"""code_agent.py — Coding Agent: scan→analyze→plan→edit→validate→reflect"""
import asyncio, json, re
from models.ollama_client import OllamaClient

SYSTEM = """Sei un coding agent esperto. Analisi codice, bugfix, refactor.
Rispondi con JSON strutturato quando richiesto."""

class CodeAgent:
  def __init__(self, ollama: OllamaClient): self.ollama = ollama

  async def _llm_json(self, prompt:str, system:str=SYSTEM, max_tokens:int=1024)->dict:
      msgs=[{"role":"system","content":system},{"role":"user","content":prompt}]
      try:
          raw=await self.ollama.chat(msgs,temperature=0.2,max_tokens=max_tokens)
          m=re.search(r'\{[\s\S]+\}',raw)
          return json.loads(m.group()) if m else {}
      except: return {}

  async def analyze_file(self, filepath:str, content:str, goal:str)->dict:
      p=f"Analizza per: {goal}\nFile: {filepath}\nContenuto (prime 3000 char):\n{content[:3000]}\n\nRispondi JSON: {{issues:[], suggestions:[], complexity:'low|medium|high', priority:1-10, safe_to_edit:bool}}"
      r=await self._llm_json(p)
      return r or {"issues":[],"suggestions":[],"complexity":"unknown","priority":5,"safe_to_edit":True}

  async def plan_edit(self, filepath:str, content:str, goal:str)->dict:
      p=f"Pianifica modifiche per: {goal}\nFile: {filepath}\n{content[:2500]}\n\nJSON: {{steps:[{{description,type:'insert|replace|delete',old_code,new_code,risk:'low|medium|high'}}], requires_tests:bool, breaking_change:bool}}"
      r=await self._llm_json(p,max_tokens=2048)
      return r or {"steps":[],"requires_tests":False,"breaking_change":False,"_fallback":True}

  async def generate_fix(self, filepath:str, content:str, issue:str)->str:
      msgs=[{"role":"system","content":SYSTEM},
            {"role":"user","content":f"Correggi: {issue}\nFile: {filepath}\n{content[:4000]}\n\nRestituisci SOLO il codice corretto."}]
      return await self.ollama.chat(msgs,temperature=0.15,max_tokens=4096)

  async def validate_edit(self, original:str, edited:str, goal:str)->dict:
      p=f"Valida modifica per: {goal}\nPRIMA:{original[:1500]}\nDOPO:{edited[:1500]}\nJSON:{{valid:bool,achieves_goal:bool,regressions:[],confidence:0-1}}"
      r=await self._llm_json(p,max_tokens=512)
      return r or {"valid":True,"achieves_goal":True,"regressions":[],"confidence":0.7,"_fallback":True}

  async def full_session(self, goal:str, files:list[dict])->dict:
      session={"goal":goal,"analyzed":[],"edits":[]}
      for f in files[:4]:
          analysis=await self.analyze_file(f["path"],f["content"],goal)
          if (analysis.get("priority") or 0)>3:
              plan=await self.plan_edit(f["path"],f["content"],goal)
              session["analyzed"].append({"path":f["path"],"analysis":analysis,"plan":plan})
              if plan.get("steps"):
                  edited=await self.generate_fix(f["path"],f["content"],goal)
                  val=await self.validate_edit(f["content"],edited,goal)
                  session["edits"].append({"path":f["path"],"edited":edited,"validation":val})
      session["summary"]=f"Analizzati {len(session['analyzed'])} file, {len(session['edits'])} modifiche."
      return session