Update app.py

app.py

@@ -1,16 +1,20 @@
 import streamlit as st 
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.document_loaders import PyPDFLoader
-import time
+
 from transformers import T5Tokenizer, T5ForConditionalGeneration
 from transformers import pipeline
 import torch
 import base64
 
 # Model and tokenizer 
+#model_checkpoint = "LaMini-Flan-T5-248M"
 model_checkpoint = "MBZUAI/LaMini-Flan-T5-783M"
 model_tokenizer = T5Tokenizer.from_pretrained(model_checkpoint)
-model = T5ForConditionalGeneration.from_pretrained(model_checkpoint)
+model = T5ForConditionalGeneration.from_pretrained(model_checkpoint, device_map='auto', torch_dtype=torch.float32)
+
+#REPO_ID = "MBZUAI/LaMini-Flan-T5-783M"
+#model = pipeline(task='summarization', model=REPO_ID, token=access_token)
 
 # File loader and preprocessing
 def preprocess_pdf(file):
@@ -36,13 +40,37 @@ def language_model_pipeline(filepath):
     summarized_text = summary_result[0]['summary_text']
     return summarized_text
 
+@st.cache_data
+# Function to display the PDF content
+def display_pdf(file):
+    with open(file, "rb") as f:
+        base64_pdf = base64.b64encode(f.read()).decode('utf-8')
+
+    pdf_display = f'<iframe src="data:application/pdf;base64,{base64_pdf}" width="100%" height="600" type="application/pdf"></iframe>'
+    st.markdown(pdf_display, unsafe_allow_html=True)
+
+# Streamlit code 
+#st.set_page_config(layout="wide")
+
+def main():
+    st.title("Document Summarization App using Language Model")
+
+    uploaded_file = st.file_uploader("Upload your PDF file", type=['pdf'])
+
+    if uploaded_file is not None:
+        if st.button("Summarize"):
+            col1, col2 = st.columns(2)
+            filepath = "pdf/" + uploaded_file.name
+            with open(filepath, "wb") as temp_file:
+                temp_file.write(uploaded_file.read())
+            with col1:
+                st.info("Uploaded File")
+                pdf_view = display_pdf(filepath)
+
+            with col2:
+                summarized_result = language_model_pipeline(filepath)
+                st.info("Summarization Complete")
+                st.success(summarized_result)
 
-st.title("Document Summarization App using Language Model")
-uploaded_file = st.file_uploader("Upload your PDF file", type=['pdf'])
-if uploaded_file is not None:
-    if st.button("Summarize"):
-        filepath = uploaded_file.name
-        with open(filepath, "wb") as temp_file:
-            temp_file.write(uploaded_file.read())
-        summarized_result = language_model_pipeline(filepath)
-        st.success("Summarization Complete", summarized_result)
+if __name__ == "__main__":
+    main()