update to gradio v3.4

app.py

@@ -1,27 +1,28 @@
+import gradio as gr
 import spacy
 from spacy import displacy
-import random
 from spacy.tokens import Span
-import gradio as gr
 import pandas as pd
 import base64
+import random
 
 
 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;"
+DEFAULT_COLOR = "linear-gradient(90deg, #FFCA74, #7AECEC)"
+DEFAULT_ENTS = ['CARDINAL', 'DATE', 'EVENT', 'FAC', 'GPE', 'LANGUAGE', 'LAW', 'LOC', 'MONEY',
+                'NORP', 'ORDINAL', 'ORG', 'PERCENT', 'PERSON', 'PRODUCT', 'QUANTITY', 'TIME', 'WORK_OF_ART']
+DEFAULT_TOK_ATTR = ['idx', 'text', 'pos_', 'lemma_', 'shape_', 'dep_']
 NOUN_ATTR = ['text', 'root.text', 'root.dep_', 'root.head.text']
 
+
+
+
 # get the huggingface models specified in the requirements.txt file
 def get_all_models():
     with open("requirements.txt") as f:
@@ -93,30 +94,6 @@ def default_token(text, attributes, model):
         data.append(tok_data)
     return data, model_name
 
-# returns noun chunks in text
-def noun_chunks(text, model):
-    model_name = model + "_sm"
-    nlp = spacy.load(model_name)
-    data = []
-    doc = nlp(text)
-    for chunk in doc.noun_chunks:
-        data.append([chunk.text, chunk.root.text, chunk.root.dep_,
-            chunk.root.head.text])
-    data = pd.DataFrame(data, columns=NOUN_ATTR)
-    return data, model_name
-
-# returns noun chuncks for the default value
-# the return value is not a pandas DataFrame
-def default_noun_chunks(text, model):
-    model_name = model + "_sm"
-    nlp = spacy.load(model_name)
-    data = []
-    doc = nlp(text)
-    for chunk in doc.noun_chunks:
-        data.append([chunk.text, chunk.root.text, chunk.root.dep_,
-            chunk.root.head.text])
-    return data, model_name
-
 # Get similarity of two random generated vectors
 def random_vectors(text, model):
     model_name = model + "_md"
@@ -178,6 +155,30 @@ def span(text, span1, span2, label1, label2, model):
     svg = displacy.render(doc, style="span")
     return svg, model_name
 
