Spaces:
Runtime error
Runtime error
File size: 2,761 Bytes
63337f5 7d0a6ff 11ef280 63337f5 6f9cc9b 7de3632 078b2c2 7de3632 078b2c2 6f9cc9b 63337f5 6f9cc9b 7de3632 63337f5 7de3632 63337f5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 |
import streamlit as st
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import PyPDFLoader
from transformers import T5Tokenizer, T5ForConditionalGeneration
from transformers import pipeline
import torch
import base64
#import os
#from dotenv import load_dotenv
#from huggingface_hub import HfApi
#api = HfApi()
#token = api.retrieve_token("secret_token") # Replace with your secret name
#load_dotenv()
#token = os.environ.get("HF_TOKEN")
checkpoint = "MBZUAI/LaMini-Flan-T5-248M"
#model and tokenizer loading
tokenizer = T5Tokenizer.from_pretrained(checkpoint)
base_model = T5ForConditionalGeneration.from_pretrained(checkpoint, device_map='auto', torch_dtype=torch.float32, offload_folder='D:/project/offload')
#file loader and preprocessing
def file_preprocessing(file):
loader = PyPDFLoader(file)
pages = loader.load_and_split()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=50)
texts = text_splitter.split_documents(pages)
final_texts = ""
for text in texts:
print(text)
final_texts = final_texts + text.page_content
return final_texts
#LLM pipeline
def llm_pipeline(filepath):
pipe_sum = pipeline(
'summarization',
model = base_model,
tokenizer = tokenizer,
max_length = 500,
min_length = 50)
input_text = file_preprocessing(filepath)
result = pipe_sum(input_text)
result = result[0]['summary_text']
return result
@st.cache_data
#function to display the PDF of a given file
def displayPDF(file):
# Opening file from file path
with open(file, "rb") as f:
base64_pdf = base64.b64encode(f.read()).decode('utf-8')
# Embedding PDF in HTML
pdf_display = F'<iframe src="data:application/pdf;base64,{base64_pdf}" width="100%" height="600" type="application/pdf"></iframe>'
# Displaying File
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 = "data/"+uploaded_file.name
with open(filepath, "wb") as temp_file:
temp_file.write(uploaded_file.read())
with col1:
st.info("Uploaded File")
pdf_view = displayPDF(filepath)
with col2:
summary = llm_pipeline(filepath)
st.info("Summarization Complete")
st.success(summary)
if __name__ == "__main__":
main() |