# app.py (FINAL w/ contradiction scan)
from fastapi import FastAPI
from fastapi.responses import JSONResponse
from pydantic import BaseModel, Field
from typing import Dict, Any, List, Literal
from datetime import datetime
import json

app = FastAPI()

class Metrics(BaseModel):
    TCS: float = Field(ge=0.0, le=1.0, default=0.0)
    HDI: float = 0.0
    PDS: float = Field(ge=0.0, le=1.0, default=0.0)
    EVI: int = 0
    CBS: float = Field(ge=0.0, le=1.0, default=0.0)
    LQS: float = Field(ge=0.0, le=1.0, default=0.0)

class AuditResult(BaseModel):
    metrics: Metrics
    decision: Literal['APPROVE','REWORK']
    deltas: List[str] = Field(default_factory=list)

@app.get("/")
def root():
    return JSONResponse({"ok": True, "service": "auditor", "hint": "use /health or POST /audit"})

@app.get("/health")
def health():
    return {"ok": True, "service": "auditor", "ts": datetime.utcnow().isoformat()}

def contradiction_hits(text: str) -> int:
    flags = [
        "no significant", "not associated", "fails to", "did not find",
        "contradict", "contradiction", "inconsistent", "null result",
        "lack of association", "negative finding", "non-significant"
    ]
    t = text.lower()
    return sum(1 for f in flags if f in t)

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

    # seed metrics from input
    m = Metrics(**{
        "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"))

    # heuristic patches
    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
    if regions > 8: m.HDI = max(m.HDI, 1.2)
    elif regions > 4: m.HDI = max(m.HDI, 1.0)
    else: m.HDI = max(m.HDI, 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, float(m.CBS))

    # NEW: contradiction scan → raises CBS if many “conflict” phrases are present
    try:
        blob = json.dumps(bundle, default=str)
    except Exception:
        blob = str(bundle)
    hits = contradiction_hits(blob)
    # add up to +0.7 in steps of +0.1 (cap at 0.9 total)
    m.CBS = min(0.9, max(m.CBS, 0.2 + 0.1 * hits))

    deltas: List[str] = []
    decision: Literal['APPROVE','REWORK'] = 'APPROVE'
    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 search.")
        if m.EVI < 3:   deltas.append("Add at least one more evidence type (e.g., trials or review).")
        if m.CBS > 0.6: deltas.append("Surface and reconcile conflicting findings explicitly.")

    return AuditResult(metrics=m, decision=decision, deltas=deltas)