""" Code Intelligence Module for AumCore AI Version: 1.0.0 Author: AumCore AI Location: /app/modules/code_intelligence.py """ import ast import re import json import subprocess import tempfile import os from typing import Dict, List, Optional, Tuple, Any from dataclasses import dataclass, field from enum import Enum import autopep8 import black from datetime import datetime class CodeLanguage(Enum): """Supported programming languages""" PYTHON = "python" JAVASCRIPT = "javascript" HTML = "html" CSS = "css" SQL = "sql" JAVA = "java" CPP = "cpp" GO = "go" RUST = "rust" class CodeIssueSeverity(Enum): """Code issue severity levels""" CRITICAL = "critical" # Security/bug that will break HIGH = "high" # Major issue needs fixing MEDIUM = "medium" # Should be fixed LOW = "low" # Nice to have improvements INFO = "info" # Informational only @dataclass class CodeIssue: """Code issue/improvement suggestion""" line: int column: int severity: CodeIssueSeverity message: str suggestion: Optional[str] = None code_snippet: Optional[str] = None @dataclass class CodeAnalysisResult: """Result of code analysis""" language: CodeLanguage issues: List[CodeIssue] suggestions: List[str] complexity_score: float # 0-100, lower is better security_score: float # 0-100, higher is better readability_score: float # 0-100, higher is better estimated_bugs: int class AumCoreCodeIntelligence: """ Advanced Code Intelligence System Analyzes, optimizes, and generates code with AI assistance """ def __init__(self): self._code_patterns = self._load_code_patterns() self._templates = self._load_code_templates() def _load_code_patterns(self) -> Dict: """Load code patterns for analysis""" return { "security": { "python": [ (r"exec\(", "Avoid exec() - security risk"), (r"eval\(", "Avoid eval() - security risk"), (r"subprocess\.call.*shell=True", "Avoid shell=True - security risk"), (r"pickle\.loads", "Avoid pickle.loads() with untrusted data"), (r"input\(\)", "Validate user input() to prevent injection"), (r"os\.system", "Use subprocess.run() instead of os.system()"), ], "javascript": [ (r"eval\(", "Avoid eval() - security risk"), (r"Function\(", "Avoid Function constructor - security risk"), (r"innerHTML.*=", "Use textContent instead of innerHTML to prevent XSS"), ], "sql": [ (r"'.*\+.*SELECT", "Use parameterized queries to prevent SQL injection"), ] }, "performance": { "python": [ (r"for.*in.*range\(len\(", "Use enumerate() instead of range(len())"), (r"\.append\(\) in loop", "Consider list comprehension for better performance"), (r"global ", "Avoid global variables for better performance"), ] }, "best_practices": { "python": [ (r"except:", "Specify exception type instead of bare except"), (r"print\(", "Use logging module instead of print() in production"), (r"magic_number", "Use named constants instead of magic numbers"), ] } } def _load_code_templates(self) -> Dict: """Load code templates for generation""" return { "python": { "web_api": """from fastapi import FastAPI, HTTPException from pydantic import BaseModel from typing import Optional app = FastAPI() class Item(BaseModel): name: str description: Optional[str] = None price: float tax: Optional[float] = None @app.get("/") def read_root(): return {{"message": "Hello World"}} @app.get("/items/{item_id}") def read_item(item_id: int, q: Optional[str] = None): return {{"item_id": item_id, "q": q}} @app.post("/items/") def create_item(item: Item): return item if __name__ == "__main__": import uvicorn uvicorn.run(app, host="0.0.0.0", port=8000)""", "data_processing": """import pandas as pd import numpy as np from typing import List, Dict def process_data(file_path: str) -> pd.DataFrame: \"\"\" Process data from CSV file Args: file_path: Path to CSV file Returns: Processed DataFrame \"\"\" try: df = pd.read_csv(file_path) # Basic data cleaning df = df.dropna() df = df.drop_duplicates() # Add derived columns if needed if 'price' in df.columns and 'quantity' in df.columns: df['total'] = df['price'] * df['quantity'] return df except Exception as e: raise ValueError(f"Error processing file {{file_path}}: {{e}}") def analyze_data(df: pd.DataFrame) -> Dict: \"\"\" Analyze DataFrame and return statistics Args: df: Input DataFrame Returns: Dictionary of statistics \"\"\" stats = {{ "rows": len(df), "columns": list(df.columns), "numeric_stats": {{}}, "missing_values": df.isnull().sum().to_dict() }} # Calculate numeric column statistics numeric_cols = df.select_dtypes(include=[np.number]).columns for col in numeric_cols: stats["numeric_stats"][col] = {{ "mean": df[col].mean(), "median": df[col].median(), "std": df[col].std(), "min": df[col].min(), "max": df[col].max() }} return stats""", "machine_learning": """from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score, classification_report import pandas as pd import numpy as np import pickle class MLModel: def __init__(self): self.model = RandomForestClassifier(n_estimators=100, random_state=42) self.scaler = StandardScaler() self.feature_names = None def prepare_data(self, df: pd.DataFrame, target_column: str): \"\"\"Prepare data for training\"\"\" X = df.drop(columns=[target_column]) y = df[target_column] self.feature_names = X.columns.tolist() # Split data X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42 ) # Scale features X_train_scaled = self.scaler.fit_transform(X_train) X_test_scaled = self.scaler.transform(X_test) return X_train_scaled, X_test_scaled, y_train, y_test def train(self, X_train, y_train): \"\"\"Train the model\"\"\" self.model.fit(X_train, y_train) def evaluate(self, X_test, y_test): \"\"\"Evaluate model performance\"\"\" y_pred = self.model.predict(X_test) accuracy = accuracy_score(y_test, y_pred) report = classification_report(y_test, y_pred, output_dict=True) return {{ "accuracy": accuracy, "report": report, "feature_importance": dict(zip(self.feature_names, self.model.feature_importances_)) }} def predict(self, X): \"\"\"Make predictions\"\"\" X_scaled = self.scaler.transform(X) return self.model.predict(X_scaled) def save(self, path: str): \"\"\"Save model to file\"\"\" with open(path, 'wb') as f: pickle.dump({{ 'model': self.model, 'scaler': self.scaler, 'features': self.feature_names }}, f) def load(self, path: str): \"\"\"Load model from file\"\"\" with open(path, 'rb') as f: data = pickle.load(f) self.model = data['model'] self.scaler = data['scaler'] self.feature_names = data['features']""" }, "javascript": { "react_component": """import React, { useState, useEffect } from 'react'; import axios from 'axios'; const MyComponent = () => { const [data, setData] = useState([]); const [loading, setLoading] = useState(true); const [error, setError] = useState(null); useEffect(() => { const fetchData = async () => { try { const response = await axios.get('https://api.example.com/data'); setData(response.data); setLoading(false); } catch (err) { setError(err.message); setLoading(false); } }; fetchData(); }, []); if (loading) return
Loading...
; if (error) return
Error: {error}
; return (

Data List

); }; export default MyComponent;""", "node_api": """const express = require('express'); const app = express(); const port = 3000; // Middleware app.use(express.json()); // Sample data let items = [ { id: 1, name: 'Item 1', description: 'First item' }, { id: 2, name: 'Item 2', description: 'Second item' } ]; // Routes app.get('/', (req, res) => { res.json({ message: 'Welcome to the API' }); }); app.get('/items', (req, res) => { res.json(items); }); app.get('/items/:id', (req, res) => { const item = items.find(i => i.id === parseInt(req.params.id)); if (!item) return res.status(404).json({ error: 'Item not found' }); res.json(item); }); app.post('/items', (req, res) => { const newItem = { id: items.length + 1, name: req.body.name, description: req.body.description || '' }; items.push(newItem); res.status(201).json(newItem); }); app.put('/items/:id', (req, res) => { const item = items.find(i => i.id === parseInt(req.params.id)); if (!item) return res.status(404).json({ error: 'Item not found' }); item.name = req.body.name || item.name; item.description = req.body.description || item.description; res.json(item); }); app.delete('/items/:id', (req, res) => { items = items.filter(i => i.id !