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| from turtle import down | |
| import spacy | |
| from spacy import displacy | |
| import random | |
| from spacy.tokens import Span | |
| import gradio as gr | |
| import pandas as pd | |
| # import cairosvg | |
| import base64 | |
| DEFAULT_MODEL = "en_core_web" | |
| DEFAULT_TEXT = "Apple is looking at buying U.K. startup for $1 billion." | |
| DEFAULT_TOK_ATTR = ['idx', 'text', 'pos_', 'lemma_', 'shape_', 'dep_'] | |
| DEFAULT_ENTS = ['CARDINAL', 'DATE', 'EVENT', 'FAC', 'GPE', 'LANGUAGE', 'LAW', 'LOC', 'MONEY', | |
| 'NORP', 'ORDINAL', 'ORG', 'PERCENT', 'PERSON', 'PRODUCT', 'QUANTITY', 'TIME', 'WORK_OF_ART'] | |
| DEFAULT_COLOR = "linear-gradient(90deg, #FFCA74, #7AECEC)" | |
| texts = {"en": DEFAULT_TEXT, "ca": "Apple està buscant comprar una startup del Regne Unit per mil milions de dòlars", "da": "Apple overvejer at købe et britisk startup for 1 milliard dollar.", "de": "Die ganze Stadt ist ein Startup: Shenzhen ist das Silicon Valley für Hardware-Firmen", | |
| "el": "Η άνιση κατανομή του πλούτου και του εισοδήματος, η οποία έχει λάβει τρομερές διαστάσεις, δεν δείχνει τάσεις βελτίωσης.", "es": "Apple está buscando comprar una startup del Reino Unido por mil millones de dólares.", "fi": "Itseajavat autot siirtävät vakuutusvastuun autojen valmistajille", "fr": "Apple cherche à acheter une start-up anglaise pour 1 milliard de dollars", "it": "Apple vuole comprare una startup del Regno Unito per un miliardo di dollari", | |
| "ja": "アップルがイギリスの新興企業を１０億ドルで購入を検討", "ko": "애플이 영국의 스타트업을 10억 달러에 인수하는 것을 알아보고 있다.", "lt": "Jaunikis pirmąją vestuvinę naktį iškeitė į areštinės gultą", "nb": "Apple vurderer å kjøpe britisk oppstartfirma for en milliard dollar.", "nl": "Apple overweegt om voor 1 miljard een U.K. startup te kopen", | |
| "pl": "Poczuł przyjemną woń mocnej kawy.", "pt": "Apple está querendo comprar uma startup do Reino Unido por 100 milhões de dólares", "ro": "Apple plănuiește să cumpere o companie britanică pentru un miliard de dolari", "ru": "Apple рассматривает возможность покупки стартапа из Соединённого Королевства за $1 млрд", "sv": "Apple överväger att köpa brittisk startup för 1 miljard dollar.", "zh": "作为语言而言，为世界使用人数最多的语言，目前世界有五分之一人口做为母语。"} | |
| button_css = "float: right; --tw-border-opacity: 1; border-color: rgb(229 231 235 / var(--tw-border-opacity)); --tw-gradient-from: rgb(243 244 246 / 0.7); --tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to, rgb(243 244 246 / 0)); --tw-gradient-to: rgb(229 231 235 / 0.8); --tw-text-opacity: 1; color: rgb(55 65 81 / var(--tw-text-opacity)); border-width: 1px; --tw-bg-opacity: 1; background-color: rgb(255 255 255 / var(--tw-bg-opacity)); background-image: linear-gradient(to bottom right, var(--tw-gradient-stops)); display: inline-flex; flex: 1 1 0%; align-items: center; justify-content: center; --tw-shadow: 0 1px 2px 0 rgb(0 0 0 / 0.05); --tw-shadow-colored: 0 1px 2px 0 var(--tw-shadow-color); box-shadow: var(--tw-ring-offset-shadow, 0 0 #0000), var(--tw-ring-shadow, 0 0 #0000), var(--tw-shadow); -webkit-appearance: button; border-radius: 0.5rem; padding-top: 0.5rem; padding-bottom: 0.5rem; padding-left: 1rem; padding-right: 1rem; font-size: 1rem; line-height: 1.5rem; font-weight: 600;" | |
| def get_all_models(): | |
| with open("requirements.txt") as f: | |
| content = f.readlines() | |
| models = [] | |
| for line in content: | |
| if "huggingface.co" in line: | |
| model = "_".join(line.split("/")[4].split("_")[:3]) | |
| if model not in models: | |
| models.append(model) | |
| return models | |
| models = get_all_models() | |
| def download_svg(svg): | |
| encode = base64.b64encode(bytes(svg, 'utf-8')) | |