+# returns noun chunks in text
+def noun_chunks(text, model):
+    model_name = model + "_sm"
+    nlp = spacy.load(model_name)
+    data = []
+    doc = nlp(text)
+    for chunk in doc.noun_chunks:
+        data.append([chunk.text, chunk.root.text, chunk.root.dep_,
+            chunk.root.head.text])
+    data = pd.DataFrame(data, columns=NOUN_ATTR)
+    return data, model_name
+
+# returns noun chuncks for the default value
+# the return value is not a pandas DataFrame
+def default_noun_chunks(text, model):
+    model_name = model + "_sm"
+    nlp = spacy.load(model_name)
+    data = []
+    doc = nlp(text)
+    for chunk in doc.noun_chunks:
+        data.append([chunk.text, chunk.root.text, chunk.root.dep_,
+            chunk.root.head.text])
+    return data, model_name
+
 # get default text based on language model
 def get_text(model):
     for i in range(len(models)):
@@ -200,37 +201,27 @@ with demo:
         with gr.Column():
             gr.Markdown(" ## Choose a language model and the inputted text")
             with gr.Row():
-                with gr.Column():
+                with gr.Column(scale=0.25):
                     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():
+                with gr.Column(scale=0.5):
                     text_input = gr.Textbox(
                         value=DEFAULT_TEXT, interactive=True, label="Input Text")
-                with gr.Column():
-                    gr.Markdown("")
-            button = gr.Button("Update", variant="primary")
+            with gr.Row():
+                with gr.Column(scale=0.25):
+                    button = gr.Button("Update", variant="primary").style(full_width=False)
     with gr.Box():
         with gr.Column():
             with gr.Row():
-                with gr.Column():
+                with gr.Column(scale=0.75):
                     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.Column():
-                    with gr.Row():
-                        with gr.Column():
-                            gr.Markdown(" ")
-                        with gr.Column():
-                            dep_model = gr.Textbox(
-                                label="Model", value="en_core_web_sm")
+                with gr.Column(scale=0.25):
+                    dep_model = gr.Textbox(
+                        label="Model", value="en_core_web_sm")
             with gr.Row():
                 with gr.Column():
                     col_punct = gr.Checkbox(
@@ -244,168 +235,145 @@ with demo:
                 with gr.Column():
                     text = gr.Textbox(
                         label="Text Color", value="black")
-
-            dep_output = gr.HTML(value=dependency(
-                DEFAULT_TEXT, True, True, False, DEFAULT_COLOR, "black", DEFAULT_MODEL)[0])
             with gr.Row():
-                with gr.Column():
+                dep_output = gr.HTML(value=dependency(
+                    DEFAULT_TEXT, True, True, False, DEFAULT_COLOR, "black", DEFAULT_MODEL)[0])
+            with gr.Row():
+                with gr.Column(scale=0.25):
                     dep_button = gr.Button(
-                        "Update Dependency Parser", variant="primary")
+                        "Update Dependency Parser", variant="primary").style(full_width=False)
                 with gr.Column():
                     dep_download_button = gr.HTML(
                         value=download_svg(dep_output.value))
-                gr.Markdown(" ")
     with gr.Box():
         with gr.Column():
             with gr.Row():
-                with gr.Column():
+                with gr.Column(scale=0.75):
                     gr.Markdown(
                         "## [🔗 Entity Recognizer](https://spacy.io/usage/visualizers#ent)")
                     gr.Markdown(
                         "The entity visualizer highlights named entities and their labels in a text")
-                with gr.Column():
-                    with gr.Row():
-                        with gr.Column():
-                            gr.Markdown(" ")
-                        with gr.Column():
-                            ent_model = gr.Textbox(
-                                label="Model", value="en_core_web_sm")
+                with gr.Column(scale=0.25):
+                        ent_model = gr.Textbox(
+                            label="Model", value="en_core_web_sm")
             ent_input = gr.CheckboxGroup(
-                DEFAULT_ENTS, value=DEFAULT_ENTS)
+                DEFAULT_ENTS, value=DEFAULT_ENTS, label="Entity Types")
             ent_output = gr.HTML(value=entity(
                 DEFAULT_TEXT, DEFAULT_ENTS, DEFAULT_MODEL)[0])
-            ent_button = gr.Button(
-                "Update Entity Recognizer", variant="primary")
+            with gr.Row():
+                with gr.Column(scale=0.25):
+                    ent_button = gr.Button(
+                        "Update Entity Recognizer", variant="primary")
     with gr.Box():
         with gr.Column():
             with gr.Row():
-                with gr.Column():
+                with gr.Column(scale=0.75):
                     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():
-                    with gr.Row():
-                        with gr.Column():
-                            gr.Markdown(" ")
-                        with gr.Column():
-                            tok_model = gr.Textbox(
-                                label="Model", value="en_core_web_sm")
+                with gr.Column(scale=0.25):
+                        tok_model = gr.Textbox(
+                                label="Model", value="en_core_web_sm")               
             with gr.Row():
-                with gr.Column():
+                with gr.Column(scale=0.5):
                     tok_input = gr.CheckboxGroup(
-                        DEFAULT_TOK_ATTR, value=DEFAULT_TOK_ATTR)
-                with gr.Column():
-                    gr.Markdown("")
+                        DEFAULT_TOK_ATTR, value=DEFAULT_TOK_ATTR, label="Token Attributes", interactive=True)
             tok_output = gr.Dataframe(headers=DEFAULT_TOK_ATTR, value=default_token(