== parseInt(req.params.id)); res.status(204).send(); }); // Start server app.listen(port, () => { console.log(`Server running at http://localhost:${port}`); });""" }, "sql": { "database_schema": """-- Users table CREATE TABLE users ( id SERIAL PRIMARY KEY, username VARCHAR(50) UNIQUE NOT NULL, email VARCHAR(100) UNIQUE NOT NULL, password_hash VARCHAR(255) NOT NULL, created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, is_active BOOLEAN DEFAULT TRUE ); -- Posts table CREATE TABLE posts ( id SERIAL PRIMARY KEY, user_id INTEGER REFERENCES users(id) ON DELETE CASCADE, title VARCHAR(200) NOT NULL, content TEXT NOT NULL, created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, published BOOLEAN DEFAULT FALSE ); -- Comments table CREATE TABLE comments ( id SERIAL PRIMARY KEY, post_id INTEGER REFERENCES posts(id) ON DELETE CASCADE, user_id INTEGER REFERENCES users(id) ON DELETE CASCADE, content TEXT NOT NULL, created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ); -- Indexes for performance CREATE INDEX idx_posts_user_id ON posts(user_id); CREATE INDEX idx_comments_post_id ON comments(post_id); CREATE INDEX idx_comments_user_id ON comments(user_id); CREATE INDEX idx_users_email ON users(email); -- Trigger to update updated_at timestamp CREATE OR REPLACE FUNCTION update_updated_at_column() RETURNS TRIGGER AS $$ BEGIN NEW.updated_at = CURRENT_TIMESTAMP; RETURN NEW; END; $$ language 'plpgsql'; CREATE TRIGGER update_users_updated_at BEFORE UPDATE ON users FOR EACH ROW EXECUTE FUNCTION update_updated_at_column(); CREATE TRIGGER update_posts_updated_at BEFORE UPDATE ON posts FOR EACH ROW EXECUTE FUNCTION update_updated_at_column();""", "common_queries": """-- Get all active users with their post count SELECT u.id, u.username, u.email, COUNT(p.id) as post_count, MAX(p.created_at) as latest_post FROM users u LEFT JOIN posts p ON u.id = p.user_id AND p.published = TRUE WHERE u.is_active = TRUE GROUP BY u.id, u.username, u.email ORDER BY post_count DESC; -- Get posts with comments count SELECT p.id, p.title, p.content, u.username as author, COUNT(c.id) as comment_count, p.created_at FROM posts p JOIN users u ON p.user_id = u.id LEFT JOIN comments c ON p.id = c.post_id WHERE p.published = TRUE GROUP BY p.id, p.title, p.content, u.username, p.created_at ORDER BY p.created_at DESC; -- Search posts by keyword SELECT p.id, p.title, p.content, u.username, p.created_at FROM posts p JOIN users u ON p.user_id = u.id WHERE p.published = TRUE AND (p.title ILIKE '%search_term%' OR p.content ILIKE '%search_term%') ORDER BY CASE WHEN p.title ILIKE '%search_term%' THEN 1 ELSE 2 END, p.created_at DESC;""" } } def analyze_code(self, code: str, language: CodeLanguage = CodeLanguage.PYTHON) -> CodeAnalysisResult: """ Analyze code for issues and improvements Args: code: Source code to analyze language: Programming language Returns: CodeAnalysisResult with analysis details """ issues = [] suggestions = [] # Language-specific analysis if language == CodeLanguage.PYTHON: issues.extend(self._analyze_python_code(code)) elif language == CodeLanguage.JAVASCRIPT: issues.extend(self._analyze_javascript_code(code)) # General pattern matching issues.extend(self._pattern_match_code(code, language.value)) # Complexity analysis complexity_score = self._calculate_complexity(code, language) # Security analysis security_score = self._calculate_security_score(code, language, issues) # Readability analysis readability_score = self._calculate_readability_score(code, language) # Estimate bugs estimated_bugs = self._estimate_bugs(issues) # Generate suggestions suggestions = self._generate_suggestions(issues, code, language) return CodeAnalysisResult( language=language, issues=issues, suggestions=suggestions, complexity_score=complexity_score, security_score=security_score, readability_score=readability_score, estimated_bugs=estimated_bugs ) def _analyze_python_code(self, code: str) -> List[CodeIssue]: """Analyze Python code specifically""" issues = [] try: # Parse AST for deeper analysis tree = ast.parse(code) # AST-based checks for node in ast.walk(tree): # Check for bare except if isinstance(node, ast.ExceptHandler) and node.type is None: issues.append(CodeIssue( line=node.lineno, column=node.col_offset, severity=CodeIssueSeverity.MEDIUM, message="Bare except clause - specify exception type", suggestion="Use 'except ExceptionType:' instead of 'except:'", code_snippet=self._get_line(code, node.lineno) )) # Check for too many nested blocks if isinstance(node, (ast.FunctionDef, ast.ClassDef)): complexity = self._calculate_function_complexity(node) if complexity > 10: issues.append(CodeIssue( line=node.lineno, column=node.col_offset, severity=CodeIssueSeverity.MEDIUM, message=f"High