From 177e8e8fe9dd2dc38f55f171480e69c77f98bd3c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?DonaldFang=20=E6=96=B9=E5=A3=AB=E7=A2=A9?= Date: Fri, 12 Dec 2025 12:53:24 +0800 Subject: [PATCH 1/2] =?UTF-8?q?Phase=200:=20=E5=B0=88=E6=A1=88=E5=88=9D?= =?UTF-8?q?=E5=A7=8B=E5=8C=96=20-=20=E5=BB=BA=E7=AB=8B=E5=B0=88=E6=A1=88?= =?UTF-8?q?=E7=B5=90=E6=A7=8B=E3=80=81=E7=92=B0=E5=A2=83=E8=A8=AD=E5=AE=9A?= =?UTF-8?q?=E8=88=87=20LLM=20=E6=9C=8D=E5=8B=99=E6=A8=A1=E7=B5=84?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .env.example | 19 +++++ .gitignore | 49 +++++++++++++ README.md | 34 +++++++++ app.py | 50 ++++++++++++++ config.py | 37 ++++++++++ models/__init__.py | 3 + requirements.txt | 7 ++ routes/__init__.py | 1 + services/__init__.py | 1 + services/llm_service.py | 150 ++++++++++++++++++++++++++++++++++++++++ utils/__init__.py | 1 + 11 files changed, 352 insertions(+) create mode 100644 .env.example create mode 100644 .gitignore create mode 100644 README.md create mode 100644 app.py create mode 100644 config.py create mode 100644 models/__init__.py create mode 100644 requirements.txt create mode 100644 routes/__init__.py create mode 100644 services/__init__.py create mode 100644 services/llm_service.py create mode 100644 utils/__init__.py diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..8a9374a --- /dev/null +++ b/.env.example @@ -0,0 +1,19 @@ +# Flask +FLASK_SECRET_KEY=your-secret-key-change-in-production +FLASK_DEBUG=True + +# Database (MySQL) +DB_HOST=your-db-host +DB_PORT=3306 +DB_NAME=your-db-name +DB_USER=your-db-user +DB_PASSWORD=your-db-password + +# Ollama API +OLLAMA_API_URL=https://your-ollama-api-url +OLLAMA_DEFAULT_MODEL=qwen2.5:3b + +# Gitea +GITEA_URL=https://your-gitea-url +GITEA_USER=your-username +GITEA_TOKEN=your-token diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..142fde4 --- /dev/null +++ b/.gitignore @@ -0,0 +1,49 @@ +# Environment +.env +venv/ +env/ +.venv/ + +# Python +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg + +# IDE +.idea/ +.vscode/ +*.swp +*.swo +*~ + +# OS +.DS_Store +Thumbs.db + +# Logs +*.log +logs/ + +# Database +*.db +*.sqlite3 + +# Uploads +uploads/ diff --git a/README.md b/README.md new file mode 100644 index 0000000..70f9619 --- /dev/null +++ b/README.md @@ -0,0 +1,34 @@ +# DIT_C + +## 快速開始 + +```bash +# 安裝依賴 +pip install -r requirements.txt + +# 設定環境變數 +cp .env.example .env +# 編輯 .env 填入正確的資料庫與 API 資訊 + +# 啟動服務 +python app.py +``` + +## 測試端點 + +- 健康檢查: `GET /health` +- LLM 連線測試: `GET /test-llm` + +## 專案結構 + +``` +DIT_C/ +├── app.py # 主程式入口 +├── config.py # 設定檔 +├── models/ # 資料庫模型 +├── routes/ # 路由模組 +├── services/ # 商業邏輯 (含 LLM 服務) +├── utils/ # 工具函式 +├── templates/ # HTML 模板 +└── static/ # 靜態資源 +``` diff --git a/app.py b/app.py new file mode 100644 index 0000000..593bc27 --- /dev/null +++ b/app.py @@ -0,0 +1,50 @@ +""" +DIT_C 主程式入口 +""" + +from flask import Flask, jsonify +from config import Config +from models import db + + +def create_app(config_class=Config): + """應用程式工廠""" + app = Flask(__name__) + app.config.from_object(config_class) + + # 初始化擴展 + db.init_app(app) + + # 註冊 Blueprint (後續擴展) + # from routes.auth import auth_bp + # from routes.admin import admin_bp + # from routes.api import api_bp + # app.register_blueprint(auth_bp) + # app.register_blueprint(admin_bp) + # app.register_blueprint(api_bp) + + # 健康檢查端點 + @app.route('/health') + def health_check(): + return jsonify({"status": "ok", "message": "DIT_C is running"}) + + # 測試 LLM 連線 + @app.route('/test-llm') + def test_llm(): + from services.llm_service import llm_service, LLMServiceError + try: + models = llm_service.get_available_models() + return jsonify({ + "status": "ok", + "available_models": models, + "default_model": Config.OLLAMA_DEFAULT_MODEL + }) + except LLMServiceError as e: + return jsonify({"status": "error", "message": str(e)}), 500 + + return app + + +if __name__ == '__main__': + app = create_app() + app.run(host='0.0.0.0', port=5000, debug=Config.DEBUG) diff --git a/config.py b/config.py new file mode 100644 index 0000000..8d5ca15 --- /dev/null +++ b/config.py @@ -0,0 +1,37 @@ +import os +from dotenv import load_dotenv + +load_dotenv() + + +class Config: + """應用程式設定""" + + # Flask + SECRET_KEY = os.getenv('FLASK_SECRET_KEY', 'dev-secret-key') + DEBUG = os.getenv('FLASK_DEBUG', 'False').lower() == 'true' + + # Database (MySQL) + DB_HOST = os.getenv('DB_HOST', 'localhost') + DB_PORT = int(os.getenv('DB_PORT', 3306)) + DB_NAME = os.getenv('DB_NAME', 'database') + DB_USER = os.getenv('DB_USER', 'root') + DB_PASSWORD = os.getenv('DB_PASSWORD', '') + + # SQLAlchemy 連線字串 + SQLALCHEMY_DATABASE_URI = ( + f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}:{DB_PORT}/{DB_NAME}" + ) + SQLALCHEMY_TRACK_MODIFICATIONS = False + + # Table 前綴 + TABLE_PREFIX = 'DIT_C_' + + # Ollama API + OLLAMA_API_URL = os.getenv('OLLAMA_API_URL', 'https://ollama_pjapi.theaken.com') + OLLAMA_DEFAULT_MODEL = os.getenv('OLLAMA_DEFAULT_MODEL', 'qwen2.5:3b') + + # Gitea + GITEA_URL = os.getenv('GITEA_URL', '') + GITEA_USER = os.getenv('GITEA_USER', '') + GITEA_TOKEN = os.getenv('GITEA_TOKEN', '') diff --git a/models/__init__.py b/models/__init__.py new file mode 100644 index 0000000..f0b13d6 --- /dev/null +++ b/models/__init__.py @@ -0,0 +1,3 @@ +from flask_sqlalchemy import SQLAlchemy + +db = SQLAlchemy() diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..633b3ee --- /dev/null +++ b/requirements.txt @@ -0,0 +1,7 @@ +flask>=3.0.0 +flask-sqlalchemy>=3.1.0 +flask-login>=0.6.3 +python-dotenv>=1.0.0 +pymysql>=1.1.0 +cryptography>=41.0.0 +requests>=2.31.0 diff --git a/routes/__init__.py b/routes/__init__.py new file mode 100644 index 0000000..1102393 --- /dev/null +++ b/routes/__init__.py @@ -0,0 +1 @@ +# Routes module diff --git a/services/__init__.py b/services/__init__.py new file mode 100644 index 0000000..0557eb6 --- /dev/null +++ b/services/__init__.py @@ -0,0 +1 @@ +# Services module diff --git a/services/llm_service.py b/services/llm_service.py new file mode 100644 index 0000000..759837a --- /dev/null +++ b/services/llm_service.py @@ -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() diff --git a/utils/__init__.py b/utils/__init__.py new file mode 100644 index 0000000..feddb93 --- /dev/null +++ b/utils/__init__.py @@ -0,0 +1 @@ +# Utils module From 44cd2f8e76707fb0b49f15c4817b52d63f5e62d7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?DonaldFang=20=E6=96=B9=E5=A3=AB=E7=A2=A9?= Date: Fri, 12 Dec 2025 13:12:31 +0800 Subject: [PATCH 2/2] feat: DITAnalyzer module - Feature 6.2 & 6.3 implementation - DITAnalyzer class with data preprocessing - Feature 6.2: High value resource allocation analysis - Feature 6.3: Stagnant deal alerts - Flask API routes for CSV upload and analysis - Test suite with sample data --- app.py | 10 +- requirements.txt | 3 + routes/api.py | 155 ++++++++++++++++++ services/dit_analyzer.py | 323 +++++++++++++++++++++++++++++++++++++ tests/__init__.py | 0 tests/test_dit_analyzer.py | 153 ++++++++++++++++++ 6 files changed, 637 insertions(+), 7 deletions(-) create mode 100644 routes/api.py create mode 100644 services/dit_analyzer.py create mode 100644 tests/__init__.py create mode 100644 tests/test_dit_analyzer.py diff --git a/app.py b/app.py index 593bc27..80fdff3 100644 --- a/app.py +++ b/app.py @@ -15,13 +15,9 @@ def create_app(config_class=Config): # 初始化擴展 db.init_app(app) - # 註冊 Blueprint (後續擴展) - # from routes.auth import