                 DEFAULT_TEXT, DEFAULT_TOK_ATTR, DEFAULT_MODEL)[0], overflow_row_behaviour="paginate")
-            tok_button = gr.Button(
-                "Update Token Properties", variant="primary")
-    with gr.Box():
-        with gr.Column():
             with gr.Row():
-                with gr.Column():
-                    gr.Markdown(
-                        "## [🔗 Noun chunks](https://spacy.io/usage/linguistic-features#noun-chunks)")
-                    gr.Markdown(
-                        "You can use `doc.noun_chunks` to extract noun phrases from a doc object")
-                with gr.Column():
-                    with gr.Row():
-                        with gr.Column():
-                            gr.Markdown(" ")
-                        with gr.Column():
-                            noun_model = gr.Textbox(
-                                label="Model", value="en_core_web_sm")
-            noun_output = gr.Dataframe(headers=NOUN_ATTR, value=default_noun_chunks(
-                DEFAULT_TEXT, DEFAULT_MODEL)[0], overflow_row_behaviour="paginate")
-            noun_button = gr.Button(
-                "Update Noun Chunks", variant="primary")
+                with gr.Column(scale=0.25):
+                    tok_button = gr.Button(
+                        "Update Token Properties", variant="primary")
     with gr.Box():
         with gr.Column():
             with gr.Row():
-                with gr.Column():
+                with gr.Column(scale=0.75):
                     gr.Markdown(
                         "## [🔗 Word and Phrase Similarity](https://spacy.io/usage/linguistic-features#vectors-similarity)")
                     gr.Markdown(
                         "Words and spans have similarity ratings based on their word vectors")
-                with gr.Column():
-                    with gr.Row():
-                        with gr.Column():
-                            gr.Markdown(" ")
-                        with gr.Column():
-                            sim_model = gr.Textbox(
-                                label="Model", value="en_core_web_md")
+                with gr.Column(scale=0.25):
+                        sim_model = gr.Textbox(
+                            label="Model", value="en_core_web_md")             
             with gr.Row():
-                with gr.Column():
+                with gr.Column(scale=0.25):
                     sim_text1 = gr.Textbox(
                         value="Apple", label="Word 1", interactive=True,)
-                with gr.Column():
+                with gr.Column(scale=0.25):
                     sim_text2 = gr.Textbox(
                         value="U.K. startup", label="Word 2", interactive=True,)
-                with gr.Column():
+                with gr.Column(scale=0.25):
                     sim_output = gr.Textbox(
                         label="Similarity Score", value="0.12")
-                with gr.Column():
-                    gr.Markdown("")
-            sim_random_button = gr.Button("Update random words")
-            sim_button = gr.Button("Update similarity", variant="primary")       
+            with gr.Row():
+                with gr.Column(scale=0.25):
+                    sim_random_button = gr.Button("Update random words")
+                with gr.Column(scale=0.25):
+                    sim_button = gr.Button("Update similarity", variant="primary")
     with gr.Box():
         with gr.Column():
             with gr.Row():
-                with gr.Column():
+                with gr.Column(scale=0.75):
                     gr.Markdown(
                         "## [🔗 Spans](https://spacy.io/usage/visualizers#span)")
                     gr.Markdown(
                         "The span visualizer highlights overlapping spans in a text")
-                with gr.Column():
-                    with gr.Row():
-                        with gr.Column():
-                            gr.Markdown(" ")
-                        with gr.Column():
-                            span_model = gr.Textbox(
+                with gr.Column(scale=0.25):
+                        span_model = gr.Textbox(
                                 label="Model", value="en_core_web_sm")
             with gr.Row():
-                with gr.Column():
+                with gr.Column(scale=0.3):
                     span1 = gr.Textbox(
                         label="Span 1", value="U.K. startup", placeholder="Input a part of the sentence")
-                with gr.Column():
+                with gr.Column(scale=0.3):
                     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():
+                with gr.Column(scale=0.3):
                     span2 = gr.Textbox(
                         label="Span 2", value="U.K.", placeholder="Input another part of the sentence")
-                with gr.Column():
+                with gr.Column(scale=0.3):
                     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)[0])
-            span_button = gr.Button("Update Spans", variant="primary")
+            with gr.Row():
+                with gr.Column(scale=0.25):
+                    span_button = gr.Button("Update Spans", variant="primary")
+    with gr.Box():
+        with gr.Column():
+            with gr.Row():
+                with gr.Column(scale=0.75):
+                    gr.Markdown(
+                        "## [🔗 Noun chunks](https://spacy.io/usage/linguistic-features#noun-chunks)")
+                    gr.Markdown(
+                        "You can use `doc.noun_chunks` to extract noun phrases from a doc object")
+                with gr.Column(scale=0.25):
+                        noun_model = gr.Textbox(
+                                label="Model", value="en_core_web_sm") 
+            noun_output = gr.Dataframe(headers=NOUN_ATTR, value=default_noun_chunks(
+                DEFAULT_TEXT, DEFAULT_MODEL)[0], overflow_row_behaviour="paginate")
+            with gr.Row():
+                with gr.Column(scale=0.25):
+                    noun_button = gr.Button(
+                        "Update Noun Chunks", variant="primary")
 