| img = 'data:image/svg+xml;base64,' + str(encode)[2:-1] | |
| html = f'<a download="displacy.svg" href="{img}" style="{button_css}">Download as SVG</a>' | |
| return html | |
| # def download_png(svg): | |
| # encode = base64.b64encode(bytes(svg, 'utf-8')) | |
| # svg_uri = 'data:image/svg+xml;base64,' + str(encode)[2:-1] | |
| # output = cairosvg.svg2png(url=svg_uri) | |
| # encoded = base64.b64encode(output) | |
| # img = 'data:image/png;base64,' + str(encoded)[2:-1] | |
| # html = f'<a download="displacy.png" href="{img}" style="{button_css}">Download as PNG</a>' | |
| # return html | |
| # def download(type, svg): | |
| # if type == 'png': | |
| # return download_png(svg) | |
| # elif type == 'svg': | |
| # return download_svg(svg) | |
| def dependency(text, col_punct, col_phrase, compact, bg, font, model): | |
| nlp = spacy.load(model + "_sm") | |
| doc = nlp(text) | |
| options = {"compact": compact, "collapse_phrases": col_phrase, | |
| "collapse_punct": col_punct, "bg": bg, "color": font} | |
| svg = displacy.render(doc, style="dep", options=options) | |
| download = download_svg(svg) | |
| return svg, download | |
| def entity(text, ents, model): | |
| nlp = spacy.load(model + "_sm") | |
| doc = nlp(text) | |
| options = {"ents": ents} | |
| svg = displacy.render(doc, style="ent", options=options) | |
| # download = download_svg('<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" xml:lang="en" id="97e9d3ac65344f2bb6e6ce517bd13b1e-0" class="displacy" width="1800" height="399.5" direction="ltr" style="max-width: none; height: 399.5px; color: black; font-family: Arial; direction: ltr">' + svg + "</svg>") | |
| return svg | |
| def token(text, attributes, model): | |
| nlp = spacy.load(model + "_sm") | |
| data = [] | |
| doc = nlp(text) | |
| for tok in doc: | |
| tok_data = [] | |
| for attr in attributes: | |
| tok_data.append(getattr(tok, attr)) | |
| data.append(tok_data) | |
| data = pd.DataFrame(data, columns=attributes) | |
| return data | |
| def default_token(text, attributes, model): | |
| nlp = spacy.load(model + "_sm") | |
| data = [] | |
| doc = nlp(text) | |
| for tok in doc: | |
| tok_data = [] | |
| for attr in attributes: | |
| tok_data.append(getattr(tok, attr)) | |
| data.append(tok_data) | |
| return data | |
| def random_vectors(text, model): | |
| nlp = spacy.load(model + "_md") | |
| doc = nlp(text) | |
| n_chunks = [chunk for chunk in doc.noun_chunks if doc.noun_chunks] | |
| words = [tok for tok in doc if not tok.is_stop and tok.pos_ not in [ | |
| 'PUNCT', "PROPN"]] | |
| str_list = n_chunks + words | |
| choice = random.choices(str_list, k=2) | |
| return round(choice[0].similarity(choice[1]), 2), choice[0].text, choice[1].text | |
| def vectors(input1, input2, model): | |
| nlp = spacy.load(model + "_md") | |
| return round(nlp(input1).similarity(nlp(input2)), 2) | |
| def span(text, span1, span2, label1, label2, model): | |
| nlp = spacy.load(model + "_sm") | |
| doc = nlp(text) | |
| if span1: | |
| idx1_1 = 0 | |
| idx1_2 = 0 | |
| idx2_1 = 0 | |
| idx2_2 = 0 | |
| span1 = [split for split in span1.split(" ") if split] | |
| span2 = [split for split in span2.split(" ") if split] | |
| for i in range(len(list(doc))): | |
| tok = list(doc)[i] | |
| if span1[0] == tok.text: | |
| idx1_1 = i | |
| if span1[-1] == tok.text: | |
| idx1_2 = i + 1 | |
| if span2[0] == tok.text: | |
| idx2_1 = i | |
| if span2[-1] == tok.text: | |
| idx2_2 = i + 1 | |
| doc.spans["sc"] = [ | |
| Span(doc, idx1_1, idx1_2, label1), | |
| Span(doc, idx2_1, idx2_2, label2), | |
| ] | |
| else: | |
| idx1_1 = 0 | |
| idx1_2 = round(len(list(doc)) / 2) | |
| idx2_1 = 0 | |
| idx2_2 = 1 | |
| doc.spans["sc"] = [ | |
| Span(doc, idx1_1, idx1_2, label1), | |
