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app.py

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+import streamlit as st
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.document_loaders import PyPDFLoader
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+from transformers import pipeline
+import torch
+import base64
+import time
+from PIL import Image
+
+st.image("https://huggingface.co/spaces/wiwaaw/summary/resolve/main/banner.png")
+
+#MODEL AND TOKENIZER
+model_checkpoint = "MBZUAI/LaMini-Flan-T5-783M"
+model_tokenizer = T5Tokenizer.from_pretrained(model_checkpoint)
+model = T5ForConditionalGeneration.from_pretrained(model_checkpoint)
+
+#FILE LOADER AND PREPROCESSING
+def preprocess_pdf(file):
+    loader = PyPDFLoader(file)
+    pages = loader.load_and_split()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=170, chunk_overlap=70)
+    texts = text_splitter.split_documents(pages)
+    final_text = ""
+    for text in texts:
+        final_text = final_text + text.page_content
+    return final_text
+
+@st.cache_data
+#LLM PIPELINE
+def language_model_pipeline(filepath):
+    summarization_pipeline = pipeline(
+        'summarization',
+        model = model,
+        tokenizer = model_tokenizer,
+        max_length = 500,
+        min_length = 32
+    )
+    input_text = preprocess_pdf(filepath)
+    summary_result = summarization_pipeline(input_text)
+    summarized_text = summary_result[0]['summary_text']
+    return summarized_text
+
+title = st.title("PDF Summarization using LaMini")
+uploaded_file = st.file_uploader('Upload your PDF file', type=['pdf'])
+if uploaded_file is not None:
+    st.success("File Uploaded")
+    if st.button ("Summarize"):
+        time.sleep(10)
+        
+        filepath = uploaded_file.name
+        with open(filepath, "wb") as temp_file:
+            temp_file.write(uploaded_file.read())
+        
+        summarized_result = language_model_pipeline(filepath)
+        st.info("Summarization Complete")
+        st.success(summarized_result)