Update app.py

app.py

@@ -1,19 +1,63 @@
 from fastapi import FastAPI
-from typing import Dict, Any
+from pydantic import BaseModel, Field, conlist, confloat
+from typing import Dict, Any, List, Literal
+from datetime import datetime
 
 app = FastAPI()
 
-@app.post("/audit")
-async def audit(bundle: Dict[str, Any]):
-    # very simple scoring; replace with your real checks later
-    metrics = {
-        "TCS": 0.9 if bundle.get("network",{}).get("data") else 0.5,
-        "HDI": 1.0,
-        "PDS": 0.6,
-        "EVI": 3,
-        "CBS": 0.2,
-        "LQS": 0.6
-    }
-    decision = "approve" if metrics["CBS"] <= 0.6 else "revise"
-    deltas = [] if decision=="approve" else ["Add contrary evidence scan."]
-    return {"metrics": metrics, "decision": decision, "deltas": deltas}
+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)
+
+@app.get("/health")
+def health():
+    return {"ok": True, "service": "auditor", "ts": datetime.utcnow().isoformat()}
+
+@app.post("/audit", response_model=AuditResult)
+def audit(payload: Dict[str, Any]):
+    bundle = payload.get("bundle", {})
+    m_in   = payload.get("metrics", {})
+
+    # Use incoming metrics as a starting point
+    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)),
+    })
+
+    # Simple structural checks (you can expand later)
+    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"))
+
+    # Patch metrics heuristically
+    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))  # pretend we saw a little contrary evidence
+
+    deltas: List[str] = []
+
+    # Gate: if hypotheses thin or contrary evidence high -> REWORK
+    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 address contrary findings explicitly.")
+
+    return AuditResult(metrics=m, decision=decision, deltas=deltas)