function complexity ({complexity})", suggestion="Consider breaking function into smaller functions", code_snippet=node.name )) except SyntaxError as e: issues.append(CodeIssue( line=e.lineno or 1, column=e.offset or 1, severity=CodeIssueSeverity.CRITICAL, message=f"Syntax error: {e.msg}", suggestion="Fix syntax error before further analysis", code_snippet=self._get_line(code, e.lineno or 1) )) return issues def _analyze_javascript_code(self, code: str) -> List[CodeIssue]: """Analyze JavaScript code""" issues = [] # Simple regex-based checks for JS patterns = [ (r"console\.log\(", "Remove console.log() in production code", CodeIssueSeverity.LOW), (r"alert\(", "Avoid alert() - use better user feedback", CodeIssueSeverity.MEDIUM), (r"document\.write", "Avoid document.write() - bad practice", CodeIssueSeverity.HIGH), ] lines = code.split('\n') for i, line in enumerate(lines, 1): for pattern, message, severity in patterns: if re.search(pattern, line): issues.append(CodeIssue( line=i, column=0, severity=severity, message=message, suggestion="Remove or replace with proper implementation", code_snippet=line.strip() )) return issues def _pattern_match_code(self, code: str, language: str) -> List[CodeIssue]: """Pattern matching for code issues""" issues = [] lines = code.split('\n') # Check security patterns if language in self._code_patterns["security"]: for pattern, message in self._code_patterns["security"][language]: for i, line in enumerate(lines, 1): if re.search(pattern, line, re.IGNORECASE): issues.append(CodeIssue( line=i, column=0, severity=CodeIssueSeverity.HIGH, message=f"Security concern: {message}", suggestion="Use safer alternative", code_snippet=line.strip() )) # Check performance patterns if language in self._code_patterns["performance"]: for pattern, message in self._code_patterns["performance"][language]: for i, line in enumerate(lines, 1): if re.search(pattern, line, re.IGNORECASE): issues.append(CodeIssue( line=i, column=0, severity=CodeIssueSeverity.MEDIUM, message=f"Performance: {message}", suggestion="Optimize for better performance", code_snippet=line.strip() )) return issues def _calculate_complexity(self, code: str, language: CodeLanguage) -> float: """Calculate code complexity score (0-100, lower is better)""" if language == CodeLanguage.PYTHON: # Simple complexity estimation for Python lines = code.split('\n') if not lines: return 0.0 complexity_indicators = 0 for line in lines: line_lower = line.lower().strip() if any(keyword in line_lower for keyword in ['for ', 'while ', 'if ', 'def ', 'class ', 'try:', 'except:']): complexity_indicators += 1 complexity = (complexity_indicators / len(lines)) * 100 return min(100.0, complexity) return 50.0 # Default def _calculate_security_score(self, code: str, language: CodeLanguage, issues: List[CodeIssue]) -> float: """Calculate security score (0-100, higher is better)""" base_score = 80.0 # Deduct for security issues security_issues = [i for i in issues if i.severity in [CodeIssueSeverity.CRITICAL, CodeIssueSeverity.HIGH]] deduction = len(security_issues) * 10 score = max(0.0, base_score - deduction) return score def _calculate_readability_score(self, code: str, language: CodeLanguage) -> float: """Calculate readability score (0-100, higher is better)""" lines = code.split('\n') if not lines: return 100.0 # Simple readability heuristics good_practices = 0 total_lines = len(lines) for line in lines: line_stripped = line.strip() # Check for good practices if line_stripped and not line_stripped.startswith('#'): # Reasonable line length if len(line) <= 100: good_practices += 1 # Avoid too many spaces if not line.startswith(' ' * 4): # More than 3 indentation levels good_practices += 1 readability = (good_practices / (total_lines * 2)) * 100 return min(100.0, readability) def _estimate_bugs(self, issues: List[CodeIssue]) -> int: """Estimate number of potential bugs""" bug_count = 0 for issue in issues: if issue.severity in [CodeIssueSeverity.CRITICAL, CodeIssueSeverity.HIGH]: bug_count += 2 elif issue.severity == CodeIssueSeverity.MEDIUM: bug_count += 1 return bug_count def _generate_suggestions(self, issues: List[CodeIssue], code: str, language: CodeLanguage) -> List[str]: """Generate improvement suggestions""" suggestions = [] if not