Update app.py

app.py

@@ -11,6 +11,9 @@ import spacy
 import os
 from transformers import pipeline
 import itertools
+import pandas as pd
+
+OUT_HEADERS = ['E','S','G']
 
 MODEL_TRANSFORMER_BASED = "distilbert-base-uncased"
 MODEL_ONNX_FNAME = "ESG_classifier_batch.onnx"
@@ -160,10 +163,10 @@ examples = [[[['https://www.bbc.com/news/uk-62732447'],
             ['https://www.bbc.com/news/science-environment-62680423']],'url',False,5]]
 demo = gr.Interface(fn=inference, 
                     inputs=[gr.Dataframe(label='input batch', col_count=1, datatype='str', type='array', wrap=True),
-                            gr.Dropdown(label='data type', choices=['text','url'], type='index'),
+                            gr.Dropdown(label='data type', choices=['text','url'], type='index', value='url'),
                             gr.Checkbox(label='if url parse cached in archive.org'),
-                            gr.Slider(minimum=1, maximum=10, step=1, label='Limit NER output')],
-                    outputs=[gr.Dataframe(label='output raw', col_count=1, datatype='number', type='array', wrap=True)],
+                            gr.Slider(minimum=1, maximum=10, step=1, label='Limit NER output'), value=5],
+                    outputs=[gr.Dataframe(label='output raw', col_count=1, datatype='number', type='array', wrap=True, header=OUT_HEADERS)],
                              #gr.Label(label='Company'),
                              #gr.Label(label='ESG'),
                              #gr.Label(label='Sentiment'),