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AI Notes Summarizer - Main Application
A Streamlit web application for summarizing PDF files and text content using AI.
"""
import streamlit as st
import os
from pathlib import Path
# Import custom modules
from modules.pdf_processor import PDFProcessor
from modules.text_summarizer import TextSummarizer
from modules.utils import setup_logging, validate_input, display_summary_stats, format_file_size
# Initialize components
@st.cache_resource
def initialize_components():
"""Initialize PDF processor and text summarizer"""
pdf_processor = PDFProcessor()
text_summarizer = TextSummarizer()
return pdf_processor, text_summarizer
def main():
"""Main application function"""
st.set_page_config(
page_title="AI Notes Summarizer",
page_icon="📝",
layout="wide",
initial_sidebar_state="expanded"
)
# Initialize components
pdf_processor, text_summarizer = initialize_components()
# App header
st.title("📝 AI Notes Summarizer")
st.markdown("Transform your lengthy documents and notes into concise, bullet-point summaries using AI.")
# Sidebar for options
st.sidebar.header("⚙️ Settings")
# Model selection
model_options = {
"BART (Recommended)": "facebook/bart-large-cnn",
"T5 Small": "t5-small",
"DistilBART": "sshleifer/distilbart-cnn-12-6"
}
selected_model = st.sidebar.selectbox(
"Choose AI Model:",
options=list(model_options.keys()),
index=0,
help="BART is recommended for best quality summaries"
)
# Update text summarizer model if changed
if text_summarizer.model_name != model_options[selected_model]:
text_summarizer.model_name = model_options[selected_model]
text_summarizer.summarizer = None # Reset to reload model
# Summary length options
summary_length = st.sidebar.select_slider(
"Summary Length:",
options=["Short", "Medium", "Long"],
value="Medium",
help="Choose the desired length of the summary"
)
# Update summary length settings
length_settings = {
"Short": (30, 150),
"Medium": (50, 300),
"Long": (100, 500)
}
text_summarizer.min_summary_length, text_summarizer.max_summary_length = length_settings[summary_length]
# Main content area
tab1, tab2 = st.tabs(["📄 PDF Upload", "📝 Text Input"])
with tab1:
st.header("Upload PDF File")
st.markdown("Upload a PDF file to extract and summarize its content.")
uploaded_file = st.file_uploader(
"Choose a PDF file",
type=['pdf'],
help="Upload a PDF file (max 10MB)"
)
if uploaded_file is not None:
# Display file info
file_size = format_file_size(uploaded_file.size)
st.info(f"📄 **File:** {uploaded_file.name} ({file_size})")
# Process PDF button
if st.button("📖 Extract & Summarize PDF", type="primary"):
with st.spinner("Processing PDF file..."):
# Extract text from PDF
extracted_text = pdf_processor.process_pdf(uploaded_file)
if extracted_text:
st.success("✅ Text extracted successfully!")
# Show extracted text preview
with st.expander("📝 View Extracted Text (Preview)"):
st.text_area(
"Extracted Content:",
value=extracted_text[:1000] + "..." if len(extracted_text) > 1000 else extracted_text,
height=200,
disabled=True
)
# Generate summary
summary = text_summarizer.summarize_text(extracted_text)
if summary:
st.success("✅ Summary generated successfully!")
# Display summary
st.subheader("📋 Summary")
st.markdown(summary)
# Display statistics
st.subheader("📊 Statistics")
display_summary_stats(extracted_text, summary)
# Download option
st.download_button(
label="💾 Download Summary",
data=summary,
file_name=f"{uploaded_file.name}_summary.txt",
mime="text/plain"
)
with tab2:
st.header("Direct Text Input")
st.markdown("Paste your text content directly for summarization.")
text_input = st.text_area(
"Enter your text here:",
height=300,
placeholder="Paste your text content here...",
help="Minimum 100 characters required for effective summarization"
)
# Character count
char_count = len(text_input)
st.caption(f"Characters: {char_count:,}")
if st.button("🚀 Summarize Text", type="primary"):
if validate_input(text_input, min_length=100):
# Generate summary
summary = text_summarizer.summarize_text(text_input)
if summary:
st.success("✅ Summary generated successfully!")
# Display summary
st.subheader("📋 Summary")
st.markdown(summary)
# Display statistics
st.subheader("📊 Statistics")
display_summary_stats(text_input, summary)
# Download option
st.download_button(
label="💾 Download Summary",
data=summary,
file_name="text_summary.txt",
mime="text/plain"
)
# Footer
st.markdown("---")
st.markdown(
"""
<div style='text-align: center; color: #666;'>
<p>AI Notes Summarizer | Powered by Hugging Face Transformers</p>
</div>
""",
unsafe_allow_html=True
)
if __name__ == "__main__":
main()
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