Update app.py

app.py

@@ -4,6 +4,14 @@ import numpy as np
 import os
 from grobidmonkey import reader
 
+from transformers import pipeline
+from transformers import BartTokenizer, BartModel, BartForConditionalGeneration
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+from document import Document
+from BartSE import BARTAutoEncoder
+
+
 def save_uploaded_file(uploaded_file):
     file_path = os.path.join("./uploads", uploaded_file.name)
     os.makedirs("./uploads", exist_ok=True)  # Create 'uploads' directory if it doesn't exist
@@ -40,5 +48,21 @@ if uploaded_file is not None:
     essay = monkeyReader.readEssay(saved_file_path)
     for key, values in essay.items():
         st.write(f"{key}: {', '.join(values)}")
-    
+
+    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(article, Barttokenizer)
+    del Barttokenizer
+    length = document.merge(10, 30, BartSE, device)
+
+    summarizor = pipeline("summarization", model=summ_model_path, device = 0)
+    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]