     # change text based on model input
     model_input.change(get_text, inputs=[model_input], outputs=text_input)
-
     # main button - update all components
     button.click(dependency, inputs=[
         text_input, col_punct, col_phrase, compact, bg, text, model_input], outputs=[dep_output, dep_download_button, dep_model])
     button.click(
         entity, inputs=[text_input, ent_input, model_input], outputs=[ent_output, ent_model])
-    button.click(
-        noun_chunks, inputs=[text_input, model_input], outputs=[noun_output, noun_model])
     button.click(
         token, inputs=[text_input, tok_input, model_input], outputs=[tok_output, tok_model])
     button.click(vectors, inputs=[sim_text1,
                  sim_text2, model_input], outputs=[sim_output, sim_model])
     button.click(
         span, inputs=[text_input, span1, span2, label1, label2, model_input], outputs=[span_output, span_model])
+    button.click(
+        noun_chunks, inputs=[text_input, model_input], outputs=[noun_output, noun_model])
     
     # individual component buttons
     dep_button.click(dependency, inputs=[
@@ -414,13 +382,13 @@ with demo:
         entity, inputs=[text_input, ent_input, model_input], outputs=[ent_output, ent_model])
     tok_button.click(
         token, inputs=[text_input, tok_input, model_input], outputs=[tok_output, tok_model])
-    noun_button.click(
-        noun_chunks, inputs=[text_input, model_input], outputs=[noun_output, noun_model])
     sim_button.click(vectors, inputs=[
                      sim_text1, sim_text2, model_input], outputs=[sim_output, sim_model])
-    span_button.click(
-        span, inputs=[text_input, span1, span2, label1, label2, model_input], outputs=[span_output, span_model])
     sim_random_button.click(random_vectors, inputs=[text_input, model_input], outputs=[
                             sim_output, sim_text1, sim_text2, sim_model])
-
-demo.launch()
+    span_button.click(
+        span, inputs=[text_input, span1, span2, label1, label2, model_input], outputs=[span_output, span_model])
+    noun_button.click(
+        noun_chunks, inputs=[text_input, model_input], outputs=[noun_output, noun_model])
+      
+demo.launch()

requirements.txt

@@ -1,5 +1,5 @@
 pandas==1.4.2
-gradio==3.0.18
+gradio==3.4.0
 spacy==3.4.0
 
 https://huggingface.co/spacy/ca_core_news_md/resolve/main/ca_core_news_md-any-py3-none-any.whl