Create model.py

model.py

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+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;"