fix: edits

app.py

@@ -61,6 +61,17 @@ 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 = []
+    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")
@@ -131,55 +142,59 @@ def get_text(model):
 demo = gr.Blocks()
 
 with demo:
-    model_input = gr.Dropdown(
-        choices=models, value=DEFAULT_MODEL, interactive=True, label="Pretrained Pipelines")
-    text_button = gr.Button("Get new text")
-    text_input = gr.Textbox(
-        value=DEFAULT_TEXT, interactive=True, label="Input Text")
-    button = gr.Button("Generate")
-    with gr.Tabs():
-        with gr.TabItem("Dependency"):
-            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)
-            depen_output = gr.HTML()
-            dep_button = gr.Button("Generate this tab")
-        with gr.TabItem("Entity"):
-            entity_input = gr.CheckboxGroup(DEFAULT_ENTS, value=DEFAULT_ENTS)
-            entity_output = gr.HTML()
-            ent_button = gr.Button("Generate this tab")
-        with gr.TabItem("Tokens"):
-            with gr.Column():
-                tok_input = gr.CheckboxGroup(
-                    DEFAULT_TOK_ATTR, value=DEFAULT_TOK_ATTR)
-                tok_output = gr.Dataframe(
-                    headers=DEFAULT_TOK_ATTR, overflow_row_behaviour="paginate")
-            tok_button = gr.Button("Generate this tab")
-        with gr.TabItem("Similarity"):
+    with gr.Box():
+        with gr.Row():
+            with gr.Row():
+                gr.Markdown("Chose a language model")
+                model_input = gr.Dropdown(
+                    choices=models, value=DEFAULT_MODEL, interactive=True, label="Pretrained Pipelines")
+                text_button = gr.Button("Get text in new language")
+            with gr.Row():
+                text_input = gr.Textbox(
+                value=DEFAULT_TEXT, interactive=True, label="Input Text")
+        button = gr.Button("Generate", variant="primary")
+    with gr.Column():
+        gr.Markdown("Dependency Parser")
+        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)
+        depen_output = gr.HTML(value=dependency(DEFAULT_TEXT, True, True, False, DEFAULT_MODEL))
+        dep_button = gr.Button("Generate Dependency Parser")
+        gr.Markdown("Entity Recognizer")
+        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")
+        gr.Markdown("Token Properties")
+        with gr.Column():
+            tok_input = gr.CheckboxGroup(
+                DEFAULT_TOK_ATTR, value=DEFAULT_TOK_ATTR)
+            tok_output = gr.Dataframe(value=default_token(DEFAULT_TEXT, DEFAULT_TOK_ATTR, DEFAULT_MODEL),overflow_row_behaviour="paginate")
+        tok_button = gr.Button("Generate Token Properties")
+        gr.Markdown("Word and Phrase Similarity")
+        with gr.Row():
+            sim_text1 = gr.Textbox(
+                value="Apple", label="Chosen", interactive=True,)
+            sim_text2 = gr.Textbox(
+                value="U.K. startup", label="Chosen", interactive=True,)
+        sim_output = gr.Textbox(label="Similarity Score", value="0.12")
+        sim_random_button = gr.Button("Generate random words")
+        sim_button = gr.Button("Generate similarity")
+        gr.Markdown("Spans")
+        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.Row():
-                sim_text1 = gr.Textbox(
-                    value="Apple", label="Chosen", interactive=True,)
-                sim_text2 = gr.Textbox(
-                    value="U.K. startup", label="Chosen", interactive=True,)
-            sim_output = gr.Textbox(label="Similarity Score")
-            sim_random_button = gr.Button("Generate random words")
-            sim_button = gr.Button("Generate inputs")
-        with gr.TabItem("Spans"):
-            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.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()
-                gr.Markdown(value="\n\n\n\n")
-                gr.Markdown(value="\n\n\n\n")
-                span_button = gr.Button("Generate this tab")
+                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))
+            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, model_input], outputs=depen_output)