Update app.py

app.py

@@ -156,14 +156,17 @@ def inference(input_batch,isurl,use_archive,limit_companies=10):
     print("[i] Batch size:",len(input_batch_content))
     print("[i] Running ESG classifier inference...")
     prob_outs = _inference_classifier(input_batch_content)
+    print("[i] Classifier output shape:",prob_outs.shape)
     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 )
+    print("[i] Sentiment output:",sentiment )
     #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']
+    print("[i] Pandas output shape:",df.shape)
 
     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