issues: suggestions.append("Code looks good! No major issues found.") return suggestions # Group suggestions by category security_issues = [i for i in issues if i.severity in [CodeIssueSeverity.CRITICAL, CodeIssueSeverity.HIGH]] performance_issues = [i for i in issues if "performance" in i.message.lower()] style_issues = [i for i in issues if i.severity == CodeIssueSeverity.LOW] if security_issues: suggestions.append(f"Fix {len(security_issues)} security issues for better safety") if performance_issues: suggestions.append(f"Address {len(performance_issues)} performance concerns") if style_issues: suggestions.append(f"Consider {len(style_issues)} style improvements") # Language-specific suggestions if language == CodeLanguage.PYTHON: suggestions.append("Use type hints for better code clarity") suggestions.append("Add docstrings to functions and classes") elif language == CodeLanguage.JAVASCRIPT: suggestions.append("Use const/let instead of var") suggestions.append("Add error handling for async operations") return suggestions def _calculate_function_complexity(self, node: ast.AST) -> int: """Calculate complexity of a function/class from AST""" complexity = 0 for child in ast.walk(node): if isinstance(child, (ast.If, ast.While, ast.For, ast.Try, ast.ExceptHandler)): complexity += 1 elif isinstance(child, ast.BoolOp): complexity += len(child.values) - 1 return complexity def _get_line(self, code: str, line_number: int) -> str: """Get specific line from code""" lines = code.split('\n') if 1 <= line_number <= len(lines): return lines[line_number - 1] return "" def generate_code(self, template_type: str, language: CodeLanguage = CodeLanguage.PYTHON, variables: Dict[str, Any] = None) -> str: """ Generate code from template Args: template_type: Type of template to use language: Programming language variables: Variables to substitute in template Returns: Generated code """ variables = variables or {} try: if language.value in self._templates and template_type in self._templates[language.value]: template = self._templates[language.value][template_type] # Simple variable substitution for key, value in variables.items(): placeholder = "{{" + key + "}}" template = template.replace(placeholder, str(value)) return template else: return f"# Template '{template_type}' not found for {language.value}" except Exception as e: return f"# Error generating code: {str(e)}" def optimize_code(self, code: str, language: CodeLanguage = CodeLanguage.PYTHON) -> str: """ Optimize code for better performance/readability Args: code: Source code to optimize language: Programming language Returns: Optimized code """ if language == CodeLanguage.PYTHON: try: # Format with autopep8 optimized = autopep8.fix_code(code) return optimized except: # Fallback to simple formatting return code return code def explain_code(self, code: str, language: CodeLanguage = CodeLanguage.PYTHON, language_output: str = "en") -> str: """ Generate explanation of code in simple language Args: code: Code to explain language: Programming language of code language_output: Output language (en/hi) Returns: Code explanation """ explanations = { "python": { "en": { "import": "Imports modules/libraries for use in code", "def": "Defines a function with given name and parameters", "class": "Defines a class/blueprint for creating objects", "if": "Conditional statement - executes code if condition is true", "for": "Loop that iterates over items in a sequence", "while": "Loop that continues while condition is true", "return": "Returns value from function", "try": "Begins exception handling block", "except": "Catches and handles exceptions", }, "hi": { "import": "कोड में उपयोग के लिए मॉड्यूल/लाइब्रेरी आयात करता है", "def": "दिए गए नाम और पैरामीटर्स के साथ एक फ़ंक्शन को परिभाषित करता है", "class": "ऑब्जेक्ट बनाने के लिए एक क्लास/ब्लूप्रिंट को परिभाषित करता है", "if": "सशर्त स्टेटमेंट - अगर कंडीशन सही है तो कोड एक्जीक्यूट करता है", "for": "लूप जो एक सीक्वेंस में आइटम्स पर इटरेट करता है", "while": "लूप जो कंडीशन सही रहने तक जारी रहता है", "return": "फ़ंक्शन से वैल्यू रिटर्न करता है", "try": "एक्सेप्शन हैंडलिंग ब्लॉक शुरू करता है", "except": "एक्सेप्शन को कैच और हैंडल करता है", } } } # Simple explanation based on keywords lines = code.split('\n') explanation_lines = [] lang_key = language.value output_lang = language_output if language_output in ["en", "hi"] else "en" explanation_dict = explanations.get(lang_key, {}).get(output_lang, {}) for i, line in enumerate(lines, 1): line_stripped = line.strip() if line_stripped: # Find keywords in line for keyword, meaning in explanation_dict.items(): if keyword in line_stripped.split(): explanation_lines.append(f"Line {i}: {meaning}") break if not explanation_lines: if output_lang == "en": return "Code explanation not available for this snippet." else: return "इस कोड स्निपेट के लिए स्पष्टीकरण उपलब्ध नहीं है।" if output_lang == "en": header = "Code Explanation:\n" else: header = "कोड स्पष्टीकरण:\n" return header + "\n".join(explanation_lines) def debug_code(self, code: str, error_message: str, language: CodeLanguage = CodeLanguage.PYTHON) -> str: """ Suggest fixes for code errors Args: code: Code with error error_message: Error message from interpreter language: Programming language Returns: Debugging suggestions """ suggestions = [] # Common error patterns error_patterns = { "python": [ (r"SyntaxError", "Check for missing colons, parentheses, or quotes"), (r"IndentationError", "Check indentation consistency (use 4 spaces)"), (r"NameError.*not defined", "Variable/function not defined - check spelling"), (r"TypeError", "Check data types and operations compatibility"), (r"IndexError", "List/array index out of range"), (r"KeyError", "Dictionary key not found"), (r"AttributeError", "Object doesn't have the attribute/method"), (r"ImportError", "Module not installed or incorrect import path"), (r"ValueError", "Function received argument of right type but inappropriate value"), ], "javascript": [ (r"ReferenceError", "Variable not defined - check scope and spelling"), (r"TypeError", "Value is not of expected type"), (r"SyntaxError", "Check syntax - missing brackets, semicolons, etc."), (r"RangeError", "Numeric value out of range"), ] } lang_key = language.value if lang_key in error_patterns: for pattern, suggestion in error_patterns[lang_key]: if re.search(pattern, error_message, re.IGNORECASE): suggestions.append(suggestion) if not suggestions: suggestions.append("Try checking syntax and variable names") suggestions.append("Ensure all required modules/libraries are imported") suggestions.append("Check data types and operations compatibility") return "Debug suggestions:\n- " + "\n- ".join(suggestions) # Global instance code_intel = AumCoreCodeIntelligence() # Helper functions for easy import def analyze_code(code: str, language: str = "python") -> Dict: """Analyze code and return results as dictionary""" lang_enum = CodeLanguage(language.lower()) result = code_intel.analyze_code(code, lang_enum) return { "language": result.language.value, "issues": [ { "line": issue.line, "column": issue.column, "severity": issue.severity.value, "message": issue.message, "suggestion": issue.suggestion, "code_snippet": issue.code_snippet } for issue in result.issues ], "suggestions": result.suggestions, "complexity_score": result.complexity_score, "security_score": result.security_score, "readability_score": result.readability_score, "estimated_bugs": result.estimated_bugs } def generate_code_template(template_type: str, language: str = "python", variables: Dict = None) -> str: """Generate code from template""" lang_enum = CodeLanguage(language.lower()) return code_intel.generate_code(template_type, lang_enum, variables) def explain_code_simple(code: str, language: str = "python", output_language: str = "en") -> str: """Explain code in simple terms""" lang_enum = CodeLanguage(language.lower()) return code_intel.explain_code(code, lang_enum, output_language) # Module exports __all__ = [ 'AumCoreCodeIntelligence', 'CodeLanguage', 'CodeIssueSeverity', 'code_intel', 'analyze_code', 'generate_code_template', 'explain_code_simple' ] # ============================================ # MODULE REGISTRATION FOR APPPY # ============================================ def register_module(app, client, username): """ Required function for ModuleManager to load this module """ print("✅ Code Intelligence module registered with FastAPI") return { "module": "code_intelligence", "status": "registered", "version": __version__, "description": "Advanced code analysis and intelligence system" } __version__ = "1.0.0" __author__ = "AumCore AI"