Update app.py

app.py

@@ -29,52 +29,48 @@ st.title('Paper2Slides')
 
 st.subheader('Upload paper in pdf format')
 
-# col1, col2 = st.columns([3, 1])
-# with col1:
-#     uploaded_file = st.file_uploader("Choose a file")
-# with col2:
-#     option = st.selectbox(
-#         'Select parsing method.',
-#         ('monkey', 'x2d', 'lxml'))
-
-# if uploaded_file is not None:
-    
-#     st.write(uploaded_file.name)
-#     bytes_data = uploaded_file.getvalue()
-#     st.write(len(bytes_data), "bytes")
+col1, col2 = st.columns([3, 1])
+with col1:
+    uploaded_file = st.file_uploader("Choose a file")
+with col2:
+    option = st.selectbox(
+        'Select parsing method.',
+        ('monkey', 'x2d', 'lxml'))
+
+if uploaded_file is not None:
     
-#     saved_file_path = save_uploaded_file(uploaded_file)
-#     monkeyReader = reader.MonkeyReader(option)
-#     outline = monkeyReader.readOutline(saved_file_path)
-#     for pre, fill, node in outline:
-#         st.write("%s%s" % (pre, node.name))
+    st.write(uploaded_file.name)
+    bytes_data = uploaded_file.getvalue()
+    st.write(len(bytes_data), "bytes")
     
+    saved_file_path = save_uploaded_file(uploaded_file)
+    monkeyReader = reader.MonkeyReader(option)
     
-#     # read paper content
-#     essay = monkeyReader.readEssay(saved_file_path)
+    # read paper content
+    essay = monkeyReader.readEssay(saved_file_path)
         
-    # with st.status("Understanding paper..."):
+    with st.status("Understanding paper..."):
         
-    #     Barttokenizer = BartTokenizer.from_pretrained('facebook/bart-large-cnn')
-    #     summ_model_path = 'com3dian/Bart-large-paper2slides-summarizer'
-    #     summarizor = BartForConditionalGeneration.from_pretrained(summ_model_path)
-    #     exp_model_path = 'com3dian/Bart-large-paper2slides-expander'
-    #     expandor = BartForConditionalGeneration.from_pretrained(exp_model_path)
-    #     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    #     BartSE = BARTAutoEncoder(summarizor, summarizor, device)
-    #     del summarizor, expandor
+        Barttokenizer = BartTokenizer.from_pretrained('facebook/bart-large-cnn')
+        summ_model_path = 'com3dian/Bart-large-paper2slides-summarizer'
+        summarizor = BartForConditionalGeneration.from_pretrained(summ_model_path)
+        exp_model_path = 'com3dian/Bart-large-paper2slides-expander'
+        expandor = BartForConditionalGeneration.from_pretrained(exp_model_path)
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        BartSE = BARTAutoEncoder(summarizor, summarizor, device)
+        del summarizor, expandor
             
-    #     document = Document(essay, Barttokenizer)
-    #     del Barttokenizer
-    #     length = document.merge(25, 30, BartSE, device)
-
-    # with st.status("Generating slides..."):
-    #     summarizor = pipeline("summarization", model=summ_model_path, device = device)
-    #     summ_text = summarizor(document.segmentation['text'], max_length=100, min_length=10, do_sample=False)
-    #     summ_text = [text['summary_text'] for text in summ_text]
+        document = Document(essay, Barttokenizer)
+        del Barttokenizer
+        length = document.merge(25, 30, BartSE, device)
+
+    with st.status("Generating slides..."):
+        summarizor = pipeline("summarization", model=summ_model_path, device = device)
+        summ_text = summarizor(document.segmentation['text'], max_length=100, min_length=10, do_sample=False)
+        summ_text = [text['summary_text'] for text in summ_text]
         
-    #     for summ in summ_text:
-    #         st.write(summ)
+        for summ in summ_text:
+            st.write(summ)
 
 
 with open('slides_text.pkl', 'rb') as file: