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| import gradio as gr | |
| from model import DemoModel | |
| import json | |
| import numpy as np | |
| from fetch_prod import Scraper | |
| from copy import deepcopy | |
| from custom_label import format_labels_html | |
| from pdb import set_trace as bp | |
| model = DemoModel() | |
| examples = json.load(open('amzn_examples.json')) | |
| # cache = {x['text']: {'label': x['label']} for x in examples} | |
| cache = json.load(open('cache.json')) | |
| unseen_labels = {x.strip() for x in open('cleaned_code/datasets/Amzn13K/unseen_labels_split6500_2.txt')} | |
| all_labels = {x.strip() for x in open('cleaned_code/datasets/Amzn13K/all_labels.txt')} | |
| descriptions_visible = False | |
| scraper = Scraper() | |
| def format_gold_label_text(tex): | |
| if tex not in cache: | |
| return [] | |
| if 'label' not in cache[tex]: | |
| return [] | |
| return sorted([(x, 'Unseen' if x in unseen_labels else 'Seen' if x in all_labels else 'No Descriptions Available') for x in cache[tex]['label']], key = lambda x: (x[1], x[0]))[::-1] | |
| def extract_topk(preds, is_unseen , k = 5): | |
| preds_clone = deepcopy(preds) | |
| preds_dic = preds_clone['preds'] | |
| # bp() | |
| if is_unseen: | |
| preds_dic = {k:preds_dic[k] for k in set(preds_dic.keys()).intersection(unseen_labels)} | |
| if 'label' in preds_clone: | |
| preds_clone['label'] = list(set(preds_clone['label']).intersection(unseen_labels)) | |
| else: | |
| if 'label' in preds_clone: | |
| preds_clone['label'] = list(set(preds_clone['label']).intersection(all_labels)) | |
| preds_dic = {k:v for k,v in sorted(preds_dic.items(), key = lambda x: -x[1])[:k]} | |
| # bp() | |
| preds_clone['preds'] = preds_dic | |
| return preds_clone | |
| def classify(text, is_unseen): | |
| print(is_unseen) | |
| print('See this', text) | |
| if text in cache and 'preds' in cache[text]: | |
| print('Using Cached Result') | |
| return extract_topk(cache[text], is_unseen)#['preds'] | |
| preds, descs = model.classify(text, unseen_labels if is_unseen else None) | |
| if text not in cache: | |
| cache[text] = dict() | |
| cache[text]['preds'] = preds | |
| cache[text]['descs'] = descs | |
| json.dump(cache, open('cache.json','w'), indent = 2) | |
| print(text, preds) | |
| # return preds | |
| return extract_topk(cache[text], is_unseen) | |
| def scrape_click(url): | |
| out = scraper.get_product(url) | |
| if isinstance(out, str): | |
| if out == 'Invalid URL': | |
| raise gr.Error("Please enter a valid Amazon URL") | |
| else: | |
| print('Error Occured', out) | |
| raise gr.Error("Error Occured. Check the URL or try again later.") | |
| return | |
| text = out['description'] | |
| if text not in cache: | |
| cache[text] = {'label': out['labels']} | |
| return gr.update(value = out['description']) | |
| def get_random_example(): | |
| return np.random.choice(examples)['text'] | |
| def toggle_descriptions_fn(): | |
| print('Toggling descriptions visibility') | |
| global descriptions_visible | |
| descriptions_visible = not descriptions_visible | |
| return descriptions_visible | |
| # for example in examples: | |
| # classify(example['text'], False) | |
| with gr.Blocks(css="#warning {height: 100%}") as demo: | |
| with gr.Column(): | |
| title = "<h1 style='margin-bottom: -10px; text-align: center'>SemSup-XC: Semantic Supervision for Extreme Classification</h1>" | |
| # gr.HTML(title) | |
| gr.Markdown( | |
| "<h1 style='text-align: center; margin-bottom: 1rem'>" | |
| + title | |
| + "</h1>" | |
| ) | |
| description = "<p style='font-size: 14px; margin: 5px; font-weight: w300; text-align: center'> <a href='https://github.com/Pranjal2041' style='text-decoration:none' target='_blank'>Pranjal Aggarwal, </a> <a href='' style='text-decoration:none' target='_blank'>Ameet Deshpande, </a> <a href='' style='text-decoration:none' target='_blank'>Karthik Narasimhan </a> </p>" \ | |
| + "<p style='font-size: 16px; margin: 5px; font-weight: w600; text-align: center'> <a href='https://sites.google.com/view/semsup-xc/home' target='_blank'>Project Page</a> | <a href='https://arxiv.org/abs/' target='_blank'>Paper</a> | <a href='https://github.com/princeton-nlp/semsup-xc' target='_blank'>Github Repo</a></p>" \ | |
| + "<p style='text-align: center; margin: 5px; font-size: 14px; font-weight: w300;'> \ | |
| Extreme classification (XC) considers the scenario of predicting over a very large number of classes (thousands to millions), with real-world applications including serving search engine results, e-commerce product tagging, and news article classification. A real-life requirement in this domain is to predict from labels unseen during training(Zero-Shot), however there have been very little success in this domain. To this end, we propose SemSup-XC, a model that achieves state-of-the-art zero-shot (ZS) and few-shot (FS) performance on three extreme classification benchmarks spanning various domains. Instead of treating labels as class ids, our model learns from diverse descriptions of them, thereby attaining a more better understanding of the label space, evident from qualitative and quantitative results. \ | |
