Update app.py

app.py

@@ -19,12 +19,56 @@ model = tf.keras.models.load_model(MODEL_PATH, compile=False)
 with open(STATS_PATH, "r") as f:
     stats: Dict[str, Dict[str, float]] = json.load(f)
 
+# IMPORTANT: FEATURES order must match training!
 FEATURES = list(stats.keys())
 print("Feature order:", FEATURES)
 
+# ---------- robust numeric coercion ----------
+def coerce_float(val: Any) -> float:
+    """
+    Accepts numeric, or strings like:
+      "49.709,14"  -> 49709.14
+      "49,709.14"  -> 49709.14
+      "0,005"      -> 0.005
+      "  1 234 "   -> 1234
+    Returns float, or raises ValueError if impossible.
+    """
+    if isinstance(val, (int, float)):
+        return float(val)
+
+    s = str(val).strip()
+    if s == "":
+        raise ValueError("empty")
+
+    # remove spaces
+    s = s.replace(" ", "")
+
+    has_dot = "." in s
+    has_comma = "," in s
+
+    if has_dot and has_comma:
+        # Decide which is decimal separator by last occurrence
+        last_dot = s.rfind(".")
+        last_comma = s.rfind(",")
+        if last_comma > last_dot:
+            # decimal is comma, thousands is dot
+            s = s.replace(".", "")
+            s = s.replace(",", ".")
+        else:
+            # decimal is dot, thousands is comma
+            s = s.replace(",", "")
+    elif has_comma and not has_dot:
+        # likely decimal is comma
+        s = s.replace(",", ".")
+    else:
+        # dots only or pure digits -> leave as is
+        pass
+
+    return float(s)
+
 def _z(val: Any, mean: float, sd: float) -> float:
     try:
-        v = float(val)
+        v = coerce_float(val)
     except Exception:
         return 0.0
     if not sd:
@@ -54,10 +98,7 @@ app.add_middleware(
 
 @app.get("/")
 def root():
-    return {
-        "message": "Static Fingerprint API is running.",
-        "try": ["GET /health", "POST /predict"],
-    }
+    return {"message": "Static Fingerprint API is running.", "try": ["GET /health", "POST /predict"]}
 
 @app.get("/health")
 def health():
@@ -69,6 +110,11 @@ def health():
         "stats_file": STATS_PATH,
     }
 
+@app.post("/echo")
+async def echo(req: Request):
+    payload = await req.json()
+    return {"received": payload}
+
 @app.post("/predict")
 async def predict(req: Request):
     """
@@ -89,14 +135,15 @@ async def predict(req: Request):
     z_detail = {}
     missing = []
     for f in FEATURES:
-        val = payload.get(f, 0)
         mean = stats[f]["mean"]
         sd   = stats[f]["std"]
-        zf = _z(val, mean, sd)
+        if f in payload:
+            zf = _z(payload[f], mean, sd)
+        else:
+            missing.append(f)
+            zf = _z(0.0, mean, sd)  # treat missing as 0 input
         z.append(zf)
         z_detail[f] = zf
-        if f not in payload:
-            missing.append(f)
 
     X = np.array([z], dtype=np.float32)
     raw = model.predict(X, verbose=0)
@@ -106,7 +153,6 @@ async def predict(req: Request):
         probs = coral_probs_from_logits(raw)[0]
     else:
         probs = raw[0]
-        # If it's not normalized, normalize defensively:
         s = float(np.sum(probs))
         if s > 0:
             probs = probs / s
@@ -118,23 +164,4 @@ async def predict(req: Request):
         "z_scores": z_detail,
         "probabilities": {CLASSES[i]: float(probs[i]) for i in range(len(CLASSES))},
         "predicted_state": CLASSES[pred_idx],
-    }
-
-    # ---- add to app.py ----
-from fastapi import FastAPI, HTTPException, Request
-from fastapi.responses import JSONResponse
-import traceback
-
-@app.post("/echo")
-async def echo(payload: dict):
-    return {"received": payload}
-
-@app.post("/predict")
-async def predict(payload: dict):
-    try:
-        return predict_from_json(payload)  # your existing function
-    except Exception as e:
-        tb = traceback.format_exc()
-        print("PREDICT ERROR:", tb)
-        # Return 400 with details so Excel can read it
-        return JSONResponse(status_code=400, content={"error": str(e)})
+    }