app.py

import json
import spaces
import requests
import numpy as np
import gradio as gr
from PIL import Image
from io import BytesIO
from turtle import title
from transformers import pipeline
import ast
pipe = pipeline("zero-shot-image-classification", model="patrickjohncyh/fashion-clip")

color_file_path = 'color_config.json'
attributes_file_path = 'attributes_config.json'

# Open and read the COLOR JSON file
with open(color_file_path, 'r') as file:
    color_data = json.load(file)

# Open and read the ATTRIBUTES JSON file
with open(attributes_file_path, 'r') as file:
    attributes_data = json.load(file)

COLOURS_DICT = color_data['color_mapping']
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)
    return {
        "colors":{
            "main":mainColour,
            "sub":subColour,
            "score":round(score*100,2)
        }   
        "attributes":common_result
    }



@spaces.GPU  
def get_colour(image_urls, category):
    colourLabels = list(COLOURS_DICT.keys())
    for i in range(len(colourLabels)):
        colourLabels[i] = colourLabels[i] + " clothing: " + category

    responses = pipe(image_urls, candidate_labels=colourLabels)
    # Get the most common colour
    mainColour = responses[0][0]['label'].split(" clothing:")[0]


    if mainColour not in COLOURS_DICT:
        return None, None, None

    # Add category to the end of each label
    labels = COLOURS_DICT[mainColour]
    for i in range(len(labels)):
        labels[i] = labels[i] + " clothing: " + category

    # Run pipeline in one go
    responses = pipe(image_urls, candidate_labels=labels)
    subColour = responses[0][0]['label'].split(" clothing:")[0]

    return subColour, mainColour, responses[0][0]['score']

@spaces.GPU  
def get_predicted_attributes(image_urls, category):
    # Get the predicted attributes for the image
    # attributes = get_category_attributes(category)
    
    attributes = list(ATTRIBUTES_DICT.get(category,{}).keys())
    
    # Mapping of possible values per attribute
    common_result = []
    for attribute in attributes:
        # values = get_attribute_values(attribute, category)
        values = list(ATTRIBUTES_DICT.get(category,{}).get(attribute,{}).keys())

        if len(values) == 0:
            continue

        # Adjust labels for the pipeline to be in format: "{attr}: {value}, clothing: {category}"
        attribute = attribute.replace("colartype", "collar").replace("sleevelength", "sleeve length").replace("fabricstyle", "fabric")
        values = [f"{attribute}: {value}, clothing: {category}" for value in values]

        # Get the predicted values for the attribute
        responses = pipe(image_urls, candidate_labels=values, device=device)
        result = [response[0]['label'].split(", clothing:")[0] for response in responses]

        # If attribute is details, then get the top 2 most common labels
        if attribute == "details":
            result += [response[1]['label'].split(", clothing:")[0] for response in responses]
            common_result.append(Counter(result).most_common(2))
        else:
            common_result.append(Counter(result).most_common(1))

    # Clean up the results into one long string
    for i, result in enumerate(common_result):
        common_result[i] = ", ".join([f"{x[0]}" for x in result])

    return common_result




# Define the Gradio interface with the updated components
iface = gr.Interface(
    fn=shot, 
    inputs=[
        gr.Textbox(label="Image URLs (starting with http/https) comma seperated "), 
        gr.Textbox(label="Category")
    ], 
    outputs="text" ,
    description="Add an image URL (starting with http/https) or upload a picture, and provide a list of labels separated by commas.",
    title="Full product flow"
)

# Launch the interface
iface.launch()