| </p>" \ | |
| # gr.HTML(description) | |
| gr.Markdown(description) | |
| # head_html = gr.HTML(''' | |
| # <h1> | |
| # SemSup-XC | |
| # </h1> | |
| # <p style='text-align: center;'> | |
| # Ask stable diffusion for images by speaking (or singing 🤗) in your native language ! Try it in French 😉 | |
| # </p> | |
| # <p style='text-align: center;'> | |
| # This demo is wired to the official SD Space • Offered by Sylvain <a href='https://twitter.com/fffiloni' target='_blank'>@fffiloni</a> • <img id='visitor-badge' alt='visitor badge' style='display: inline-block' /><br /> | |
| # — | |
| # </p> | |
| # ''') | |
| gr.Markdown( | |
| """ | |
| <br> | |
| <br> | |
| Our model was trained on over 1 million product descriptions from Amazon on 6500 different categories. | |
| SemSup-XC can generalize to both seen and unseen labels. | |
| You can either use already available examples or enter your own text to classify. | |
| You can also fetch product descriptions by simply entering the product link, and classify categories on both seen and unseen labels. | |
| """ | |
| ) | |
| with gr.Tab(label = "Amazon"): | |
| # with gr.Row(): | |
| # with gr.Column(scale = 4): | |
| url_textbox = gr.Textbox( | |
| label = 'URL for Amazon Product', | |
| lines=1, | |
| interactive=True | |
| ) | |
| # with gr.Column(scale = 1): | |
| # scrape_btn = CustomButton(value = 'Fetch')#gr.Button('Fetch') | |
| scrape_btn = gr.Button('Fetch', variant='primary') | |
| # example_selection_dropdown = gr.Dropdown(choices = ["Example 1", "Example 2", "Example 3"], value = "Example 1", label = "Select an Example", interactive = True) | |
| text_box = gr.Textbox( | |
| label="Text to Classify", | |
| lines=4, | |
| interactive=True, | |
| value = get_random_example(), | |
| ) | |
| # example_selection_dropdown.change(fn=lambda value: gr.update(value=value), inputs=example_selection_dropdown, outputs=text_box) | |
| with gr.Row(): | |
| classify_btn = gr.Button("Classify", variant = 'primary') | |
| random_example_btn = gr.Button("Try Random") | |
| radio_btn = gr.Radio(choices = ['Unseen Labels', 'All Labels'], value = 'Unseen Labels', label = 'Classify on', interactive = True) | |
| # classified_labels_text = gr.Label( | |
| # label = "Predicted Classes", | |
| # visible = False, | |
| # num_top_classes = 5, | |
| # ) | |
| # classified_labels_text.style(True) | |
| with gr.Column(variant = 'panel'): | |
| label_html = gr.HTML('''''', visible=False) | |
| with gr.Row(): | |
| with gr.Column(scale = 8): | |
| gold_labels = gr.HighlightedText( | |
| label="Gold Labels", | |
| value=[("Label 1", "Seen"), ("Label 2", "Seen"), ("Label 3", "Unseen"), ("Label 4", "No Descriptions Available")], | |
| disabled=True, | |
| visible=False | |
| ) | |
| with gr.Column(scale = 1): | |
| toggle_descriptions = gr.Button( | |
| "Toggle Descriptions", | |
| visible= False, | |
| elem_id='warning' | |
| ) | |
| gold_labels.style(color_map = {'Seen': 'green', 'Unseen': 'blue', 'No Descriptions': 'gray'}) | |
| # label_html2 = gr.HTML('''''', visible=False) | |
| # classify_btn.click(lambda value, is_unseen: gr.update(value = classify(value, is_unseen == 'Unseen Labels'), visible = True), inputs = [text_box, radio_btn], outputs=classified_labels_text) | |
| classify_btn.click(lambda value, is_unseen: gr.update(value = format_labels_html(classify(value, is_unseen == 'Unseen Labels'), desc_is_visible = descriptions_visible), visible = True), inputs = [text_box, radio_btn], outputs=label_html) | |
| classify_btn.click(lambda x: gr.update(visible=True), inputs = classify_btn, outputs = label_html) | |
| random_example_btn.click(lambda value: gr.update(value = get_random_example()), inputs= random_example_btn, outputs=text_box) | |
| random_example_btn.click(lambda value: (gr.update(visible = False), gr.update(visible = False), gr.update(visible = False)), inputs = random_example_btn, outputs=[label_html, gold_labels, toggle_descriptions]) | |
| # radio_btn.change(lambda value: gr.update(visible)) | |
| radio_btn.change(lambda value, is_unseen: gr.update(value = format_labels_html(classify(value, is_unseen == 'Unseen Labels'), desc_is_visible = descriptions_visible)), inputs = [text_box, radio_btn], outputs=label_html) | |
| scrape_btn.click(lambda value : scrape_click(value), inputs = url_textbox, outputs = text_box) | |
| classify_btn.click(lambda x : gr.update(value = format_gold_label_text(x), visible = x in cache and 'label' in cache[x]), inputs = text_box, outputs = gold_labels) | |
| classify_btn.click(lambda x : gr.update(visible = True), inputs = text_box, outputs = toggle_descriptions) | |
| toggle_descriptions.click(lambda value, is_unseen: gr.update(value = format_labels_html(classify(value, is_unseen == 'Unseen Labels'), desc_is_visible = toggle_descriptions_fn()), visible = True), inputs = [text_box, radio_btn], outputs=label_html) | |
| if __name__ == '__main__': | |
| demo.launch(share = False) |