from process.code_interpreter import CodeInterpreter interpreter_instance = CodeInterpreter() from langchain_core.tools import tool @tool def execute_code_multilang(code: str, language: str = "python") -> str: """Execute code in multiple languages (Python, Bash, SQL, C, Java) and return results. Args: code (str): The source code to execute. language (str): The language of the code. Supported: "python", "bash", "sql", "c", "java". Returns: A string summarizing the execution results (stdout, stderr, errors, plots, dataframes if any). """ supported_languages = ["python", "bash", "sql", "c", "java"] language = language.lower() if language not in supported_languages: return f"❌ Unsupported language: {language}. Supported languages are: {', '.join(supported_languages)}" result = interpreter_instance.execute_code(code, language=language) response = [] if result["status"] == "success": response.append(f"✅ Code executed successfully in **{language.upper()}**") if result.get("stdout"): response.append( "\n**Standard Output:**\n```\n" + result["stdout"].strip() + "\n```" ) if result.get("stderr"): response.append( "\n**Standard Error (if any):**\n```\n" + result["stderr"].strip() + "\n```" ) if result.get("result") is not None: response.append( "\n**Execution Result:**\n```\n" + str(result["result"]).strip() + "\n```" ) if result.get("dataframes"): for df_info in result["dataframes"]: response.append( f"\n**DataFrame `{df_info['name']}` (Shape: {df_info['shape']})**" ) df_preview = pd.DataFrame(df_info["head"]) response.append("First 5 rows:\n```\n" + str(df_preview) + "\n```") if result.get("plots"): response.append( f"\n**Generated {len(result['plots'])} plot(s)** (Image data returned separately)" ) else: response.append(f"❌ Code execution failed in **{language.upper()}**") if result.get("stderr"): response.append( "\n**Error Log:**\n```\n" + result["stderr"].strip() + "\n```" ) return "\n".join(response)