added spinners to make it less boring

app.py

@@ -90,31 +90,34 @@ if task == 'Questions/Answers Generation':
         sent_tokenized = nltk.sent_tokenize(inputs)
         res = {}
 
-        # Iterate over sentences
-        for sentence in sent_tokenized:
-            predictions = model.multitask([sentence], max_length=512)
-            questions, answers, answers_bis = predictions['questions'], predictions['answers'], predictions[
-                'answers_bis']
-
-            # Build answer dict
-            content = {}
-            for question, answer, answer_bis in zip(questions[0], answers[0], answers_bis[0]):
-                content[question] = {'answer (extracted)': answer, 'answer (generated)': answer_bis}
-            res[sentence] = content
+        with st.spinner('Please wait while the inputs are being processed...'):
+            # Iterate over sentences
+            for sentence in sent_tokenized:
+                predictions = model.multitask([sentence], max_length=512)
+                questions, answers, answers_bis = predictions['questions'], predictions['answers'], predictions[
+                    'answers_bis']
+
+                # Build answer dict
+                content = {}
+                for question, answer, answer_bis in zip(questions[0], answers[0], answers_bis[0]):
+                    content[question] = {'answer (extracted)': answer, 'answer (generated)': answer_bis}
+                res[sentence] = content
 
         # Answer area
         st.write(res)
 
     else:
-        # Prediction
-        predictions = model.multitask([inputs], max_length=512)
-        questions, answers, answers_bis = predictions['questions'], predictions['answers'], predictions['answers_bis']
+        with st.spinner('Please wait while the inputs are being processed...'):
+            # Prediction
+            predictions = model.multitask([inputs], max_length=512)
+            questions, answers, answers_bis = predictions['questions'], predictions['answers'], predictions['answers_bis']
+
+            # Answer area
+            zip = zip(questions[0], answers[0], answers_bis[0])
+            content = {}
+            for question, answer, answer_bis in zip:
+                content[question] = {'answer (extracted)': answer, 'answer (generated)': answer_bis}
 
-        # Answer area
-        zip = zip(questions[0], answers[0], answers_bis[0])
-        content = {}
-        for question, answer, answer_bis in zip:
-            content[question] = {'answer (extracted)': answer, 'answer (generated)': answer_bis}
         st.write(content)
 
 elif task == 'Question Answering':
@@ -136,8 +139,9 @@ elif task == 'Question Answering':
     question = st.text_input('Question:', value="What forced Bohemond to retreat from his campaign? ")
 
     # Prediction
-    predictions = model.qa([{'question': question, 'context': inputs}], max_length=512)
-    answer = {question: predictions[0]}
+    with st.spinner('Please wait while the inputs are being processed...'):
+        predictions = model.qa([{'question': question, 'context': inputs}], max_length=512)
+        answer = {question: predictions[0]}
 
     # Answer area
     st.write(answer)
@@ -173,7 +177,8 @@ elif task == 'Question Generation':
         answers.append(inputs[hl_index[i]: hl_index[i + 1] + 4].replace('<hl>', '').strip())
 
     # Prediction
-    predictions = model.qg(contexts, max_length=512)
+    with st.spinner('Please wait while the inputs are being processed...'):
+        predictions = model.qg(contexts, max_length=512)
 
     # Answer area
     content = {}