Move spinner to earlier in app

app.py

@@ -31,67 +31,64 @@ with st.form(key='my_form'):
     labels = list(set([x.strip() for x in labels.strip().split(',') if len(x.strip()) > 0]))
     submit_button = st.form_submit_button(label='Submit')
 
+with st.spinner('Loading pretrained models...'):
+    summarizer = load_summary_model()   
+    classifier = load_model()
+st.success('All Done!')
 
-summarizer = load_summary_model()   
-classifier = load_model()
 
 if submit_button:
     if len(labels) == 0:
         st.write('Enter some text and at least one possible topic to see predictions.')
 
-    with st.spinner('Generating partial summaries...'):
-        # For each body of text, create text chunks of a certain token size required for the transformer
-        nested_sentences = create_nest_sentences(document = text_input, token_max_length = 1024)
-
-        summary = []
-        st.markdown("### Text Chunk & Summaries")
-        st.markdown("Breaks up the original text into sections with complete sentences totaling \
-            less than 1024 tokens, a requirement for the summarizer.")
-
-        # For each chunk of sentences (within the token max), generate a summary
-        for n in range(0, len(nested_sentences)):
-            text_chunk = " ".join(map(str, nested_sentences[n]))
-            st.markdown(f"###### Chunk {n+1}/{len(nested_sentences)}" )
-            st.markdown(text_chunk)
-
-            chunk_summary = summarizer_gen(summarizer, sequence=text_chunk, maximum_tokens = 300, minimum_tokens = 20)
-            summary.append(chunk_summary) 
-            st.markdown("###### Partial Summary")
-            st.markdown(chunk_summary)
-            # Combine all the summaries into a list and compress into one document, again
-            final_summary = " \n".join(list(summary))
-
-        # final_summary = summarizer_gen(summarizer, sequence=text_input, maximum_tokens = 30, minimum_tokens = 100)
-        st.markdown("### Combined Summary")
-        st.markdown(final_summary)
-
-    with st.spinner('Matching labels to text...'):
-        topics, scores = classifier_zero(classifier, sequence=final_summary, labels=labels, multi_class=True)
-        # st.markdown("### Top Label Predictions: Combined Summary")
-        # plot_result(topics[::-1][:], scores[::-1][:])
-        # st.markdown("### Download Data")
-        data = pd.DataFrame({'label': topics, 'scores_from_summary': scores})
-        # st.dataframe(data)
-        # coded_data = base64.b64encode(data.to_csv(index = False). encode ()).decode()
-        # st.markdown(
-        #     f'<a href="data:file/csv;base64, {coded_data}" download = "data.csv">Download Data</a>',
-        #     unsafe_allow_html = True
-        #     )
-
-        st.markdown("### Top Label Predictions: Summary & Full Text")
-        topics_ex_text, scores_ex_text = classifier_zero(classifier, sequence=example_text, labels=labels, multi_class=True)
-        plot_dual_bar_chart(topics, scores, topics_ex_text, scores_ex_text)
-
-    with st.spinner('Creating a download link...'):
-        data_ex_text = pd.DataFrame({'label': topics_ex_text, 'scores_from_full_text': scores_ex_text})
-        data2 = pd.merge(data, data_ex_text, on = ['label'])
-        st.markdown("### Data Table")
-        
-        coded_data = base64.b64encode(data2.to_csv(index = False). encode ()).decode()
-        st.markdown(
-            f'<a href="data:file/csv;base64, {coded_data}" download = "data.csv">Click here to download the data</a>',
-            unsafe_allow_html = True
-            )
-        st.dataframe(data2)
-
-    st.success('All Done!')
+    # For each body of text, create text chunks of a certain token size required for the transformer
+    nested_sentences = create_nest_sentences(document = text_input, token_max_length = 1024)
+
+    summary = []
+    st.markdown("### Text Chunk & Summaries")
+    st.markdown("Breaks up the original text into sections with complete sentences totaling \
+        less than 1024 tokens, a requirement for the summarizer.")
+
+    # For each chunk of sentences (within the token max), generate a summary
+    for n in range(0, len(nested_sentences)):
+        text_chunk = " ".join(map(str, nested_sentences[n]))
+        st.markdown(f"###### Chunk {n+1}/{len(nested_sentences)}" )
+        st.markdown(text_chunk)
+
+        chunk_summary = summarizer_gen(summarizer, sequence=text_chunk, maximum_tokens = 300, minimum_tokens = 20)
+        summary.append(chunk_summary) 
+        st.markdown("###### Partial Summary")
+        st.markdown(chunk_summary)
+        # Combine all the summaries into a list and compress into one document, again
+        final_summary = " \n".join(list(summary))
+
+    # final_summary = summarizer_gen(summarizer, sequence=text_input, maximum_tokens = 30, minimum_tokens = 100)
+    st.markdown("### Combined Summary")
+    st.markdown(final_summary)
+
+    topics, scores = classifier_zero(classifier, sequence=final_summary, labels=labels, multi_class=True)
+    # st.markdown("### Top Label Predictions: Combined Summary")
+    # plot_result(topics[::-1][:], scores[::-1][:])
+    # st.markdown("### Download Data")
+    data = pd.DataFrame({'label': topics, 'scores_from_summary': scores})
+    # st.dataframe(data)
+    # coded_data = base64.b64encode(data.to_csv(index = False). encode ()).decode()
+    # st.markdown(
+    #     f'<a href="data:file/csv;base64, {coded_data}" download = "data.csv">Download Data</a>',
+    #     unsafe_allow_html = True
+    #     )
+
+    st.markdown("### Top Label Predictions: Summary & Full Text")
+    topics_ex_text, scores_ex_text = classifier_zero(classifier, sequence=example_text, labels=labels, multi_class=True)
+    plot_dual_bar_chart(topics, scores, topics_ex_text, scores_ex_text)
+
+    data_ex_text = pd.DataFrame({'label': topics_ex_text, 'scores_from_full_text': scores_ex_text})
+    data2 = pd.merge(data, data_ex_text, on = ['label'])
+    st.markdown("### Data Table")
+    
+    coded_data = base64.b64encode(data2.to_csv(index = False). encode ()).decode()
+    st.markdown(
+        f'<a href="data:file/csv;base64, {coded_data}" download = "data.csv">Click here to download the data</a>',
+        unsafe_allow_html = True
+        )
+    st.dataframe(data2)