Update app.py

app.py

@@ -60,6 +60,76 @@ def _load_model():
 def health():
     return {"ok": predictor is not None, "uptime_s": time.time()-t0}
 
+# Ordine delle classi (stesso usato dal modello)
+_CLASS_ORDER = LABELS + ["100%"]
+_CLASS_TO_IDX = {c: i for i, c in enumerate(_CLASS_ORDER)}
+
+def _payload_from_inp(inp) -> dict:
+    """Ricostruisce un dict 'payload' a partire dall'input pydantic."""
+    payload = {}
+    for k in FEATURE_MAP.values():
+        ak = k.replace(" ", "_").replace(".", "_")
+        payload[k] = getattr(inp, ak, None)
+    return payload
+
+def _moving_average(y: np.ndarray, window: int = 9):
+    """Applica una media mobile semplice per smoothing."""
+    w = int(window)
+    if w < 1: 
+        return y
+    if w % 2 == 0: 
+        w += 1
+    if w > len(y): 
+        w = max(1, len(y)//2*2+1)
+    kernel = np.ones(w) / w
+    return np.convolve(y, kernel, mode="same")
+
+def _class_curve_png(predictor, base_payload: dict, var_name: str,
+                     vmin: int = 0, vmax: int = 3000,
+                     n_base: int = 80,     # punti reali (inferenze)
+                     n_dense: int = 400,   # punti interpolati
+                     ma_window: int = 9,
+                     title: str = "") -> bytes:
+    xs_base = np.linspace(vmin, vmax, n_base).round().astype(int)
+    xs_base = np.clip(xs_base, vmin, vmax)
+    xs_base = np.unique(xs_base)
+
+    # classe → indice
+    y_base = []
+    for v in xs_base:
+        p = dict(base_payload)
+        p[var_name] = int(v)
+        out = predictor.predict_class_fast(p)
+        y_base.append(_CLASS_TO_IDX[out["class"]])
+    y_base = np.array(y_base, dtype=float)
+
+    # interpolazione
+    xs_dense = np.linspace(vmin, vmax, n_dense)
+    y_dense = np.interp(xs_dense, xs_base, y_base)
+
+    # smoothing
+    y_smooth = _moving_average(y_dense, ma_window)
+    y_smooth = np.clip(y_smooth, 0, len(_CLASS_ORDER)-1)
+
+    # plot
+    fig, ax = plt.subplots(figsize=(9, 4))
+    ax.plot(xs_dense, y_smooth, linewidth=2)
+    ax.set_xlim(vmin, vmax)
+    ax.set_ylim(-0.2, len(_CLASS_ORDER)-1 + 0.2)
+    ax.set_yticks(range(len(_CLASS_ORDER)))
+    ax.set_yticklabels(_CLASS_ORDER)
+    ax.set_xlabel(var_name)
+    ax.set_ylabel("Classe (smooth)")
+    ax.set_title(title or f"Classe (smooth) vs {var_name}")
+    ax.grid(True, linestyle="--", alpha=0.35)
+    fig.tight_layout()
+
+    buf = io.BytesIO()
+    fig.savefig(buf, format="png", dpi=150, bbox_inches="tight")
+    plt.close(fig)
+    return buf.getvalue()
+
+
 @app.post("/predict")
 def predict(inp: PredictIn):
     if predictor is None:
@@ -89,3 +159,34 @@ def predict(inp: PredictIn):
     except Exception as e:
         log.exception("predict_error")
         raise HTTPException(500, f"Prediction error: {e}") from e
+        
+@app.post("/plot/curve-class-cessione.png")
+def plot_curve_class_cessione(inp: PredictIn,
+                              vmin: int = 0, vmax: int = 3000,
+                              n_base: int = 80, n_dense: int = 400, ma_window: int = 9):
+    if predictor is None:
+        raise HTTPException(503, "Model not ready")
+    base_payload = _payload_from_inp(inp)
+    img = _class_curve_png(
+        predictor, base_payload,
+        var_name="giorni_da_cessione",
+        vmin=vmin, vmax=vmax, n_base=n_base, n_dense=n_dense, ma_window=ma_window,
+        title="Classe predetta  vs Giorni da Cessione"
+    )
+    return Response(content=img, media_type="image/png")
+
+@app.post("/plot/curve-class-iscrizione.png")
+def plot_curve_class_iscrizione(inp: PredictIn,
+                                vmin: int = 0, vmax: int = 3000,
+                                n_base: int = 80, n_dense: int = 400, ma_window: int = 9):
+    if predictor is None:
+        raise HTTPException(503, "Model not ready")
+    base_payload = _payload_from_inp(inp)
+    img = _class_curve_png(
+        predictor, base_payload,
+        var_name="giorni_da_iscrizione",
+        vmin=vmin, vmax=vmax, n_base=n_base, n_dense=n_dense, ma_window=ma_window,
+        title="Classe predetta (smooth) vs Giorni da Iscrizione"
+    )
+    return Response(content=img, media_type="image/png")
+