feat(ssa): Complete Phase 2A frontend integration - multi-step workflow end-to-end

Phase 2A: WorkflowPlannerService, WorkflowExecutorService, Python data quality, 6 bug fixes, DescriptiveResultView, multi-step R code/Word export, MVP UI reuse. V11 UI: Gemini-style, multi-task, single-page scroll, Word export. Architecture: Block-based rendering consensus (4 block types). New R tools: chi_square, correlation, descriptive, logistic_binary, mann_whitney, t_test_paired. Docs: dev summary, block-based plan, status updates, task list v2.0.

Co-authored-by: Cursor <cursoragent@cursor.com>

extraction_service/main.py

@@ -94,6 +94,8 @@ from operations.metric_time_transform import (
     preview_multi_metric_to_matrix  # ✨ 多指标转换预览（方向2）
 )
 from operations.fillna import fillna_simple, fillna_mice, get_column_missing_stats
+# ✨ SSA Phase 2A: 数据画像
+from operations.data_profile import generate_data_profile, get_quality_score
 
 
 # ==================== Pydantic Models ====================
@@ -231,6 +233,21 @@ class FillnaStatsRequest(BaseModel):
     column: str
 
 
+# ✨ SSA Phase 2A: DataProfile 请求模型
+class DataProfileRequest(BaseModel):
+    """数据画像请求模型 (SSA Phase 2A)"""
+    data: List[Dict[str, Any]]
+    max_unique_values: int = 20  # 分类变量显示的最大唯一值数量
+    include_quality_score: bool = True  # 是否包含质量评分
+
+
+class DataProfileCSVRequest(BaseModel):
+    """数据画像请求模型 - CSV 直传 (SSA Phase 2A)"""
+    csv_content: str  # CSV 文件内容（字符串）
+    max_unique_values: int = 20
+    include_quality_score: bool = True
+
+
 class FillnaSimpleRequest(BaseModel):
     """简单填补请求模型"""
     data: List[Dict[str, Any]]
@@ -2125,6 +2142,129 @@ async def operation_fillna_mice(request: FillnaMiceRequest):
         }, status_code=400)
 
 
+# ==================== SSA Phase 2A: DataProfile API ====================
+
+@app.post("/api/ssa/data-profile")
+async def ssa_data_profile(request: DataProfileRequest):
+    """
+    生成数据画像 (SSA Phase 2A)
+    
+    用于 SSA 模块在用户上传数据时快速生成数据画像，
+    画像将喂给 LLM 以生成分析计划 (SAP)。
+    
+    Args:
+        request: DataProfileRequest
+            - data: 数据 (JSON 格式)
+            - max_unique_values: 分类变量显示的最大唯一值数量
+            - include_quality_score: 是否包含质量评分
+    
+    Returns:
+        {
+            "success": bool,
+            "profile": {
+                "columns": [...],
+                "summary": {...}
+            },
+            "quality": {...} (可选),
+            "execution_time": float
+        }
+    """
+    try:
+        import pandas as pd
+        import time
+        
+        start_time = time.time()
+        
+        df = pd.DataFrame(request.data)
+        
+        logger.info(f"[SSA] 开始生成数据画像: {df.shape}")
+        
+        profile = generate_data_profile(df, request.max_unique_values)
+        
+        result = {
+            "success": True,
+            "profile": profile
+        }
+        
+        if request.include_quality_score:
+            result["quality"] = get_quality_score(profile)
+        
+        execution_time = time.time() - start_time
+        result["execution_time"] = round(execution_time, 3)
+        
+        logger.info(f"[SSA] 数据画像生成完成: {execution_time:.3f}s")
+        
+        return JSONResponse(content=result)
+        
+    except Exception as e:
+        logger.error(f"[SSA] 数据画像生成失败: {str(e)}")
+        return JSONResponse(content={
+            "success": False,
+            "error": str(e),
+            "execution_time": time.time() - start_time if 'start_time' in locals() else 0
+        }, status_code=400)
+
+
+@app.post("/api/ssa/data-profile-csv")
+async def ssa_data_profile_csv(request: DataProfileCSVRequest):
+    """
+    生成数据画像 - CSV 直传 (SSA Phase 2A)
+    
+    直接接收 CSV 字符串，由 Python pandas 解析，
+    比 Node.js 解析后再转 JSON 更高效、更可靠。
+    
+    Args:
+        request: DataProfileCSVRequest
+            - csv_content: CSV 文件内容（字符串）
+            - max_unique_values: 分类变量显示的最大唯一值数量
+            - include_quality_score: 是否包含质量评分
+    
+    Returns:
+        {
+            "success": bool,
+            "profile": {...},
+            "quality": {...} (可选),
+            "execution_time": float
+        }
+    """
+    try:
+        import pandas as pd
+        import time
+        from io import StringIO
+        
+        start_time = time.time()
+        
+        # pandas 直接解析 CSV 字符串，自动推断类型
+        df = pd.read_csv(StringIO(request.csv_content))
+        
+        logger.info(f"[SSA] CSV 解析完成，开始生成数据画像: {df.shape}")
+        
+        profile = generate_data_profile(df, request.max_unique_values)
+        
+        result = {
+            "success": True,
+            "profile": profile
+        }
+        
+        if request.include_quality_score:
+            result["quality"] = get_quality_score(profile)
+        
+        execution_time = time.time() - start_time
+        result["execution_time"] = round(execution_time, 3)
+        
+        logger.info(f"[SSA] 数据画像生成完成 (CSV): {execution_time:.3f}s")
+        
+        return JSONResponse(content=result)
+        
+    except Exception as e:
+        logger.error(f"[SSA] CSV 数据画像生成失败: {str(e)}")
+        return JSONResponse(content={
+            "success": False,
+            "error": str(e),
+            "execution_time": time.time() - start_time if 'start_time' in locals() else 0
+        }, status_code=400)
+
+
 # ==================== Word 导出 API ====================
 
 @app.get("/api/pandoc/status")