Revert "feat: generate structured analysis reports"

This reverts commit 96c23f1f56.

app/tool/chart_visualization/normal_python_execute.py

@@ -1,8 +1,10 @@
 import sys
 from io import StringIO
 
-from app.tool.chart_visualization.utils import extract_executable_code
 from app.tool.python_execute import PythonExecute
+from app.tool.chart_visualization.utils import (
+    extract_executable_code,
+)
 
 
 class NormalPythonExecute(PythonExecute):
@@ -10,86 +12,9 @@ class NormalPythonExecute(PythonExecute):
 
     name: str = "common_python_execute"
     description: str = (
-        """
-A tool for executing Python code with data anaylsis.
-Prefix: 帮我生成结果保存在本地./data下
-
-Data Analysis Agent Protocol (Non-Visual)
-
-=== Core Requirements ===
-1. Strictly text-based outputs only
-2. Prohibited actions:
-   - Any chart/image generation
-   - Interactive visual elements
-   - Graphical libraries import
-
-=== Execution Phases ===
-
-1. DATA LOADING (Auto-detect format)
-- Supported formats: CSV/Excel/JSON
-- Mandatory checks:
-  a) File existence verification
-  b) Column structure validation
-  c) Basic integrity checks
-
-2. ANALYSIS PIPELINE
-- Cleaning:
-  • Null handling (drop or impute)
-  • Deduplication
-  • Outlier treatment (IQR/Z-score)
-
-- Transformation:
-  • Date parsing
-  • Derived metrics
-  • Aggregations
-
-3. REPORT GENERATION
-Output 1: data_exploration.md
-┌──────────────────────┬──────────────────────────────┐
-│ Section              │ Content Requirements         │
-├──────────────────────┼──────────────────────────────┤
-│ Dataset Metadata     │ Rows/Columns/Temporal Range │
-│ Column Descriptions  │ Type/Stats/Unique Values    │
-│ Key Findings         │ 3-5 bullet points           │
-└──────────────────────┴──────────────────────────────┘
-
-Output 2: preprocessing_results.md
-┌──────────────────────┬──────────────────────────────┐
-│ Section              │ Content Requirements         │
-├──────────────────────┼──────────────────────────────┤
-│ Cleaning Log         │ Rows affected by each operation │
-│ Derived Metrics      │ Formula/Summary Stats       │
-│ Anomaly Report       │ Z-score >2.5 cases          │
-└──────────────────────┴──────────────────────────────┘
-
-=== Implementation Rules ===
-1. Code Generation Constraints:
-   - Forbidden libraries: matplotlib, seaborn, plotly
-   - Maximum column width: 120 chars
-   - Required docstrings for all functions
-
-2. Error Handling:
-   - Skip corrupted records with logging
-   - Continue processing after non-critical errors
-   - Fail fast on structural issues
-
-3. Output Validation:
-   - Markdown syntax check
-   - Statistical validity verification
-   - Cross-report consistency
-
-=== Sample Invocation ===
-def analyze(data_path):
-    '''Main analysis workflow'''
-    df = load_data(data_path)          # Phase 1
-    cleaned = clean_and_transform(df)  # Phase 2
-    generate_reports(cleaned)          # Phase 3
-=== 执行约束 ===
-当检测到错误时：
-1. 分析错误类型（数据/逻辑/环境）
-2. 生成修正方案（自动重试≤3次）
-3. 严重错误时回滚中间文件
-"""
+        """Executes Python code strings. Note:
+1. Only outputs from print() are visible; function return values are not captured. Use print() statements to display results
+2. Applicable to scenarios **excluding data analysis and chart generation**"""
     )
     parameters: dict = {
         "type": "object",

app/tool/chart_visualization/test/amazon_fashion_analysis.py

@@ -1,17 +0,0 @@
-import asyncio
-
-from app.agent.data_analysis import DataAnalysis
-
-# from app.agent.manus import Manus
-
-
-async def main():
-    agent = DataAnalysis()
-    # agent = Manus()
-    await agent.run(
-        """Here's last month's sales data from my Amazon store in './data/amazon_sales_jan2025.xlsx'. Could you analyze it?  """
-    )
-
-
-if __name__ == "__main__":
-    asyncio.run(main())