Update app.py

app.py

@@ -148,6 +148,6 @@ def demo_(sentence):
   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 sigmoid 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)
+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 sigmoid 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 all 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)
 
 demo.launch(share=True)