Update app.py
Browse files
app.py
CHANGED
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@@ -77,31 +77,57 @@ def shot(input, category, level):
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@spaces.GPU
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def get_colour(image_urls, category):
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colourLabels = list(COLOURS_DICT.keys())
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for i in range(len(colourLabels)):
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colourLabels[i] = colourLabels[i] + " clothing: " + category
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responses = pipe(image_urls, candidate_labels=colourLabels)
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# Get the most common colour
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mainColour = responses[0][0]['label'].split(" clothing:")[0]
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if mainColour not in COLOURS_DICT:
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return None, None, None
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# Add category to the end of each label
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labels = COLOURS_DICT[mainColour]
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for i in range(len(labels)):
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labels[i] = labels[i] + " clothing: " + category
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#
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responses = pipe(image_urls, candidate_labels=labels)
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subColour = responses[0][0]['label'].split(" clothing:")[0]
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return subColour, mainColour, responses[0][0]['score']
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@spaces.GPU
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def get_predicted_attributes(image_urls, category):
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# Assuming ATTRIBUTES_DICT and pipe are defined outside this function
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# @spaces.GPU
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# def get_colour(image_urls, category):
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# colourLabels = list(COLOURS_DICT.keys())
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# for i in range(len(colourLabels)):
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# colourLabels[i] = colourLabels[i] + " clothing: " + category
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# responses = pipe(image_urls, candidate_labels=colourLabels)
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# # Get the most common colour
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# mainColour = responses[0][0]['label'].split(" clothing:")[0]
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# if mainColour not in COLOURS_DICT:
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# return None, None, None
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# # Add category to the end of each label
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# labels = COLOURS_DICT[mainColour]
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# for i in range(len(labels)):
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# labels[i] = labels[i] + " clothing: " + category
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# # Run pipeline in one go
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# responses = pipe(image_urls, candidate_labels=labels)
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# subColour = responses[0][0]['label'].split(" clothing:")[0]
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# return subColour, mainColour, responses[0][0]['score']
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@spaces.GPU
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def get_colour(image_urls, category):
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colourLabels = list(COLOURS_DICT.keys())
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for i in range(len(colourLabels)):
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colourLabels[i] = colourLabels[i] + " clothing: " + category
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print("Colour Labels:", colourLabels) # Debug: Print colour labels
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print("Image URLs:", image_urls) # Debug: Print image URLs
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responses = pipe(image_urls, candidate_labels=colourLabels)
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mainColour = responses[0][0]['label'].split(" clothing:")[0]
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if mainColour not in COLOURS_DICT:
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return None, None, None
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labels = COLOURS_DICT[mainColour]
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for i in range(len(labels)):
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labels[i] = labels[i] + " clothing: " + category
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print("Labels for pipe:", labels) # Debug: Confirm labels are correct
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responses = pipe(image_urls, candidate_labels=labels)
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subColour = responses[0][0]['label'].split(" clothing:")[0]
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return subColour, mainColour, responses[0][0]['score']
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@spaces.GPU
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def get_predicted_attributes(image_urls, category):
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# Assuming ATTRIBUTES_DICT and pipe are defined outside this function
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