| Span(doc, idx2_1, idx2_2, label2), | |
| ] | |
| svg = displacy.render(doc, style="span") | |
| # download = download_svg(svg) | |
| return svg | |
| def get_text(model): | |
| for i in range(len(models)): | |
| model = model.split("_")[0] | |
| new_text = texts[model] | |
| return new_text | |
| demo = gr.Blocks(css="scrollbar.css") | |
| with demo: | |
| with gr.Box(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown("# Pipeline Visualizer") | |
| gr.Markdown( | |
| "### Visualize parts of the spaCy pipeline in an interactive demo.") | |
| with gr.Column(): | |
| gr.Image("pipeline.svg") | |
| with gr.Box(): | |
| with gr.Column(): | |
| gr.Markdown(" ## Choose a language model and text") | |
| with gr.Row(): | |
| with gr.Column(): | |
| model_input = gr.Dropdown( | |
| choices=models, value=DEFAULT_MODEL, interactive=True, label="Pretrained Pipelines") | |
| with gr.Column(): | |
| gr.Markdown("") | |
| with gr.Column(): | |
| gr.Markdown("") | |
| with gr.Column(): | |
| gr.Markdown("") | |
| with gr.Row(): | |
| with gr.Column(): | |
| text_input = gr.Textbox( | |
| value=DEFAULT_TEXT, interactive=True, label="Input Text") | |
| with gr.Column(): | |
| gr.Markdown("") | |
| button = gr.Button("Generate", variant="primary") | |
| with gr.Box(): | |
| with gr.Column(): | |
| gr.Markdown( | |
| "## [Dependency Parser](https://spacy.io/usage/visualizers#dep)") | |
| gr.Markdown( | |
| "The dependency visualizer shows part-of-speech tags and syntactic dependencies.") | |
| with gr.Row(): | |
| with gr.Column(): | |
| col_punct = gr.Checkbox( | |
| label="Collapse Punctuation", value=True) | |
| col_phrase = gr.Checkbox( | |
| label="Collapse Phrases", value=True) | |
| compact = gr.Checkbox(label="Compact", value=False) | |
| with gr.Column(): | |
| bg = gr.Textbox( | |
| label="Background Color", value=DEFAULT_COLOR) | |
| with gr.Column(): | |
| text = gr.Textbox( | |
| label="Text Color", value="black") | |
| depen_output = gr.HTML(value=dependency( | |
| DEFAULT_TEXT, True, True, False, DEFAULT_COLOR, "black", DEFAULT_MODEL)[0]) | |
| with gr.Row(): | |
| with gr.Column(): | |
| dep_button = gr.Button("Generate Dependency Parser", variant="primary") | |
| with gr.Column(): | |
| dep_download_button = gr.HTML(value=download_svg(depen_output.value)) | |
| gr.Markdown(" ") | |
| with gr.Box(): | |
| with gr.Column(): | |
| gr.Markdown( | |
| "## [Entity Recognizer](https://spacy.io/usage/visualizers#ent)") | |
| gr.Markdown( | |
| "The entity visualizer highlights named entities and their labels in a text.") | |
| ent_input = gr.CheckboxGroup( | |
| DEFAULT_ENTS, value=DEFAULT_ENTS) | |
| ent_output = gr.HTML(value=entity( | |
| DEFAULT_TEXT, DEFAULT_ENTS, DEFAULT_MODEL)) | |
| ent_button = gr.Button("Generate Entity Recognizer", variant="primary") | |
| # with gr.Row(): | |
| # with gr.Column(): | |
| # ent_button = gr.Button("Generate Entity Recognizer", variant="primary") | |
| # with gr.Column(): | |
| # ent_download_button = gr.HTML(value=download_svg(ent_output.value)) | |
| # with gr.Column(): | |
| # gr.Markdown(" ") | |
| # with gr.Column(): | |
| # gr.Markdown(" ") | |
| with gr.Box(): | |
| with gr.Column(): | |
| gr.Markdown( | |
| "## [Token Properties](https://spacy.io/usage/linguistic-features)") | |
| gr.Markdown( | |
| "When you put in raw text to spaCy, it returns a Doc object with different linguistic features") | |
| with gr.Row(): | |
| with gr.Column(): | |
| tok_input = gr.CheckboxGroup( | |
| DEFAULT_TOK_ATTR, value=DEFAULT_TOK_ATTR) | |
| with gr.Column(): | |
| gr.Markdown("") | |
| tok_output = gr.Dataframe(headers=DEFAULT_TOK_ATTR, value=default_token( | |
| DEFAULT_TEXT, DEFAULT_TOK_ATTR, DEFAULT_MODEL), overflow_row_behaviour="paginate") | |
