Update main.py

main.py

@@ -7,7 +7,7 @@ from numpy.linalg import norm
 from scipy.linalg import expm
 
 from fastapi import FastAPI
-from pydantic import BaseModel, Field
+from pydantic import BaseModel
 
 from sentence_transformers import SentenceTransformer
 from huggingface_hub import hf_hub_download
@@ -32,12 +32,11 @@ except Exception as e:
 print("🔄 [Startup] Descargando meta-logit desde HF Hub...", flush=True)
 try:
     meta_logit_path = hf_hub_download(
-    repo_id="antonypamo/RRFSavantMetaLogit",
-    filename="logreg_rrf_savant.joblib",
-    token=os.environ.get("HF_TOKEN"),  # si el repo es público, puede ser None
-)
-
-    print("🔄 [Startup] Cargando modelo meta-logit v...", flush=True)
+        repo_id=META_LOGIT_REPO,
+        filename=META_LOGIT_FILENAME,
+        token=os.environ.get("HF_TOKEN"),  # si el repo es público, puede ser None
+    )
+    print("🔄 [Startup] Cargando modelo meta-logit...", flush=True)
     meta_logit = joblib.load(meta_logit_path)
     print("✅ [Startup] Meta-logit cargado.", flush=True)
 except Exception as e:
@@ -283,30 +282,34 @@ class EvaluateRequest(BaseModel):
     answer: str
     model_label: Optional[str] = None
 
+
 class EvaluateResponse(BaseModel):
     scores: Dict[str, float]
     features: Dict[str, float]
     sim_summary: Dict[str, Any]
 
+
 app = FastAPI(
     title="Savant RRF Φ12.0 API",
     description="Dirac-Resonant conceptual quality layer for LLM-generated text.",
     version="1.0.0",
 )
 
+
 @app.get("/")
 def root():
     return {"message": "Savant RRF Φ12.0 API running", "docs": "/docs"}
 
+
 @app.get("/health")
 def health():
     return {"status": "ok"}
 
+
 @app.post("/evaluate", response_model=EvaluateResponse)
 def evaluate(req: EvaluateRequest):
     scores, feats = compute_scores_srff_crff_ephi(req.prompt, req.answer)
 
-    # pequeño resumen adicional de simulación fresca (opcional)
     H = build_dirac_hamiltonian(
         m=0.25, v=1.0, sigma=0.618,
         alpha_log=0.10, q=1.0,