Add scores

pipeline.py

@@ -42,15 +42,12 @@ class PreTrainedPipeline():
         similarities = distance.cdist(embeddings.reshape((1,300)), self.comparisons, "cosine")[0]
         top_indices = similarities.argsort()[:10]
         top_words = [[self.id2h[str(top_indices[i])]] for i in range(10)]
-        
+        logits = np.exp(-10*np.array(similarities[top_indices]))
+        softmax_probs = tf.nn.softmax(logits).numpy()
+        top_scores = [round(float(softmax_probs[i]), 3) for i in range(10)]
         
         return [
-            [ 
-                {'label': top_words[0], 'score': 0},
-                {'label': top_words[1], 'score': 0},
-                {'label': top_words[2], 'score': 0},
-                {'label': top_words[3], 'score': 0},
-            ]
+            [{'lable': word, 'score': score} for word, score in zip(top_words, top_scores)]
         ]