| tok_button = gr.Button("Generate Token Properties", variant="primary") | |
| with gr.Box(): | |
| with gr.Column(): | |
| gr.Markdown( | |
| "## [Word and Phrase Similarity](https://spacy.io/usage/linguistic-features#vectors-similarity)") | |
| gr.Markdown( | |
| "Words and spans have similarity ratings based off of their word vectors, or word embeddings") | |
| with gr.Row(): | |
| with gr.Column(): | |
| sim_text1 = gr.Textbox( | |
| value="Apple", label="Word 1", interactive=True,) | |
| with gr.Column(): | |
| sim_text2 = gr.Textbox( | |
| value="U.K. startup", label="Word 2", interactive=True,) | |
| with gr.Column(): | |
| sim_output = gr.Textbox( | |
| label="Similarity Score", value="0.12") | |
| with gr.Column(): | |
| gr.Markdown("") | |
| sim_random_button = gr.Button("Generate random words") | |
| sim_button = gr.Button("Generate similarity", variant="primary") | |
| with gr.Box(): | |
| with gr.Column(): | |
| gr.Markdown( | |
| "## [Spans](https://spacy.io/usage/visualizers#span)") | |
| gr.Markdown( | |
| "The span visualizer highlights overlapping spans in a text.") | |
| with gr.Row(): | |
| with gr.Column(): | |
| span1 = gr.Textbox( | |
| label="Span 1", value="U.K. startup", placeholder="Input a part of the sentence") | |
| with gr.Column(): | |
| label1 = gr.Textbox(value="ORG", | |
| label="Label for Span 1") | |
| with gr.Column(): | |
| gr.Markdown("") | |
| with gr.Column(): | |
| gr.Markdown("") | |
| with gr.Row(): | |
| with gr.Column(): | |
| span2 = gr.Textbox( | |
| label="Span 2", value="U.K.", placeholder="Input another part of the sentence") | |
| with gr.Column(): | |
| label2 = gr.Textbox(value="GPE", | |
| label="Label for Span 2") | |
| with gr.Column(): | |
| gr.Markdown("") | |
| with gr.Column(): | |
| gr.Markdown("") | |
| span_output = gr.HTML(value=span( | |
| DEFAULT_TEXT, "U.K. startup", "U.K.", "ORG", "GPE", DEFAULT_MODEL)) | |
| span_button = gr.Button("Generate Spans", variant="primary") | |
| # with gr.Row(): | |
| # with gr.Column(): | |
| # span_button = gr.Button("Generate Spans", variant="primary") | |
| # with gr.Column(): | |
| # span_download_button = gr.HTML(value=download_svg(span_output.value)) | |
| # with gr.Column(): | |
| # gr.Markdown(" ") | |
| # with gr.Column(): | |
| # gr.Markdown(" ") | |
| model_input.change(get_text, inputs=[model_input], outputs=text_input) | |
| button.click(dependency, inputs=[ | |
| text_input, col_punct, col_phrase, compact, bg, text, model_input], outputs=[depen_output, dep_download_button]) | |
| button.click( | |
| entity, inputs=[text_input, ent_input, model_input], outputs=[ent_output]) | |
| button.click( | |
| token, inputs=[text_input, tok_input, model_input], outputs=tok_output) | |
| button.click(vectors, inputs=[sim_text1, | |
| sim_text2, model_input], outputs=sim_output) | |
| button.click( | |
| span, inputs=[text_input, span1, span2, label1, label2, model_input], outputs=[span_output]) | |
| dep_button.click(dependency, inputs=[ | |
| text_input, col_punct, col_phrase, compact, bg, text, model_input], outputs=[depen_output, dep_download_button]) | |
| ent_button.click( | |
| entity, inputs=[text_input, ent_input, model_input], outputs=[ent_output]) | |
| tok_button.click( | |
| token, inputs=[text_input, tok_input, model_input], outputs=[tok_output]) | |
| sim_button.click(vectors, inputs=[ | |
| sim_text1, sim_text2, model_input], outputs=sim_output) | |
| span_button.click( | |
| span, inputs=[text_input, span1, span2, label1, label2, model_input], outputs=[span_output]) | |
| sim_random_button.click(random_vectors, inputs=[text_input, model_input], outputs=[ | |
| sim_output, sim_text1, sim_text2]) | |
| demo.launch() | |