Update app.py

app.py

@@ -42,6 +42,8 @@ with open(attributes_file_path, 'r') as file:
 COLOURS_DICT = color_data['color_mapping']
 ATTRIBUTES_DICT = attributes_data['attribute_mapping']
 
+DETAILS_THRESHOLD = 0.8 # This is how high the total score of an additional detail attribute should be for it to be included
+
 
 def shot(input, category, level):
     output_dict = {}
@@ -157,13 +159,11 @@ def get_most_common_label(responses):
 def get_predicted_attributes(image_urls, category):
     # Assuming ATTRIBUTES_DICT and pipe are defined outside this function
     attributes = list(ATTRIBUTES_DICT.get(category, {}).keys())
-    print(category, attributes)
     
     # Mapping of possible values per attribute
     common_result = []
     for attribute in attributes:
         values = ATTRIBUTES_DICT.get(category, {}).get(attribute, [])
-        print(attribute, values)
 
         if len(values) == 0:
             continue
@@ -176,7 +176,6 @@ def get_predicted_attributes(image_urls, category):
         responses = pipe(image_urls, candidate_labels=values)
         most_common, score = get_most_common_label(responses)
         common_result.append(most_common)
-        print(common_result)
 
         if attribute == "details":
             # Process additional details labels if the score is higher than 0.8
@@ -184,7 +183,7 @@ def get_predicted_attributes(image_urls, category):
                 values = [value for value in values if value != f"{most_common}, clothing: {category}"]
                 responses = pipe(image_urls, candidate_labels=values)
                 most_common, score = get_most_common_label(responses)
-                if score > 0.8:
+                if score > DETAILS_THRESHOLD:
                     common_result.append(most_common)
 
     # Convert common_result into a dictionary