Update app.py

app.py

@@ -3,6 +3,9 @@ import PyPDF2
 from PIL import Image
 import streamlit as st 
 
+model = T5ForConditionalGeneration.from_pretrained("t5-base")
+tokenizer = T5TokenizerFast.from_pretrained("t5-base")
+
 def read_pdf(pdf):
     reader=PyPDF2.PdfReader(pdf)
     text=''
@@ -15,9 +18,10 @@ def read_pdf(pdf):
     return text
 
 
-def summarizer(text):
-    model = T5ForConditionalGeneration.from_pretrained("t5-base")
-    tokenizer = T5TokenizerFast.from_pretrained("t5-base")
+def summarizer(pdf):
+    # model = T5ForConditionalGeneration.from_pretrained("t5-base")
+    # tokenizer = T5TokenizerFast.from_pretrained("t5-base")
+    text=read_pdf(pdf)
     inputs = tokenizer.encode("summarize: " + text,return_tensors="pt", max_length=1000,truncation=True)
     outputs = model.generate(inputs,max_length=1000, min_length=100,length_penalty=2.0, num_beams=4,early_stopping=True)
     summary = tokenizer.decode(outputs[0])
@@ -31,11 +35,8 @@ uploaded_file = st.file_uploader('Choose your .pdf file', type="pdf")
 if uploaded_file is not None:
     if st.button('Summarize Document'):
         with st.spinner("📚    Please wait while we produce a summary..."):
-            try:
-                text=read_pdf(uploaded_file)
-                summary=summarizer(text)
-                st.divider()
-                st.markdown(summary, unsafe_allow_html=True)
-                st.divider()
-            except Exception as e:
-                pass
+            # text=read_pdf(uploaded_file)
+            summary=summarizer(uploaded_file)
+            st.divider()
+            st.markdown(summary, unsafe_allow_html=True)
+            st.divider()