Spaces:

vivek9
/

CS772_Assignment2

Sleeping

App Files Files Community

vivek9 commited on Apr 1, 2024

Commit

b3ad09c

verified ·

1 Parent(s): bb141ab

Update app.py

Browse files

Files changed (1) hide show

app.py +1 -1

app.py CHANGED Viewed

@@ -143,7 +143,7 @@ def demo_(sentence):
   preds3=predict_for_example(sentence=sentence, tags=tags, model=model3)
   preds2=predict_for_example(sentence=sentence, tags=tags, model=model2)
   preds4=predict_for_example(sentence=sentence, tags=tags, model=model4)
-  return "predicted labels:\t"str(preds2)+"\n"+"predicted Noun chunks \t"str(get_noun_chunks(sentence=sentence, tags=tags,preds=preds2)),"predicted labels:\t"str(preds4)+"\n"+"predicted Noun chunks \t"str(get_noun_chunks(sentence=sentence, tags=tags,preds=preds4)),"predicted labels:\t"str(preds1)+"\n"+"predicted Noun chunks \t"str(get_noun_chunks(sentence=sentence, tags=tags,preds=preds1)),"predicted labels:\t"str(preds3)+"\n"+"predicted Noun chunks \t"str(get_noun_chunks(sentence=sentence, tags=tags,preds=preds3)),tags
 title="POS-Tagged Corpus Analysis: Training a Recurrent Perceptron for Noun Chunk Identification"
 demo = gr.Interface(fn=demo_, inputs=gr.Textbox(label="sentence for which you want noun chunks",lines=1, interactive=True, show_copy_button=True), outputs=[gr.Textbox(label="prediction on conditioned data with step activation function",lines=2, interactive=True, show_copy_button=True),gr.Textbox(label="prediction on conditioned data with step activation function",lines=2, interactive=True, show_copy_button=True),gr.Textbox(label="prediction on all data with step activation function",lines=2, interactive=True, show_copy_button=True),gr.Textbox(label="prediction on whole data with sigmoid activation function",lines=2, interactive=True, show_copy_button=True),gr.Textbox(label="pos tag label given by nltk library",lines=1, interactive=True, show_copy_button=True)],title=title)

   preds3=predict_for_example(sentence=sentence, tags=tags, model=model3)
   preds2=predict_for_example(sentence=sentence, tags=tags, model=model2)
   preds4=predict_for_example(sentence=sentence, tags=tags, model=model4)
+  return "predicted labels:\t"+str(preds2)+"\n"+"predicted Noun chunks \t"+str(get_noun_chunks(sentence=sentence, tags=tags,preds=preds2)),"predicted labels:\t"+str(preds4)+"\n"+"predicted Noun chunks \t"+str(get_noun_chunks(sentence=sentence, tags=tags,preds=preds4)),"predicted labels:\t"+str(preds1)+"\n"+"predicted Noun chunks \t"+str(get_noun_chunks(sentence=sentence, tags=tags,preds=preds1)),"predicted labels:\t"+str(preds3)+"\n"+"predicted Noun chunks \t"+str(get_noun_chunks(sentence=sentence, tags=tags,preds=preds3)),tags
 title="POS-Tagged Corpus Analysis: Training a Recurrent Perceptron for Noun Chunk Identification"
 demo = gr.Interface(fn=demo_, inputs=gr.Textbox(label="sentence for which you want noun chunks",lines=1, interactive=True, show_copy_button=True), outputs=[gr.Textbox(label="prediction on conditioned data with step activation function",lines=2, interactive=True, show_copy_button=True),gr.Textbox(label="prediction on conditioned data with step activation function",lines=2, interactive=True, show_copy_button=True),gr.Textbox(label="prediction on all data with step activation function",lines=2, interactive=True, show_copy_button=True),gr.Textbox(label="prediction on whole data with sigmoid activation function",lines=2, interactive=True, show_copy_button=True),gr.Textbox(label="pos tag label given by nltk library",lines=1, interactive=True, show_copy_button=True)],title=title)