Update app.py

app.py

@@ -130,14 +130,15 @@ def inference(input_batch,isurl,use_archive,limit_companies=10):
     return prob_outs #ner_labels, {'E':float(prob_outs[0]),"S":float(prob_outs[1]),"G":float(prob_outs[2])},{sentiment['label']:float(sentiment['score'])},"**Summary:**\n\n" + summary
 
 title = "ESG API Demo"
-description = """This is a demonstration of the full ESG pipeline backend where given a URL (english, news) the news contents are extracted, using extractnet, and fed to three models:
+description = """This is a demonstration of the full ESG pipeline backend where given a list of URL (english, news) the news contents are extracted, using extractnet, and fed to three models:
 
 - An off-the-shelf sentiment classification model (ProsusAI/finbert)
 - A custom NER for the company extraction
 - A custom ESG classifier for the ESG labeling of the news (the extracted text is also lemmatised prior to be fed to this classifier) 
 
 API input parameters:
-- URL: text. Url of the news (english)
+- List: list of text. Either list of Url of the news (english) or list of extracted news contents
+- 'Data type': int. 0=list is of extracted news contents, 1=list is of urls.
 - `use_archive`: boolean. The model will extract the archived version in archive.org of the url indicated. This is useful with old news and to bypass news behind paywall
 - `limit_companies`: integer. Number of found relevant companies to report.