Update app.py

app.py

@@ -154,14 +154,18 @@ def inference(input_batch,isurl,use_archive,limit_companies=10):
             input_batch_content = input_batch_r
     
     print("[i] Batch size:",len(input_batch_content))
-    
+    print("[i] Running ESG classifier inference...")
     prob_outs = _inference_classifier(input_batch_content)
+    print("[i] Running sentiment using",MODEL_SENTIMENT_ANALYSIS ,"inference...")
     #sentiment = _inference_sentiment_model_via_api_query({"inputs": extracted['content']})
-    #sentiment = _inference_sentiment_model_pipeline(input_batch_content )[0]
+    sentiment = _inference_sentiment_model_pipeline(input_batch_content )[0]
     #summary = _inference_summary_model_pipeline(input_batch_content )[0]['generated_text']
     #ner_labels = _inference_ner_spancat(input_batch_content ,summary, penalty = 0.8, limit_outputs=limit_companies)
+    df = pd.DataFrame(prob_outs,columns =['E','S','G'])
+    df['sent_lbl'] = sentiment['label']
+    df['sent_score'] = sentiment['score']
 
-    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
+    return df #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 list of URL (english, news) the news contents are extracted, using extractnet, and fed to three models:
@@ -191,7 +195,7 @@ demo = gr.Interface(fn=inference,
                             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', value=5)],
-                    outputs=[gr.Dataframe(label='output raw', col_count=1, datatype='number', type='array', wrap=True)],#, header=OUT_HEADERS)],
+                    outputs=[gr.Dataframe(label='output raw', col_count=1, type='pandas', wrap=True, header=OUT_HEADERS)],
                              #gr.Label(label='Company'),
                              #gr.Label(label='ESG'),
                              #gr.Label(label='Sentiment'),