Update app.py

app.py

@@ -36,7 +36,7 @@ ATTRIBUTES_DICT = attributes_data['attribute_mapping']
 def shot(input, category):
     subColour,mainColour,score = get_colour(ast.literal_eval(str(input)),category)
     common_result = get_predicted_attributes(ast.literal_eval(str(input)),category)
-    # openai_parsed_response = get_openAI_tags(ast.literal_eval(str(input)))
+    openai_parsed_response = get_openAI_tags(ast.literal_eval(str(input)))
     return {
         "colors":{
             "main":mainColour,
@@ -44,7 +44,7 @@ def shot(input, category):
             "score":round(score*100,2)
         }, 
         "attributes":common_result,
-        # "image_mapping":openai_parsed_response
+        "image_mapping":openai_parsed_response
     }
 
 
@@ -135,7 +135,7 @@ def get_openAI_tags(image_urls):
             "content": [
                 {
                 "type": "text",
-                "text": "You're a tagging assistant, you will help label and tag product pictures for my online e-commerce platform. Your tasks will be to return which angle the product images were taken from. You will have to choose from 'full-body', 'half-body', 'side', 'back', or 'zoomed' angles. You should label each of the images with one of these labels depending on which you think fits best (ideally, every label should be used at least once, but only if there are 5 or more images), and should respond with nothing but the labels separated by a comma in the order of the images without any other text. You should label every picture, no more, no less."
+                "text": "You're a tagging assistant, you will help label and tag product pictures for my online e-commerce platform. Your tasks will be to return which angle the product images were taken from. You will have to choose from 'full-body', 'half-body', 'side', 'back', or 'zoomed' angles. You should label each of the images with one of these labels depending on which you think fits best (ideally, every label should be used at least once, but only if there are 5 or more images), and should respond with an unformatted dictionary where the key is a string representation of the url index of the url and the value is the assigned label."
                 }
             ]
             },
@@ -150,7 +150,7 @@ def get_openAI_tags(image_urls):
         frequency_penalty=0,
         presence_penalty=0
     )
-    response= json.loads(openai_response.choices[0].message.content)
+    response = json.loads(openai_response.choices[0].message.content)
     return response
 
 # Define the Gradio interface with the updated components