app.py

import pickle
import os
from sklearn.neighbors import NearestNeighbors
import numpy as np
import gradio as gr
from PIL import Image

data_root = 'https://ai-vision-public-datasets.s3.eu.cloud-object-storage.appdomain.cloud/DomainNet'
feat_dir = 'brad_feats'
domains = ['sketch', 'painting', 'clipart', 'real']
shots = '-1'
num_nn = 20

search_domain = 'all'
num_results_per_domain = 5
src_data_dict = {}
if search_domain == 'all':
    for d in domains:
        with open(os.path.join(feat_dir, f'dst_{d}_{shots}.pkl'), 'rb') as fp:
            src_data = pickle.load(fp)

            src_nn_fit = NearestNeighbors(n_neighbors=num_results_per_domain, algorithm='auto', n_jobs=-1).fit(src_data[1])
            src_data_dict[d] = (src_data,src_nn_fit)
else:

    with open(os.path.join(feat_dir, f'dst_{search_domain}_{shots}.pkl'), 'rb') as fp:
        src_data = pickle.load(fp)
        src_nn_fit = NearestNeighbors(n_neighbors=num_results_per_domain, algorithm='auto', n_jobs=-1).fit(src_data[1])
        src_data_dict[search_domain] = (src_data,src_nn_fit)

dst_data_dict = {}
min_len = 1e10
for d in domains:
    with open(os.path.join(feat_dir, f'src_{d}_{shots}.pkl'), 'rb') as fp:
        dst_data_dict[d] = pickle.load(fp)
        min_len = min(min_len, len(dst_data_dict[d][0]))

def query(query_index, query_domain):
    dst_data = dst_data_dict[query_domain]
    dst_img_path = os.path.join(data_root, dst_data[0][query_index])
    img_paths = [dst_img_path]
    q_cl = dst_img_path.split('/')[-2]
    captions = [f'Query: {q_cl}'.title()]
    for s_domain, s_data in src_data_dict.items():
        _, top_n_matches_ids = s_data[1].kneighbors(dst_data[1][query_index:query_index+1])
        top_n_labels = s_data[0][2][top_n_matches_ids][0]
        src_img_pths = [os.path.join(data_root, s_data[0][0][ix]) for ix in top_n_matches_ids[0]]
        img_paths += src_img_pths

        for p in src_img_pths:
            src_cl = p.split('/')[-2]
            src_file =  p.split('/')[-1]
            captions.append(src_cl.title())
    print(img_paths)
    return tuple([p for p in img_paths])+ tuple(captions)

demo = gr.Blocks()
with demo:
    gr.Markdown('# Unsupervised Domain Generalization by Learning a Bridge Across Domains')
    gr.Markdown('This demo showcases the cross-domain retrieval capabilities of our self-supervised cross domain training as presented @CVPR 2022. For details please refer to [the paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Harary_Unsupervised_Domain_Generalization_by_Learning_a_Bridge_Across_Domains_CVPR_2022_paper.pdf)')
    gr.Markdown('## Instructions:')
    gr.Markdown('Select a query domain from the dropdown menu and the select any random image from the domain using the slider below. The retrieved results from each of the four domains, along with the class label will be presented.')
    gr.Markdown('## Select Query Domain: ')
    domain_drop = gr.Dropdown(domains)
#     domain_select_button = gr.Button("Select Domain")
    slider = gr.Slider(0, min_len)
    # slider = gr.Slider(0, 10000)
    image_button = gr.Button("Run")

    with gr.Row():
        gr.Markdown('# Query Image: \t\t\t\t                   ')
        gr.Markdown('\t')
        gr.Markdown('\t')
        gr.Markdown('\t')
        with gr.Column():
            src_cap = gr.Label()
            src_img = gr.Image()


    out_images = []
    out_captions = []
    for d in domains:
        gr.Markdown(f'# Retrieved Images from {d.title()} Domain:')
        with gr.Row():
            for _ in range(num_results_per_domain):
                with gr.Column():
                    out_captions.append(gr.Label())
                    out_images.append(gr.Image())

    image_button.click(query, inputs=[slider, domain_drop], outputs=[src_img]+out_images +[src_cap]+ out_captions)

demo.launch(share=True)