Victoria Slocum commited on
Commit
c8d67a4
1 Parent(s): 96daa57

fix: token and sim update

Browse files
Files changed (2) hide show
  1. .gitignore +1 -0
  2. app.py +30 -19
.gitignore ADDED
@@ -0,0 +1 @@
 
 
1
+ venv
app.py CHANGED
@@ -60,7 +60,7 @@ def token(text, attributes, model):
60
  return data
61
 
62
 
63
- def vectors(text, model):
64
  nlp = spacy.load(model + "_md")
65
  doc = nlp(text)
66
  n_chunks = [chunk for chunk in doc.noun_chunks]
@@ -71,6 +71,11 @@ def vectors(text, model):
71
  return round(choice[0].similarity(choice[1]), 2), choice[0].text, choice[1].text
72
 
73
 
 
 
 
 
 
74
  def span(text, span1, span2, label1, label2, model):
75
  nlp = spacy.load(model + "_sm")
76
  doc = nlp(text)
@@ -125,9 +130,10 @@ demo = gr.Blocks()
125
 
126
  with demo:
127
  model_input = gr.Dropdown(
128
- choices=models, value=DEFAULT_MODEL, interactive=True)
129
  text_button = gr.Button("Get new text")
130
- text_input = gr.Textbox(value=DEFAULT_TEXT, interactive=True)
 
131
  button = gr.Button("Generate")
132
  with gr.Tabs():
133
  with gr.TabItem("Dependency"):
@@ -144,24 +150,28 @@ with demo:
144
  with gr.Column():
145
  tok_input = gr.CheckboxGroup(
146
  DEFAULT_TOK_ATTR, value=DEFAULT_TOK_ATTR)
147
- tok_output = gr.Dataframe(
148
- headers=DEFAULT_TOK_ATTR, overflow_row_behaviour="paginate")
149
  tok_button = gr.Button("Generate this tab")
150
  with gr.TabItem("Similarity"):
151
  with gr.Row():
152
- sim_text1 = gr.Textbox(value="David Bowie", label="Chosen")
153
- sim_text2 = gr.Textbox(value="the US", label="Chosen")
154
- sim_output = gr.Textbox(value="0.09", label="Similarity Score")
155
- sim_button = gr.Button("Generate this tab")
 
 
 
156
  with gr.TabItem("Spans"):
157
  with gr.Column():
158
  with gr.Row():
159
- span1 = gr.Textbox(label="Span 1")
160
- label1 = gr.Textbox(value="Label 1",
 
161
  label="Label for Span 1")
162
  with gr.Row():
163
- span2 = gr.Textbox(label="Span 2")
164
- label2 = gr.Textbox(value="Label 2",
 
165
  label="Label for Span 2")
166
  span_output = gr.HTML()
167
  gr.Markdown(value="\n\n\n\n")
@@ -174,8 +184,8 @@ with demo:
174
  entity, inputs=[text_input, entity_input, model_input], outputs=entity_output)
175
  button.click(
176
  token, inputs=[text_input, tok_input, model_input], outputs=tok_output)
177
- button.click(vectors, inputs=[text_input, model_input], outputs=[
178
- sim_output, sim_text1, sim_text2])
179
  button.click(
180
  span, inputs=[text_input, span1, span2, label1, label2, model_input], outputs=span_output)
181
  dep_button.click(dependency, inputs=[
@@ -183,10 +193,11 @@ with demo:
183
  ent_button.click(
184
  entity, inputs=[text_input, entity_input, model_input], outputs=entity_output)
185
  tok_button.click(
186
- token, inputs=[text_input, tok_input, model_input], outputs=tok_output)
187
- sim_button.click(vectors, inputs=[text_input, model_input], outputs=[
188
- sim_output, sim_text1, sim_text2])
189
  span_button.click(
190
  span, inputs=[text_input, span1, span2, label1, label2, model_input], outputs=span_output)
191
-
 
