Update app.py

app.py

@@ -46,26 +46,34 @@ ATTRIBUTES_DICT = attributes_data['attribute_mapping']
 def shot(input, category, level):
     output_dict = {}
     if level == 'variant':
-        subColour,mainColour,score = get_colour(ast.literal_eval(str(input)),category)    
+        subColour, mainColour, score = get_colour(ast.literal_eval(str(input)), category)    
         openai_parsed_response = get_openAI_tags(ast.literal_eval(str(input)))
         face_embeddings = get_face_embeddings(ast.literal_eval(str(input)))
-        cropped_images = get_cropped_images(ast.literal_eval(str(input)),category)
+        cropped_images = get_cropped_images(ast.literal_eval(str(input)), category)
+
+        # Ensure all outputs are JSON serializable
         output_dict['colors'] = {
-            "main":mainColour,
-            "sub":subColour,
-            "score":score
+            "main": mainColour,
+            "sub": subColour,
+            "score": score
         }
         output_dict['image_mapping'] = openai_parsed_response
         output_dict['face_embeddings'] = face_embeddings
         output_dict['cropped_images'] = cropped_images
 
-
     if level == 'product':
-        common_result = get_predicted_attributes(ast.literal_eval(str(input)),category)    
+        common_result = get_predicted_attributes(ast.literal_eval(str(input)), category)    
         output_dict['attributes'] = common_result
         output_dict['subcategory'] = category
     
-    return json.dumps(output_dict)
+    # Convert the dictionary to a JSON-serializable format
+    try:
+        serialized_output = json.dumps(output_dict)
+    except TypeError as e:
+        print(f"Serialization Error: {e}")
+        return {"error": "Serialization failed"}
+
+    return serialized_output
 
 
 
@@ -411,35 +419,55 @@ def encode_images_to_base64(cropped_list):
     return base64_images
 
 
-def get_cropped_images(images,category):
+# def get_cropped_images(images,category):
+#     cropped_list = []
+#     resultsPerCategory = {}
+#     for num, image in enumerate(images):
+#         image = open_image_from_url(image)
+#         class_counts, output_img, cropped_images, cropped_classes = get_objects(image, 0.37)
+#         if not class_counts:
+#             continue
+        
+#         # Get the inverse category as any other mapping label except the current one corresponding category
+#         inverse_category = [label for i, labels in enumerate(label_mapping) for label in labels if i != get_category_index(category) and i != 0]
+        
+#         # If category is a cardigan, we don't recommend category indices 1 and 3
+#         if category == 'women-sweatersknits-cardigan':
+#             inverse_category = [label for i, labels in enumerate(label_mapping) for label in labels if i != get_category_index(category) and i != 1 and i != 3]
+
+#         for i, image in enumerate(cropped_images):
+#             cropped_category = cropped_classes[i]
+#             print(cropped_category, cropped_classes[i], get_category_index(category))
+            
+#             specific_category = label_mapping[cropped_category]
+
+#             if cropped_category == get_category_index(category):
+#                 continue
+
+#             cropped_list.append(image)
+
+
+#     base64_images = encode_images_to_base64(cropped_list)
+
+#     return base64_images
+
+
+
+
+def get_cropped_images(images, category):
     cropped_list = []
     resultsPerCategory = {}
     for num, image in enumerate(images):
         image = open_image_from_url(image)
         class_counts, output_img, cropped_images, cropped_classes = get_objects(image, 0.37)
-        print(cropped_images)
+        
         if not class_counts:
             continue
         
-        # Get the inverse category as any other mapping label except the current one corresponding category
-        inverse_category = [label for i, labels in enumerate(label_mapping) for label in labels if i != get_category_index(category) and i != 0]
-        
-        # If category is a cardigan, we don't recommend category indices 1 and 3
-        if category == 'women-sweatersknits-cardigan':
-            inverse_category = [label for i, labels in enumerate(label_mapping) for label in labels if i != get_category_index(category) and i != 1 and i != 3]
-
         for i, image in enumerate(cropped_images):
-            cropped_category = cropped_classes[i]
-            print(cropped_category, cropped_classes[i], get_category_index(category))
-            
-            specific_category = label_mapping[cropped_category]
-
-            if cropped_category == get_category_index(category):
-                continue
-
             cropped_list.append(image)
-
-
+    
+    # Convert cropped images to base64 strings
     base64_images = encode_images_to_base64(cropped_list)
 
     return base64_images
@@ -447,6 +475,7 @@ def get_cropped_images(images,category):
 
 
 
+
 # Define the Gradio interface with the updated components
 iface = gr.Interface(
     fn=shot,