%m-%d') if pd.notna(row['Approved date']) else 'N/A'
|
||||
|
||||
action_cards.append({
|
||||
"type": "stagnant_deal",
|
||||
"title": "呆滯案件喚醒",
|
||||
"account": account,
|
||||
"project": project,
|
||||
"approved_date": approved_date,
|
||||
"days_pending": days,
|
||||
"months_pending": months,
|
||||
"suggestion": (
|
||||
f"客戶 {account} 的 {project} 已承認超過 {months} 個月 ({days} 天)，仍未轉單。"
|
||||
f"請業務確認是否為「價格」或「庫存」問題。若無下文，應要求客戶給出 Forecast。"
|
||||
)
|
||||
})
|
||||
|
||||
# 依天數排序 (最久的在前)
|
||||
action_cards.sort(key=lambda x: x['days_pending'], reverse=True)
|
||||
|
||||
return action_cards
|
||||
|
||||
def generate_report(
|
||||
self,
|
||||
top_percent: float = 0.2,
|
||||
low_win_rate: float = 0.1,
|
||||
threshold_days: int = 60
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
彙整所有分析結果
|
||||
|
||||
Args:
|
||||
top_percent: 高價值分析的金額門檻
|
||||
low_win_rate: 高價值分析的勝率門檻
|
||||
threshold_days: 呆滯分析的天數門檻
|
||||
|
||||
Returns:
|
||||
完整分析報告 (Dict)
|
||||
"""
|
||||
if not self.processed:
|
||||
raise DITAnalyzerError("資料尚未預處理")
|
||||
|
||||
allocation_suggestions = self.analyze_resource_allocation(top_percent, low_win_rate)
|
||||
stagnant_alerts = self.analyze_stagnant_deals(threshold_days)
|
||||
|
||||
# 統計摘要
|
||||
summary = self._generate_summary()
|
||||
|
||||
return {
|
||||
"generated_at": datetime.now().isoformat(),
|
||||
"summary": summary,
|
||||
"action_cards": {
|
||||
"resource_allocation": allocation_suggestions,
|
||||
"stagnant_deals": stagnant_alerts
|
||||
},
|
||||
"total_alerts": len(allocation_suggestions) + len(stagnant_alerts)
|
||||
}
|
||||
|
||||
def _generate_summary(self) -> Dict[str, Any]:
|
||||
"""產生統計摘要"""
|
||||
total_records = len(self.df)
|
||||
total_value = self.df['Total Price'].sum()
|
||||
active_count = self.df['Is_Active'].sum()
|
||||
lost_count = self.df['Is_Lost'].sum()
|
||||
|
||||
# 各階段統計
|
||||
stage_stats = {}
|
||||
if 'Stage' in self.df.columns:
|
||||
stage_stats = self.df['Stage'].value_counts().to_dict()
|
||||
|
||||
# 應用領域 Top 5
|
||||
app_stats = self.df.groupby('Derived_Application')['Total Price'].sum()
|
||||
top_apps = app_stats.nlargest(5).to_dict()
|
||||
|
||||
return {
|
||||
"total_records": total_records,
|
||||
"total_value": f"${total_value:,.0f}",
|
||||
"total_value_raw": total_value,
|
||||
"active_count": int(active_count),
|
||||
"lost_count": int(lost_count),
|
||||
"win_rate": f"{(active_count / total_records * 100):.1f}%" if total_records > 0 else "0%",
|
||||
"stage_distribution": stage_stats,
|
||||
"top_applications": {k: f"${v:,.0f}" for k, v in top_apps.items()}
|
||||
}
|
||||
|
||||
def get_dataframe(self) -> pd.DataFrame:
|
||||
"""取得處理後的 DataFrame"""
|
||||
if self.df is None:
|
||||
raise DITAnalyzerError("尚未載入資料")
|
||||
return self.df.copy()
|
||||
|
||||
|
||||
class DITAnalyzerError(Exception):
|
||||
"""DIT 分析器錯誤"""
|
||||
pass
|
||||
150
services/llm_service.py
Normal file
150
services/llm_service.py
Normal file
@@ -0,0 +1,150 @@
|
||||
"""
|
||||
Ollama LLM API 服務模組
|
||||
支援一般請求與串流模式
|
||||
"""
|
||||
|
||||
import requests
|
||||
import json
|
||||
from typing import Generator, Optional
|
||||
from config import Config
