Spaces:
Sleeping
Sleeping
File size: 2,977 Bytes
f2c8e06 cadce87 f2c8e06 cadce87 f2c8e06 cadce87 f2c8e06 cadce87 432a8d9 cadce87 432a8d9 cadce87 f2c8e06 cadce87 432a8d9 cadce87 f2c8e06 9dca518 cadce87 f2c8e06 432a8d9 f2c8e06 cadce87 f2c8e06 cadce87 f2c8e06 cadce87 f2c8e06 cadce87 f2c8e06 cadce87 f2c8e06 cadce87 432a8d9 cadce87 432a8d9 cadce87 f2c8e06 cadce87 |
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 92 93 |
import os
import base64
import tempfile
import streamlit as st
from transformers import pipeline
from PyPDF2 import PdfReader
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# Load the summarization model
tokenizer = AutoTokenizer.from_pretrained("MBZUAI/LaMini-Flan-T5-248M")
base_model = AutoModelForSeq2SeqLM.from_pretrained("MBZUAI/LaMini-Flan-T5-248M")
# Function to extract text from a PDF using PyPDF2
def extract_text_from_pdf(pdf_path):
reader = PdfReader(pdf_path)
text = ""
for page in reader.pages:
text += page.extract_text() # Extract text from each page
if not text.strip():
raise ValueError("The PDF file contains no extractable text.")
return text
# LLM pipeline for summarization
def llm_pipeline(input_text):
pipe_sum = pipeline(
'summarization',
model=base_model,
tokenizer=tokenizer,
max_length=500,
min_length=50,
)
result = pipe_sum(input_text)
return result[0]['summary_text']
@st.cache_data
# Function to display the PDF
def displayPDF(file_path):
with open(file_path, "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 App
def main():
st.title('Content Summarizer')
# PDF Upload Section
uploaded_file = st.file_uploader("Upload your PDF file", type=['pdf'])
if uploaded_file is not None:
if st.button("Summarize PDF"):
col1, col2 = st.columns(2)
# Save the uploaded file to a temporary location
with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf", dir="/tmp/") as temp_file:
temp_file.write(uploaded_file.read())
temp_filepath = temp_file.name
with col1:
st.info("Uploaded PDF File")
displayPDF(temp_filepath)
with col2:
st.info("Summarization")
input_text = extract_text_from_pdf(temp_filepath)
if input_text: # Proceed only if text extraction was successful
summary = llm_pipeline(input_text)
st.success(summary)
# Text Input Section
st.header("Summarize Your Text")
user_input = st.text_area("Enter your content here:", height=200)
if st.button("Summarize Text"):
if user_input.strip():
col1, col2 = st.columns(2)
with col1:
st.info("Original Content")
st.write(user_input)
with col2:
st.info("Summarization")
summary = llm_pipeline(user_input)
st.success(summary)
else:
st.warning("Please enter some content to summarize.")
if __name__ == '__main__':
main()
|