From 40d5c83edcc0e5aa5e521da04e12efa855838f67 Mon Sep 17 00:00:00 2001
From: ZhangZixunCodeSpace <muzexun1tr@gmail.com>
Date: Wed, 2 Apr 2025 13:02:28 +0800
Subject: [PATCH] Feat: delete unrequired part in data analysis tool

---
 .../normal_python_execute.py                  | 48 +++----------------
 1 file changed, 6 insertions(+), 42 deletions(-)

diff --git a/app/tool/chart_visualization/normal_python_execute.py b/app/tool/chart_visualization/normal_python_execute.py
index e2f4bb4..c98a519 100644
--- a/app/tool/chart_visualization/normal_python_execute.py
+++ b/app/tool/chart_visualization/normal_python_execute.py
@@ -1,7 +1,3 @@
-import sys
-from io import StringIO
-
-from app.tool.chart_visualization.utils import extract_executable_code
 from app.tool.python_execute import PythonExecute
 
 
@@ -10,51 +6,19 @@ class NormalPythonExecute(PythonExecute):
 
     name: str = "common_python_execute"
     description: str = (
-        """
-            Execute Python code for data analysis tasks without visualization. Important notes:
-
-            1. Output: Only print() statements are visible. Use print() for all outputs.
-            2. Data Processing: Load, clean, and transform data. Save results as CSV files if needed.
-            3. Analysis: Perform statistical analysis, aggregations, and data exploration.
-            4. Code Format: Provide code as a single string, use '\\n' for line breaks.
-            5. File Paths: Use './data/' for relative paths to data files.
-            6. Error Handling: Include try-except blocks for robust error management.
-            7. No Visualization: This tool is for data analysis only, not for creating charts or plots.
-            8. Analysis Results: Generate a comprehensive analysis report and save it in the './data/' directory.
-
-            The analysis report should include:
-            - Dataset overview (rows, columns, data types)
-            - Basic statistics (averages, maximums, minimums for key metrics)
-            - Initial observations and insights
-            - Any patterns or trends identified in the data
-
-    """
+        "Execute Python code for in-depth data analysis without direct visualization. "
+        "The code should generate a comprehensive text-based report containing dataset overview, "
+        "column details, basic statistics, derived metrics, day-of-week comparisons, outliers, and key insights. "
+        "Use print() for all outputs so the analysis (including sections like 'Dataset Overview' or 'Preprocessing Results') "
+        "is clearly visible, and save any final report or processed files to config.workspace. "
+        "Include try-except blocks for error handling, and provide the code as a single string with '\\n' for line breaks."
     )
     parameters: dict = {
         "type": "object",
         "properties": {
             "code": {
                 "type": "string",
-                "default": "html",
-                "enum": ["process", "report", "others"],
             },
         },
         "required": ["code"],
     }
-
-    def _run_code(self, code: str, result_dict: dict, safe_globals: dict) -> None:
-        original_stdout = sys.stdout
-        be_extracted_code = extract_executable_code(code)  # ignore_security_alert RCE
-        try:
-            output_buffer = StringIO()
-            sys.stdout = output_buffer
-            exec(  # ignore_security_alert RCE
-                be_extracted_code, safe_globals, safe_globals
-            )  # ignore_security_alert RCE
-            result_dict["observation"] = output_buffer.getvalue()
-            result_dict["success"] = True
-        except Exception as e:
-            result_dict["observation"] = str(e)
-            result_dict["success"] = False
-        finally:
-            sys.stdout = original_stdout