from fastapi import FastAPI
from pydantic import BaseModel, conlist, confloat
from typing import Dict, Any, List, Literal
from guardrails import Guard

app = FastAPI()

class Metrics(BaseModel):
    TCS: confloat(ge=0, le=1) = 0.0
    HDI: float = 0.0
    PDS: confloat(ge=0, le=1) = 0.0
    EVI: int = 0
    CBS: confloat(ge=0, le=1) = 0.0
    LQS: confloat(ge=0, le=1) = 0.0

class AuditResult(BaseModel):
    metrics: Metrics
    decision: Literal['APPROVE','REWORK']
    deltas: conlist(str, min_items=0)

# Guardrails schema (you can add validators later)
AUDIT_SCHEMA = {
  "type": "object",
  "properties": {
    "decision": {"type":"string","enum":["APPROVE","REWORK"]},
    "metrics": {"type":"object"},
    "deltas": {"type":"array","items":{"type":"string"}}
  },
  "required": ["decision","metrics","deltas"]
}
guard = Guard().configure(output_schema=AUDIT_SCHEMA)

@app.get("/health")
def health():
    return {"ok": True}

@app.post("/audit", response_model=AuditResult)
def audit(payload: Dict[str, Any]):
    bundle = payload.get("bundle", {}) or {}
    m_in   = payload.get("metrics", {}) or {}

    m = {
      "TCS": float(m_in.get("TCS", 0.0)),
      "HDI": float(m_in.get("HDI", 0.0)),
      "PDS": float(m_in.get("PDS", 0.0)),
      "EVI": int(m_in.get("EVI", 0)),
      "CBS": float(m_in.get("CBS", 0.0)),
      "LQS": float(m_in.get("LQS", 0.0)),
    }

    edges   = len(bundle.get("network",{}).get("data",[]) or [])
    regions = len((bundle.get("regions",{}).get("data") or {}).get("regions_ranked",[]) or [])
    lit_ok  = bool(bundle.get("literature"))

    m["TCS"] = min(1.0, (1 if edges>0 else 0) + (1 if regions>0 else 0) + (1 if lit_ok else 0)) * 0.9
    m["HDI"] = 1.2 if regions>8 else (1.0 if regions>4 else 0.8)
    m["EVI"] = (1 if edges>0 else 0) + (1 if regions>0 else 0) + (1 if lit_ok else 0)
    m["CBS"] = max(0.2, m["CBS"])
    m["LQS"] = max(0.6, m["LQS"])

    decision: Literal['APPROVE','REWORK'] = 'APPROVE'
    deltas: List[str] = []
    if (m["HDI"] < 1.0) or (m["EVI"] < 3) or (m["CBS"] > 0.6):
        decision = 'REWORK'
        if m["HDI"] < 1.0: deltas.append("Increase hypothesis diversity (broaden regions).")
        if m["EVI"] < 3:   deltas.append("Add at least one more evidence type (review/trials).")
        if m["CBS"] > 0.6: deltas.append("Surface/address contrary findings explicitly.")

    out_raw = {"metrics": m, "decision": decision, "deltas": deltas}
    _, validated, *_ = guard.parse(out_raw)
    return AuditResult(**validated)