trying to thrive

Author: Electroiscoding

README.md

@@ -372,7 +372,6 @@ def calculate_fibonacci(n: int) -> str:
 #### HCMSManager
 Hierarchical memory with FAISS vector search.
 
-
 ### Layer 2: Mathematical Grounding
 - **Z3 Theorem Prover**: Proves contradictions in factual claims
 - **Fact Extraction**: Parses natural language into verifiable assertions

src/hanerma/interface/empathy.py

@@ -1,10 +1,12 @@
 import json
 import requests
-from typing import Dict, Any
+import ast
+from typing import Dict, Any, Optional
 
 class EmpathyHandler:
     """
     Emotional Failsafe: Sends tracebacks to local model for JSON mitigation strategies.
+    Enhanced with autonomous AST patching capabilities.
     """
 
     def get_mitigation(self, traceback_str: str) -> Dict[str, Any]:
@@ -52,6 +54,68 @@ def get_mitigation(self, traceback_str: str) -> Dict[str, Any]:
                 "action": "ask_human"
             }
 
+    async def generate_ast_patch(self, error_str: str, node_data: Dict[str, Any]) -> Optional[ast.AST]:
+        """
+        Generates a new AST node to fix the error dynamically.
+        Uses AI to analyze the error and create a patched version of the failing AST node.
+        """
+        system_prompt = """
+        You are an AST Patching AI. Your task is to analyze Python errors and generate corrected AST code.
+
+        Given an error message and the original AST node information, output a valid Python code snippet that fixes the error.
+        The output should be executable Python code that can replace the failing node.
+
+        Consider common fixes like:
+        - Adding try/except blocks
+        - Providing default values for missing variables
+        - Correcting function calls
+        - Adding type checks
+
+        Output ONLY the corrected Python code, no explanations.
+        """
+        
+        # Extract node info
+        original_node = node_data.get('ast_node', {})
+        node_type = type(original_node).__name__ if original_node else "Unknown"
+        
+        prompt = f"""
+        Error: {error_str}
+        Node Type: {node_type}
+        Original Node: {ast.dump(original_node) if original_node else "N/A"}
+
+        Generate corrected Python code:
+        """
+        
+        full_prompt = f"{system_prompt}\n\n{prompt}"
+        
+        try:
+            # Send to local Ollama Qwen model
+            response = requests.post(
+                "http://localhost:11434/api/generate",
+                json={
+                    "model": "qwen",
+                    "prompt": full_prompt,
+                    "stream": False
+                },
+                timeout=15
+            )
+            response.raise_for_status()
+            code_snippet = response.json()["response"].strip()
+            
+            # Parse the generated code into AST
+            try:
+                tree = ast.parse(code_snippet)
+                # Return the first statement as the patch
+                if tree.body:
+                    return tree.body[0]
+            except SyntaxError:
+                return None
+                
+        except Exception:
+            return None
+            
+        return None
+
 def friendly_fail(traceback_str: str) -> Dict[str, Any]:
     handler = EmpathyHandler()
     return handler.get_mitigation(traceback_str)