Update app.py

app.py

@@ -768,13 +768,14 @@ def respond_pkb(
     if type(model) == CrossEncoder:
         sentence_pairs = [[message, v[6:]] for v in codes]
         scores = model.predict(sentence_pairs, convert_to_tensor=True)
-        # weights = [1,1]
+        weights = [1,1]
     else:
         text_emb = model.encode(message)
         scores = cos_sim(codes_emb, text_emb).mean(axis=-1)#[:,0]
-        # weights = [19,8]
+        weights = [19,8]
         
-    # scores_argsort = scores.argsort(descending=True)
+    scores_argsort = scores.argsort(descending=True)
+    w_avg = np.average(scores[scores_argsort].numpy(), weights=weights)
 
 
     out = ""
@@ -802,19 +803,22 @@ def respond_pkb(
             
           out += "\n--"
 
-          if scores[0] > threshold:
+          if scores[scores_argsort[0]] < threshold:
+              continue
+
+          if scores[0] > w_avg:
               out += "\nRumus Bea Balik Nama Kendaraan Bermotor (BBNKB) : TARIF x NJKB x PENGENAAN"
               out += f"\nKalkulasi : 10% x Rp{int(nilai_jual):,} x 100%"
               out += f"\nTotal Pembayaran : Rp{int(bbnkb):,}\n"
-          if scores[1] > threshold:
+          if scores[1] > w_avg:
               out += "\nRumus Pokok Pajak Kendaraan Bermotor (PKB) : TARIF * NJKB * BOBOT * PENGENAAN"
               out += f"\nKalkulasi : 1% * Rp{int(nilai_jual):,} * {bobot} * 100%"
               out += f"\nTotal Pembayaran : Rp{int(pkb):,}\n"
 
-    if not out:
-        if scores[0] > threshold:
+    if not out and scores[scores_argsort[0]] >= threshold:
+        if scores[0] > w_avg:
             out += "\nRumus Bea Balik Nama Kendaraan Bermotor (BBNKB) : TARIF x NJKB x PENGENAAN"
-        if scores[1] > threshold:
+        if scores[1] > w_avg:
             out += "\nRumus Pokok Pajak Kendaraan Bermotor (PKB) : TARIF * NJKB * BOBOT * PENGENAAN"
 
     return out