auth_bp - # from routes.admin import admin_bp - # from routes.api import api_bp - # app.register_blueprint(auth_bp) - # app.register_blueprint(admin_bp) - # app.register_blueprint(api_bp) + # 註冊 Blueprint + from routes.api import api_bp + app.register_blueprint(api_bp) # 健康檢查端點 @app.route('/health') diff --git a/requirements.txt b/requirements.txt index 633b3ee..a5ad39e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,3 +5,6 @@ python-dotenv>=1.0.0 pymysql>=1.1.0 cryptography>=41.0.0 requests>=2.31.0 +pandas>=2.0.0 +numpy>=1.24.0 +werkzeug>=3.0.0 diff --git a/routes/api.py b/routes/api.py new file mode 100644 index 0000000..ce238dc --- /dev/null +++ b/routes/api.py @@ -0,0 +1,155 @@ +""" +DIT 分析 API 路由 +""" + +import os +import json +from flask import Blueprint, request, jsonify, current_app +from werkzeug.utils import secure_filename +from services.dit_analyzer import DITAnalyzer, DITAnalyzerError + +api_bp = Blueprint('api', __name__, url_prefix='/api') + +ALLOWED_EXTENSIONS = {'csv'} +UPLOAD_FOLDER = 'uploads' + + +def allowed_file(filename: str) -> bool: + """檢查檔案副檔名""" + return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS + + +@api_bp.route('/analyze', methods=['POST']) +def analyze_dit(): + """ + 分析 DIT CSV 檔案 + + 接受 multipart/form-data 上傳 CSV + 回傳 JSON 格式分析結果 + """ + # 檢查檔案 + if 'file' not in request.files: + return jsonify({"error": "未上傳檔案", "code": "NO_FILE"}), 400 + + file = request.files['file'] + if file.filename == '': + return jsonify({"error": "未選擇檔案", "code": "NO_FILENAME"}), 400 + + if not allowed_file(file.filename): + return jsonify({"error": "僅支援 CSV 檔案", "code": "INVALID_TYPE"}), 400 + + # 取得參數 + top_percent = float(request.form.get('top_percent', 0.2)) + low_win_rate = float(request.form.get('low_win_rate', 0.1)) + threshold_days = int(request.form.get('threshold_days', 60)) + + try: + # 確保上傳目錄存在 + os.makedirs(UPLOAD_FOLDER, exist_ok=True) + + # 儲存檔案 + filename = secure_filename(file.filename) + filepath = os.path.join(UPLOAD_FOLDER, filename) + file.save(filepath) + + # 執行分析 + analyzer = DITAnalyzer(filepath) + report = analyzer.generate_report( + top_percent=top_percent, + low_win_rate=low_win_rate, + threshold_days=threshold_days + ) + + # 清理暫存檔 + os.remove(filepath) + + return jsonify({ + "status": "success", + "data": report + }) + + 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 + + +@api_bp.route('/analyze/resource-allocation', methods=['POST']) +def analyze_resource_allocation(): + """ + 僅執行 Feature 6.2: 高價值資源分配分析 + """ + 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 + + top_percent = float(request.form.get('top_percent', 0.2)) + low_win_rate = float(request.form.get('low_win_rate', 0.1)) + + 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) + results = analyzer.analyze_resource_allocation(top_percent, low_win_rate) + + os.remove(filepath) + + return jsonify({ + "status": "success", + "data": { + "type": "resource_allocation", + "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 + + +@api_bp.route('/analyze/stagnant-deals', methods=['POST']) +def analyze_stagnant_deals(): + """ + 僅執行 Feature 6.3: 呆滯案件警示 + """ + 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) + 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 diff --git a/services/dit_analyzer.py b/services/dit_analyzer.py new file mode 100644 index 0000000..d4029d8 --- /dev/null +++ b/services/dit_analyzer.py @@ -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 diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_dit_analyzer.py b/tests/test_dit_analyzer.py new file mode 100644 index 0000000..8bbaf77 --- /dev/null +++ b/tests/test_dit_analyzer.py @@ -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())