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

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