normalized cosine sim to be between 0 and 1

Signed-off-by: Ilham Syahid S <ilhamsyahids@gmail.com>

app.py

@@ -34,8 +34,15 @@ def calculate_similarities(model, text, *sentences):
 
     # calculate cosine similarity between the input text and the input sentences
     similarities = {}
+
+    # to normalize cosine similarity to be between 0 and 1
+    minx = -1
+    maxx = 1
+
     for sentence, sentence_embedding in zip(sentences, sentences_embeddings):
-        similarities[sentence] = cos_sim(text_embedding, sentence_embedding)
+        sim = cos_sim(text_embedding, sentence_embedding)
+        normalized_sim = (sim - minx) / (maxx - minx)
+        similarities[sentence] = normalized_sim
 
     return similarities
 
@@ -70,12 +77,12 @@ with demo:
     gr.Examples(
         examples=[
             ["roberta", "This is happy person", "هذا شخص سعيد", "هذه قطة سعيدة"],
-            ["roberta", "car", "camry", "toyota"],
-            ["roberta", "هذا شخص سعيد", "هذه قطة سعيدة", "This is happy person"],
-            ["roberta", "ihpone for sale", "iphone for sale", "camry for sale"],
-            ["ada", "camry", "toy", "toyota"],
             ["ada", "This is happy person", "هذا شخص سعيد", "هذه قطة سعيدة"],
+            ["roberta", "هذا شخص سعيد", "هذه قطة سعيدة", "This is happy person"],
             ["ada", "هذا شخص سعيد", "هذه قطة سعيدة", "This is happy person"],
+            ["roberta", "car", "camry", "toyota"],
+            ["ada", "camry", "toy", "toyota"],
+            ["roberta", "ihpone for sale", "iphone for sale", "camry for sale"],
             ["ada", "ihpone for sale", "iphone for sale", "camry for sale"],
         ],
         inputs=[model, text, *inp_sentences],