model.py

import re
import ast
from typing import List, Dict

class AutofixCode:
    def __init__(self):
        self.code_snippets: List[Dict] = []  # Store code snippets and their corresponding fixes

    def analyze_code(self, code: str) -> List[Dict]:
        """
        Analyze the given code and identify potential issues.
        :param code: The source code to analyze
        :return: A list of dictionaries containing issue details (e.g., line number, error type)
        """
        # Tokenize the code using regular expressions or an AST parser
        tokens = re.split(r'(\W)', code)

        # Identify potential issues (e.g., syntax errors, logical flaws) and store them in a dictionary
        issues: List[Dict] = []
        for token in tokens:
            if token == 'SyntaxError':
                issue_type = 'Syntax Error'
            elif token == 'LogicalFlaw':
                issue_type = 'Logical Flaw'
            # Add more issue types as needed
            else:
                continue

            issue_data = {'line_number': int(token), 'error_type': issue_type}
            issues.append(issue_data)

        return issues

    def generate_fix_proposal(self, issue: Dict) -> str:
        """
        Generate a proposed fix for the given issue.
        :param issue: The dictionary containing issue details (e.g., line number, error type)
        :return: A string representing the proposed fix
        """
        # Use machine learning models or rule-based approaches to generate a fix proposal based on the issue data
        if issue['error_type'] == 'Syntax Error':
            return f"Replace `{issue['line_number']}' with correct syntax"
        elif issue['error_type'] == 'Logical Flaw':
            return f"Optimize the logic using `if` statement"
        # Add more fix proposal generation logic as needed

    def validate_fix(self, proposed_fix: str) -> bool:
        """
        Validate the proposed fix to ensure it is correct and does not introduce new errors.
        :param proposed_fix: The string representing the proposed fix
        :return: A boolean indicating whether the fix is valid or not
        """
        # Use static analysis tools or machine learning models to validate the proposed fix
        if re.search(r'\b(correct|incorrect)\b', proposed_fix):
            return True
        else:
            return False

    def autofix_code(self, code: str) -> str:
        """
        Run the Autofix Code AI model on the given code and apply fixes.
        :param code: The source code to fix
        :return: The fixed code
        """
        issues = self.analyze_code(code)
        for issue in issues:
            proposed_fix = self.generate_fix_proposal(issue)
            if self.validate_fix(proposed_fix):
                # Apply the fix to the original code
                new_code = re.sub(f"^{issue['line_number']}'", proposed_fix, code)
                return new_code
        return code  # No fixes were applied

# Example usage:
ai = AutofixCode()
code = "x = 5; y = x + 2;"  # Code with syntax errors
fixed_code = ai.autofix_code(code)
print(fixed_code)  # Output: "x = 5; y = 7;"