Update app.py

app.py

@@ -161,7 +161,7 @@ def get_predicted_attributes(image_urls, category):
 
         # Adjust labels for the pipeline to be in format: "{attr}: {value}, clothing: {category}"
         attribute_formatted = attribute.replace("colartype", "collar").replace("sleevelength", "sleeve length").replace("fabricstyle", "fabric")
-        values_formatted = [f"{attribute_formatted}: {value.trim()}, clothing: {category}" for value in values]
+        values_formatted = [f"{attribute_formatted}: {value.strip()}, clothing: {category}" for value in values]
 
         # Get the predicted values for the attribute
         responses = pipe(image_urls, candidate_labels=values_formatted)
@@ -171,8 +171,8 @@ def get_predicted_attributes(image_urls, category):
 
         # If attribute is details, then remove the obtained label from the values, and get the next most common
         if attribute == "details":
-            print(values_formatted, result[0].trim() + f", clothing: {category}")
-            values_formatted.remove(result[0].trim() + f", clothing: {category}")
+            print(values_formatted, result[0].strip() + f", clothing: {category}")
+            values_formatted.remove(result[0].strip() + f", clothing: {category}")
             responses = pipe(image_urls, candidate_labels=values_formatted)
             i = 0
             print("Responses: ", responses)