prettier?

app.py

@@ -16,6 +16,7 @@ texts = {"en": DEFAULT_TEXT, "ca": "Apple està buscant comprar una startup del
          "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。"}
 
+
 def get_all_models():
     with open("requirements.txt") as f:
         content = f.readlines()
@@ -35,7 +36,7 @@ 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}
+               "collapse_punct": col_punct, "bg": bg, "color": font}
     html = displacy.render(doc, style="dep", options=options)
     return html
 
@@ -60,6 +61,7 @@ def token(text, attributes, model):
     data = pd.DataFrame(data, columns=attributes)
     return data
 
+
 def default_token(text, attributes, model):
     nlp = spacy.load(model + "_sm")
     data = []
@@ -154,8 +156,7 @@ with demo:
                     gr.Markdown("")
                 with gr.Column():
                     gr.Markdown("")
-            
-            
+
             with gr.Row():
                 with gr.Column():
                     text_input = gr.Textbox(
@@ -163,89 +164,161 @@ with demo:
                 with gr.Column():
                     gr.Markdown("")
             button = gr.Button("Generate", variant="primary")
-    with gr.Column(): 
+    with gr.Column():
         with gr.Tabs():
             with gr.TabItem(""):
                 with gr.Column():
-                    gr.Markdown("## [Dependency Parser](https://spacy.io/usage/visualizers#dep)")
+                    gr.Markdown(
+                        "## [Dependency Parser](https://spacy.io/usage/visualizers#dep)")
+                    gr.Markdown(
+                        "The dependency visualizer, `dep`, shows part-of-speech tags and syntactic dependencies.")
                     with gr.Row():
                         with gr.Column():
                             gr.Markdown("""```python
                                         import spacy
                                         from spacy import displacy
 
+                                        nlp = spacy.load("en_core_web_sm")
+                                        doc = nlp(text)
                                         displacy.serve(doc, style="dep")
                                         ```
                                         """)
-                       
                         with gr.Column():
-                            col_punct = gr.Checkbox(label="Collapse Punctuation", value=True)
-                            col_phrase = gr.Checkbox(label="Collapse Phrases", value=True)
+                            gr.Markdown("")
+                        with gr.Column():
+                            gr.Markdown("")
+                    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)
+                            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))
+                            text = gr.Textbox(
+                                label="Text Color", value="black")
+                    depen_output = gr.HTML(value=dependency(
+                        DEFAULT_TEXT, True, True, False, DEFAULT_COLOR, "black", DEFAULT_MODEL))
                     dep_button = gr.Button("Generate Dependency Parser")
                     gr.Markdown("\n\n\n")
         with gr.Box():
             with gr.Column():
-                gr.Markdown("## [Entity Recognizer](https://spacy.io/usage/visualizers#ent)")
-                with gr.Column():
-                        with gr.Row():
-                            gr.Markdown("""```python
+                gr.Markdown(
+                    "## [Entity Recognizer](https://spacy.io/usage/visualizers#ent)")
+                gr.Markdown(
+                        "The entity visualizer, `ent`, highlights named entities and their labels in a text.")
+                with gr.Row():
+                    with gr.Column():
+                        gr.Markdown("""```python
                                         import spacy
                                         from spacy import displacy
-
+                                        
+                                        nlp = spacy.load("en_core_web_sm")
+                                        doc = nlp(text)
                                         displacy.serve(doc, style="ent")
                                         ```
                                         """)
-                        with gr.Row():
-                            gr.Markdown("")
-                        with gr.Row():
-                            gr.Markdown("")
-                entity_input = gr.CheckboxGroup(DEFAULT_ENTS, value=DEFAULT_ENTS)
-                entity_output = gr.HTML(value=entity(DEFAULT_TEXT, DEFAULT_ENTS, DEFAULT_MODEL))
+                    with gr.Column():
+                        gr.Markdown("")
+                    with gr.Column():
+                        gr.Markdown("")
+                entity_input = gr.CheckboxGroup(
+                    DEFAULT_ENTS, value=DEFAULT_ENTS)
+                entity_output = gr.HTML(value=entity(
+                    DEFAULT_TEXT, DEFAULT_ENTS, DEFAULT_MODEL))
                 ent_button = gr.Button("Generate Entity Recognizer")
         with gr.Box():
             with gr.Column():
-                gr.Markdown("## [Token Properties](https://spacy.io/usage/linguistic-features)")
+                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.Column():
-                    tok_input = gr.CheckboxGroup(
-                        DEFAULT_TOK_ATTR, value=DEFAULT_TOK_ATTR)
-                    tok_output = gr.Dataframe(headers=DEFAULT_TOK_ATTR, value=default_token(DEFAULT_TEXT, DEFAULT_TOK_ATTR, DEFAULT_MODEL), overflow_row_behaviour="paginate")
+                    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")
         with gr.Box():
             with gr.Column():
-                gr.Markdown("## [Word and Phrase Similarity](https://spacy.io/usage/linguistic-features#vectors-similarity)")
+                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")
+                gr.Markdown(">Word embeddings are multi-dimensional meaning representations of a word.")
                 with gr.Row():
-                    sim_text1 = gr.Textbox(
-                        value="Apple", label="Word 1", interactive=True,)
-                    sim_text2 = gr.Textbox(
-                        value="U.K. startup", label="Word 2", interactive=True,)
-                    sim_output = gr.Textbox(label="Similarity Score", value="0.12")
+                    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")
         with gr.Box():
             with gr.Column():
-                gr.Markdown("## [Spans](https://spacy.io/usage/visualizers#span)")
+                gr.Markdown(
+                    "## [Spans](https://spacy.io/usage/visualizers#span)")
+                gr.Markdown("The span visualizer, `span`, highlights overlapping spans in a text.")
+                with gr.Row():
+                    with gr.Column():
+                        gr.Markdown("""```python
+                                        import spacy
+                                        from spacy import displacy
+                                        from spacy.tokens import Span
+
+                                        nlp = spacy.load("en_core_web_sm")
+                                        doc = nlp(text)
+                                        doc.spans["sc"] = [
+                                            Span(doc, 6, 8, "ORG")
+                                            Span(doc, 6, 7, "GPE")
+                                            ]
+                                        displacy.serve(doc, style="span")
+                                        ```
+                                        """)
+                    with gr.Column():
+                        gr.Markdown("")
+                    with gr.Column():
+                        gr.Markdown("")
                 with gr.Column():
                     with gr.Row():
-                        span1 = gr.Textbox(
-                            label="Span 1", value="U.K. startup", placeholder="Input a part of the sentence")
-                        label1 = gr.Textbox(value="ORG",
-                                            label="Label for Span 1")
+                        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():
-                        span2 = gr.Textbox(
-                            label="Span 2", value="U.K.", placeholder="Input another part of the sentence")
-                        label2 = gr.Textbox(value="GPE",
-                                            label="Label for Span 2")
-                    span_output = gr.HTML(value=span(DEFAULT_TEXT, "U.K. startup", "U.K.", "ORG", "GPE", DEFAULT_MODEL))
+                        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))
                     gr.Markdown(value="\n\n\n\n")
                     gr.Markdown(value="\n\n\n\n")
                     span_button = gr.Button("Generate spans")
-    
+
     text_button.click(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)