192
  demo.launch()
 
60
  return data
61
 
62
 
63
+ def random_vectors(text, model):
64
  nlp = spacy.load(model + "_md")
65
  doc = nlp(text)
66
  n_chunks = [chunk for chunk in doc.noun_chunks]
 
71
  return round(choice[0].similarity(choice[1]), 2), choice[0].text, choice[1].text
72
 
73
 
74
+ def vectors(input1, input2, model):
75
+ nlp = spacy.load(model + "_md")
76
+ return round(nlp(input1).similarity(nlp(input2)), 2)
77
+
78
+
79
  def span(text, span1, span2, label1, label2, model):
80
  nlp = spacy.load(model + "_sm")
81
  doc = nlp(text)
 
130
 
131
  with demo:
132
  model_input = gr.Dropdown(
133
+ choices=models, value=DEFAULT_MODEL, interactive=True, label="Pretrained Pipelines")
134
  text_button = gr.Button("Get new text")
135
+ text_input = gr.Textbox(
136
+ value=DEFAULT_TEXT, interactive=True, label="Input Text")
137
  button = gr.Button("Generate")
138
  with gr.Tabs():
139
  with gr.TabItem("Dependency"):
 
150
  with gr.Column():
151
  tok_input = gr.CheckboxGroup(
152
  DEFAULT_TOK_ATTR, value=DEFAULT_TOK_ATTR)
153
+ tok_output = gr.Dataframe(overflow_row_behaviour="paginate")
 
154
  tok_button = gr.Button("Generate this tab")
155
  with gr.TabItem("Similarity"):
156
  with gr.Row():
157
+ sim_text1 = gr.Textbox(
158
+ value="Apple", label="Chosen", interactive=True,)
159
+ sim_text2 = gr.Textbox(
160
+ value="U.K. startup", label="Chosen", interactive=True,)
161
+ sim_output = gr.Textbox(label="Similarity Score")
162
+ sim_random_button = gr.Button("Generate random words")
163
+ sim_button = gr.Button("Generate inputs")
164
  with gr.TabItem("Spans"):
165
  with gr.Column():
166
  with gr.Row():
167
+ span1 = gr.Textbox(
168
+ label="Span 1", value="U.K. startup", placeholder="Input a part of the sentence")
169
+ label1 = gr.Textbox(value="ORG",
170
  label="Label for Span 1")
171
  with gr.Row():
172
+ span2 = gr.Textbox(
173
+ label="Span 2", value="U.K.", placeholder="Input another part of the sentence")
174
+ label2 = gr.Textbox(value="GPE",
175
  label="Label for Span 2")
176
  span_output = gr.HTML()
177
  gr.Markdown(value="\n\n\n\n")
 
184
  entity, inputs=[text_input, entity_input, model_input], outputs=entity_output)
185
  button.click(
186
  token, inputs=[text_input, tok_input, model_input], outputs=tok_output)
187
+ button.click(vectors, inputs=[sim_text1,
188
+ sim_text2, model_input], outputs=sim_output)
189
  button.click(
190
  span, inputs=[text_input, span1, span2, label1, label2, model_input], outputs=span_output)
191
  dep_button.click(dependency, inputs=[
 
193
  ent_button.click(
194
  entity, inputs=[text_input, entity_input, model_input], outputs=entity_output)
195
  tok_button.click(
196
+ token, inputs=[text_input, tok_input, model_input], outputs=[tok_output])
197
+ sim_button.click(vectors, inputs=[
198
+ sim_text1, sim_text2, model_input], outputs=sim_output)
199
  span_button.click(
200
  span, inputs=[text_input, span1, span2, label1, label2, model_input], outputs=span_output)
201
+ sim_random_button.click(random_vectors, inputs=[text_input, model_input], outputs=[
202
+ sim_output, sim_text1, sim_text2])
203
  demo.launch()