Remove main call to speed up rendering

app.py

@@ -16,76 +16,76 @@ ex_text, ex_license, ex_labels = examples_load()
 ex_long_text = example_long_text_load()
 
 
-if __name__ == '__main__':
-    st.header("Summzarization & Multi-label Classification for Long Text")
-    st.write("This app summarizes and then classifies your long text with multiple labels (_Please allow for a minimum of 30secs to load results_).")
-    st.write("Inputs: User enters their own custom text and labels")
-    st.write("Outputs: A summary of the text, pre and post summary label likelihood percentages and a downloadable csv of the results")
-
-    with st.form(key='my_form'):
-        example_text = ex_long_text #ex_text
-        display_text = "[Excerpt from Project Gutenberg: Frankenstein]\n" + example_text + "\n\n" + ex_license
-        text_input = st.text_area("Input any text you want to summaryize & classify here (keep in mind very long text will take a while to process):", display_text)
-
-        if text_input == display_text:
-            text_input = example_text
-
-        labels = st.text_input('Possible labels (comma-separated):',ex_labels, max_chars=1000)
-        labels = list(set([x.strip() for x in labels.strip().split(',') if len(x.strip()) > 0]))
-        submit_button = st.form_submit_button(label='Submit')
-
-    if submit_button:
-        if len(labels) == 0:
-            st.write('Enter some text and at least one possible topic to see predictions.')
-
-        # 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)
+# if __name__ == '__main__':
+st.header("Summzarization & Multi-label Classification for Long Text")
+st.write("This app summarizes and then classifies your long text with multiple labels (_Please allow for a minimum of 30secs to load results_).")
+st.write("Inputs: User enters their own custom text and labels")
+st.write("Outputs: A summary of the text, pre and post summary label likelihood percentages and a downloadable csv of the results")
+
+with st.form(key='my_form'):
+    example_text = ex_long_text #ex_text
+    display_text = "[Excerpt from Project Gutenberg: Frankenstein]\n" + example_text + "\n\n" + ex_license
+    text_input = st.text_area("Input any text you want to summaryize & classify here (keep in mind very long text will take a while to process):", display_text)
+
+    if text_input == display_text:
+        text_input = example_text
+
+    labels = st.text_input('Possible labels (comma-separated):',ex_labels, max_chars=1000)
+    labels = list(set([x.strip() for x in labels.strip().split(',') if len(x.strip()) > 0]))
+    submit_button = st.form_submit_button(label='Submit')
+
+if submit_button:
+    if len(labels) == 0:
+        st.write('Enter some text and at least one possible topic to see predictions.')
+
+    # 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)