|
||||
|
||||
|
||||
class LLMService:
|
||||
"""Ollama API 服務封裝"""
|
||||
|
||||
def __init__(self, api_url: str = None, default_model: str = None):
|
||||
self.api_url = api_url or Config.OLLAMA_API_URL
|
||||
self.default_model = default_model or Config.OLLAMA_DEFAULT_MODEL
|
||||
|
||||
def get_available_models(self) -> list:
|
||||
"""取得可用模型列表"""
|
||||
try:
|
||||
response = requests.get(f"{self.api_url}/v1/models", timeout=10)
|
||||
response.raise_for_status()
|
||||
models = response.json()
|
||||
return [m['id'] for m in models.get('data', [])]
|
||||
except requests.RequestException as e:
|
||||
raise LLMServiceError(f"無法取得模型列表: {e}")
|
||||
|
||||
def chat(
|
||||
self,
|
||||
messages: list,
|
||||
model: str = None,
|
||||
temperature: float = 0.7,
|
||||
system_prompt: str = None
|
||||
) -> str:
|
||||
"""
|
||||
發送聊天請求 (非串流)
|
||||
|
||||
Args:
|
||||
messages: 訊息列表 [{"role": "user", "content": "..."}]
|
||||
model: 模型名稱
|
||||
temperature: 溫度參數 (0-1)
|
||||
system_prompt: 系統提示詞
|
||||
|
||||
Returns:
|
||||
AI 回應內容
|
||||
"""
|
||||
model = model or self.default_model
|
||||
|
||||
# 加入系統提示詞
|
||||
if system_prompt:
|
||||
messages = [{"role": "system", "content": system_prompt}] + messages
|
||||
|
||||
payload = {
|
||||
"model": model,
|
||||
"messages": messages,
|
||||
"temperature": temperature,
|
||||
"stream": False
|
||||
}
|
||||
|
||||
try:
|
||||
response = requests.post(
|
||||
f"{self.api_url}/v1/chat/completions",
|
||||
json=payload,
|
||||
timeout=60
|
||||
)
|
||||
response.raise_for_status()
|
||||
result = response.json()
|
||||
return result['choices'][0]['message']['content']
|
||||
except requests.RequestException as e:
|
||||
raise LLMServiceError(f"聊天請求失敗: {e}")
|
||||
|
||||
def chat_stream(
|
||||
self,
|
||||
messages: list,
|
||||
model: str = None,
|
||||
temperature: float = 0.7,
|
||||
system_prompt: str = None
|
||||
) -> Generator[str, None, None]:
|
||||
"""
|
||||
發送聊天請求 (串流模式)
|
||||
|
||||
Args:
|
||||
messages: 訊息列表
|
||||
model: 模型名稱
|
||||
temperature: 溫度參數
|
||||
system_prompt: 系統提示詞
|
||||
|
||||
Yields:
|
||||
串流回應的每個片段
|
||||
"""
|
||||
model = model or self.default_model
|
||||
|
||||
if system_prompt:
|
||||
messages = [{"role": "system", "content": system_prompt}] + messages
|
||||
|
||||
payload = {
|
||||
"model": model,
|
||||
"messages": messages,
|
||||
"temperature": temperature,
|
||||
"stream": True
|
||||
}
|
||||
|
||||
try:
|
||||
response = requests.post(
|
||||
f"{self.api_url}/v1/chat/completions",
|
||||
json=payload,
|
||||
stream=True,
|
||||
timeout=120
|
||||
)
|
||||
response.raise_for_status()
|
||||
|
||||
for line in response.iter_lines():
|
||||
if line:
|
||||
if line.startswith(b"data: "):
|
||||
data_str = line[6:].decode('utf-8')
|
||||
if data_str.strip() != "[DONE]":
|
||||
try:
|
||||
data = json.loads(data_str)
|
||||
if 'choices' in data:
|
||||
delta = data['choices'][0].get('delta', {})
|
||||
if 'content' in delta:
|
||||
yield delta['content']
|
||||
except json.JSONDecodeError:
|
||||
continue
|
||||
except requests.RequestException as e:
|
||||
raise LLMServiceError(f"串流請求失敗: {e}")
|
||||
|
||||
