agent_brain.py

import os
import json
import re
from groq import Groq

class NagaMLOpsAgent:
    def __init__(
        self,
        api_key: str = "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",
        model: str = "llama-3.3-70b-versatile"
    ):
        self.api_key = api_key
        self.primary_model = model
        self.fallback_models = [
            "llama-3.3-70b-versatile",
            "llama-3.1-8b-instant",
            "deepseek-r1-distill-llama-70b",
            "mixtral-8x7b-32768"
        ]
        self.client = Groq(api_key=self.api_key)

    def diagnose_and_heal(
        self,
        script_code: str,
        execution_logs: str,
        telemetry: dict,
        fault_name: str = None
    ) -> dict:
        """
        Sends error logs, broken python code, and telemetry context to Groq ultra-fast API.
        Returns a structured dictionary containing root cause diagnosis and executable patched code.
        """
        system_prompt = (
            "You are an expert Autonomous MLOps & AI Infrastructure Diagnostic Agent.\n"
            "Your task is to analyze failing machine learning pipelines, identify root causes, "
            "and generate production-grade, fully working Python code patches to fix the issue.\n\n"
            "CRITICAL INSTRUCTION: You MUST return your answer in valid JSON format matching this EXACT schema:\n"
            "{\n"
            '  "fault_category": "DATA_DRIFT | CODE_RUNTIME_ERROR | NAN_LOSS | OOM_SPIKE | MODEL_ACCURACY_DROP",\n'
            '  "severity": "CRITICAL | HIGH | MEDIUM",\n'
            '  "root_cause_analysis": "Detailed explanation of why the crash/degradation happened.",\n'
            '  "explanation_for_engineers": "Actionable summary for MLOps dashboard.",\n'
            '  "patch_code": "FULL valid Python script replacing the broken code completely without placeholder comments.",\n'
            '  "verification_checklist": ["Check 1", "Check 2"]\n'
            "}"
        )

        user_content = (
            f"=== REPORTED FAULT SCENARIO ===\n{fault_name or 'Auto-Detected Anomaly'}\n\n"
            f"=== PIPELINE TELEMETRY ===\n{json.dumps(telemetry, indent=2)}\n\n"
            f"=== BROKEN PIPELINE CODE ===\n```python\n{script_code}\n```\n\n"
            f"=== EXECUTION LOGS & STACKTRACE ===\n{execution_logs}\n\n"
            "Perform root cause analysis and produce the fixed `patch_code` Python script. "
            "Ensure the patch is self-contained, syntax-correct, and completely resolves the error."
        )

        response_text = ""
        last_error = None
        
        models_to_try = [self.primary_model] + [m for m in self.fallback_models if m != self.primary_model]
        
        for m in models_to_try:
            try:
                completion = self.client.chat.completions.create(
                    model=m,
                    messages=[
                        {"role": "system", "content": system_prompt},
                        {"role": "user", "content": user_content}
                    ],
                    temperature=0.1,
                    response_format={"type": "json_object"}
                )
                response_text = completion.choices[0].message.content.strip()
                if response_text:
                    print(f"[AgentBrain] Super-fast Groq diagnosis generated using model: {m}")
                    break
            except Exception as e:
                print(f"[AgentBrain] Model {m} failed: {e}. Trying next fallback...")
                last_error = e

        if not response_text:
            return self._generate_fallback_diagnosis(script_code, str(last_error))

        return self._parse_agent_response(response_text, script_code)

    def _parse_agent_response(self, raw_text: str, original_code: str) -> dict:
        clean_text = raw_text
        if "```json" in clean_text:
            clean_text = clean_text.split("```json")[1].split("```")[0].strip()
        elif "```" in clean_text and clean_text.strip().startswith("```"):
            clean_text = clean_text.split("```")[1].split("```")[0].strip()

        try:
            parsed = json.loads(clean_text)
            patch = parsed.get("patch_code", original_code)
            if isinstance(patch, dict):
                patch = patch.get("code", patch.get("script", str(patch)))
            elif not isinstance(patch, str):
                patch = str(patch)

            if "```python" in patch:
                patch = patch.split("```python")[1].split("```")[0].strip()
            elif "```" in patch:
                patch = patch.split("```")[1].split("```")[0].strip()
            parsed["patch_code"] = str(patch)
            return parsed
        except Exception as json_err:
            print(f"[AgentBrain] JSON parsing failed: {json_err}. Extracting code via regex fallback...")
            code_match = re.search(r"```python(.*?)```", raw_text, re.DOTALL)
            patch_code = code_match.group(1).strip() if code_match else original_code
            
            return {
                "fault_category": "CODE_RUNTIME_ERROR",
                "severity": "HIGH",
                "root_cause_analysis": raw_text[:300] + "...",
                "explanation_for_engineers": "Agent generated fix. Extracted code patch successfully.",
                "patch_code": patch_code,
                "verification_checklist": ["Execute patched script", "Verify pipeline telemetry"]
            }

    def _generate_fallback_diagnosis(self, original_code: str, error_msg: str) -> dict:
        return {
            "fault_category": "CODE_RUNTIME_ERROR",
            "severity": "HIGH",
            "root_cause_analysis": f"Local Fallback Diagnosis: Pipeline exception detected ({error_msg}).",
            "explanation_for_engineers": "Network issue reaching LLM API. Initiated local safety patch.",
            "patch_code": original_code.replace("/ 0", "/ 1.0").replace("np.nan", "0.0"),
            "verification_checklist": ["Local syntax check", "Re-run safety sandbox"]
        }