Update main.py

main.py

@@ -63,6 +63,13 @@ def predict(item: Item):
     # Output the top X duplicate images
     for score, image_id1, image_id2 in duplicates[0:NUM_SIMILAR_IMAGES]:
         print("\nScore: {:.3f}%".format(score * 100))
+
+    # Check if there are any duplicates
+    if duplicates:
+        # Find the top score among duplicates
+        top_score = max(duplicates, key=lambda x: x[0])[0]
+        formatted_score = round(top_score * 100, 3)  # Multiplies by 100 and rounds to three decimal places
+        return ScoreResponse(score=formatted_score)        
     
     # =================
     # NEAR DUPLICATES
@@ -75,11 +82,20 @@ def predict(item: Item):
     threshold = 0.99
     near_duplicates = [image for image in processed_images if image[0] < threshold]
     
-    for score, image_id1, image_id2 in near_duplicates[0:NUM_SIMILAR_IMAGES]:
-        print("\nScore: {:.3f}%".format(score * 100))
+    #for score, image_id1, image_id2 in near_duplicates[0:NUM_SIMILAR_IMAGES]:
+        #print("\nScore: {:.3f}%".format(score * 100))
 
-    formatted_score = round(score * 100, 3)  # Multiplies by 100 and rounds to three decimal places
+    # Find the top score from near duplicates
+    if near_duplicates:
+        top_score = max(near_duplicates, key=lambda x: x[0])[0]
+    else:
+        top_score = 0  # Default score if there are no near duplicates
+
+    formatted_score = round(top_score * 100, 3)  # Multiplies by 100 and rounds to three decimal places
     return ScoreResponse(score=formatted_score)
+
+    #formatted_score = round(score * 100, 3)  # Multiplies by 100 and rounds to three decimal places
+    #return ScoreResponse(score=formatted_score)
     
     #return "Score: {:.3f}%".format(score * 100)