def simple_query(self, prompt: str, system_prompt: str = None) -> str:
|
||||
"""
|
||||
簡單查詢 (單一問題)
|
||||
|
||||
Args:
|
||||
prompt: 使用者問題
|
||||
system_prompt: 系統提示詞
|
||||
|
||||
Returns:
|
||||
AI 回應
|
||||
"""
|
||||
messages = [{"role": "user", "content": prompt}]
|
||||
return self.chat(messages, system_prompt=system_prompt)
|
||||
|
||||
|
||||
class LLMServiceError(Exception):
|
||||
"""LLM 服務錯誤"""
|
||||
pass
|
||||
|
||||
|
||||
# 全域實例
|
||||
llm_service = LLMService()
|
||||
0
tests/__init__.py
Normal file
0
tests/__init__.py
Normal file
153
tests/test_dit_analyzer.py
Normal file
153
tests/test_dit_analyzer.py
Normal file
@@ -0,0 +1,153 @@
|
||||
"""
|
||||
DITAnalyzer Test Script
|
||||
"""
|
||||
|
||||
import sys
|
||||
import os
|
||||
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
|
||||
import pandas as pd
|
||||
from datetime import datetime, timedelta
|
||||
from services.dit_analyzer import DITAnalyzer, DITAnalyzerError
|
||||
|
||||
|
||||
def create_sample_data():
|
||||
"""Create sample test data"""
|
||||
days_ago_90 = datetime.now() - timedelta(days=90)
|
||||
days_ago_30 = datetime.now() - timedelta(days=30)
|
||||
|
||||
data = {
|
||||
'Created Date': ['2024-01-01', '2024-02-01', '2024-03-01', '2024-04-01', '2024-05-01',
|
||||
'2024-01-15', '2024-02-15', '2024-03-15', '2024-04-15', '2024-05-15'],
|
||||
'Account Name': ['CustomerA', 'CustomerB', 'CustomerA', 'CustomerC', 'CustomerD',
|
||||
'CustomerE', 'CustomerF', 'CustomerA', 'CustomerG', 'CustomerH'],
|
||||
'Stage': ['Won', 'Opportunity Lost', 'Negotiation', 'Won', 'Design-Lost',
|
||||
'Negotiation', 'Mass Production', 'Opportunity Lost', 'Negotiation', 'Won'],
|
||||
'Application': ['Automotive', '', 'Automotive', 'IoT', '',
|
||||
'Automotive', 'Consumer', '', 'Industrial', 'Automotive'],
|
||||
'Application Detail': ['', 'Consumer Electronics', '', '', 'Smart Home',
|
||||
'', '', 'Power Supply', '', ''],
|
||||
'Opportunity Name': ['Project Alpha', 'Project Beta', 'Project Gamma', 'Project Delta', 'Project Epsilon',
|
||||
'Project Zeta', 'Project Eta', 'Project Theta', 'Project Iota', 'Project Kappa'],
|
||||
'Total Price': [500000, 300000, 800000, 150000, 200000,
|
||||
1200000, 50000, 100000, 450000, 600000],
|
||||
'Approved date': [None, None, days_ago_90.strftime('%Y-%m-%d'), None, None,
|
||||
days_ago_90.strftime('%Y-%m-%d'), None, None, days_ago_30.strftime('%Y-%m-%d'), None],
|
||||
'Lost Type': ['', 'Price', '', '', 'Spec',
|
||||
'', '', 'Price', '', '']
|
||||
}
|
||||
|
||||
return pd.DataFrame(data)
|
||||
|
||||
|
||||
def test_preprocess():
|
||||
"""Test data preprocessing"""
|
||||
print("=" * 50)
|
||||
print("Test 1: Data Preprocessing")
|
||||
print("=" * 50)
|
||||
|
||||
df = create_sample_data()
|
||||
analyzer = DITAnalyzer(dataframe=df)
|
||||
|
||||
processed_df = analyzer.get_dataframe()
|
||||
|
||||
print(f"Total records: {len(processed_df)}")
|
||||
print(f"Columns: {list(processed_df.columns)}")
|
||||
|
||||
assert 'Derived_Application' in processed_df.columns
|
||||
assert 'Is_Lost' in processed_df.columns
|
||||
assert 'Is_Active' in processed_df.columns
|
||||
print("\n[PASS] Preprocess test passed!")
|
||||
|
||||
|
||||
def test_resource_allocation():
|
||||
"""Test Feature 6.2: High Value Resource Allocation"""
|
||||
print("\n" + "=" * 50)
|
||||
print("Test 2: Feature 6.2 Resource Allocation")
|
||||
print("=" * 50)
|
||||
|
||||
df = create_sample_data()
|
||||
analyzer = DITAnalyzer(dataframe=df)
|
||||
|
||||
results = analyzer.analyze_resource_allocation(top_percent=0.5, low_win_rate=0.5)
|
||||
|
||||
print(f"Found {len(results)} high-value low-win-rate applications")
|
||||
for card in results:
|
||||
print(f"\n[CARD] {card['title']}")
|
||||
print(f" Application: {card['application']}")
|
||||
print(f" Potential Value: {card['money']}")
|
||||
print(f" Win Rate: {card['win_rate']}%")
|
||||
|
||||
print("\n[PASS] Resource allocation test passed!")
|
||||
|
||||
|
||||
def test_stagnant_deals():
|
||||
"""Test Feature 6.3: Stagnant Deal Alert"""
|
||||
print("\n" + "=" * 50)
|
||||
print("Test 3: Feature 6.3 Stagnant Deals")
|
||||
print("=" * 50)
|
||||
|
||||
df = create_sample_data()
|
||||
analyzer = DITAnalyzer(dataframe=df)
|
||||
|
||||
results = analyzer.analyze_stagnant_deals(threshold_days=60)
|
||||
|
||||
print(f"Found {len(results)} stagnant deals")
|
||||
for card in results:
|
||||
print(f"\n[ALERT] {card['title']}")
|
||||
print(f" Account: {card['account']}")
|
||||
print(f" Project: {card['project']}")
|
||||
print(f" Days Pending: {card['days_pending']}")
|
||||
|
||||
print("\n[PASS] Stagnant deals test passed!")
|
||||
|
||||
|
||||
def test_full_report():
|
||||
"""Test full report generation"""
|
||||
print("\n" + "=" * 50)
|
||||
print("Test 4: Full Report Generation")
|
||||
print("=" * 50)
|
||||
|
||||
df = create_sample_data()
|
||||
analyzer = DITAnalyzer(dataframe=df)
|
||||
|
||||
report = analyzer.generate_report(top_percent=0.5, low_win_rate=0.5, threshold_days=60)
|
||||
|
||||
print(f"\n[REPORT] Generated at: {report['generated_at']}")
|
||||
summary = report['summary']
|
||||
print(f" Total Records: {summary['total_records']}")
|
||||
print(f" Total Value: {summary['total_value']}")
|
||||
print(f" Win Rate: {summary['win_rate']}")
|
||||
|
||||
print(f"\n[ACTION CARDS] Total: {report['total_alerts']}")
|
||||
print(f" - Resource Allocation: {len(report['action_cards']['resource_allocation'])}")
|
||||
print(f" - Stagnant Deals: {len(report['action_cards']['stagnant_deals'])}")
|
||||
|
||||
print("\n[PASS] Full report test passed!")
|
||||
|
||||
|
||||
def main():
|
||||
"""Run all tests"""
|
||||
print("\n[START] DITAnalyzer Test Suite\n")
|
||||
|
||||
try:
|
||||
test_preprocess()
|
||||
test_resource_allocation()
|
||||
test_stagnant_deals()
|
||||
test_full_report()
|
||||
|
||||
print("\n" + "=" * 50)
|
||||
print("[SUCCESS] All tests passed!")
|
||||
print("=" * 50)
|
||||
|
||||
except Exception as e:
|
||||
print(f"\n[FAIL] Test failed: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return 1
|
||||
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
exit(main())
|
||||
1
utils/__init__.py
Normal file
1
utils/__init__.py
Normal file
@@ -0,0 +1 @@
|
||||
# Utils module
|
||||
Reference in New Issue
Block a user