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import streamlit as st
import os
from utils.pdf_utils import PDFProcessor
from utils.embeddings_utils import EmbeddingsManager
from utils.qa_utils import QASystem
from dotenv import load_dotenv
import openai
def initialize_session_state():
if 'pdf_processor' not in st.session_state:
st.session_state['pdf_processor'] = None
if 'embeddings_manager' not in st.session_state:
st.session_state['embeddings_manager'] = None
if 'qa_system' not in st.session_state:
st.session_state['qa_system'] = None
if 'processed_pdfs' not in st.session_state:
st.session_state['processed_pdfs'] = set()
if 'all_text_chunks' not in st.session_state:
st.session_state['all_text_chunks'] = []
def main():
load_dotenv()
st.set_page_config(page_title="AI-Powered PDF Assistant", layout="wide")
initialize_session_state()
# Header Section
st.markdown(
"""
<style>
.main-header {
font-size: 2.5rem;
color: #1F77B4;
text-align: center;
margin-bottom: 1rem;
}
.sub-header {
font-size: 1.25rem;
color: #555;
text-align: center;
margin-bottom: 2rem;
}
</style>
<div class="main-header">📘 AI-Powered PDF Assistant</div>
<div class="sub-header">Upload, Analyze, and Interact with Your Documents</div>
""",
unsafe_allow_html=True
)
# Navigation Menu
selected_page = st.sidebar.radio(
"Navigate", ["Upload PDFs", "Ask Questions", "About"]
)
api_key = os.getenv("OPENAI_API_KEY")
if not api_key:
st.sidebar.error("OpenAI API key not found in .env file!")
return
openai.api_key = api_key
if not st.session_state['pdf_processor']:
st.session_state['pdf_processor'] = PDFProcessor()
if not st.session_state['embeddings_manager']:
st.session_state['embeddings_manager'] = EmbeddingsManager(api_key)
if not st.session_state['qa_system']:
st.session_state['qa_system'] = QASystem(api_key)
if selected_page == "Upload PDFs":
st.header("📤 Upload PDFs")
st.markdown(
"""<p style='font-size: 1.1rem;'>Drag and drop your PDF files below to extract and process content for analysis.</p>""",
unsafe_allow_html=True
)
uploaded_files = st.file_uploader(
"Upload PDF files", type=['pdf'], accept_multiple_files=True
)
if uploaded_files:
new_files = [f for f in uploaded_files if f.name not in st.session_state['processed_pdfs']]
if new_files:
with st.spinner("Processing PDFs..."):
for pdf_file in new_files:
try:
pages = st.session_state['pdf_processor'].extract_text(pdf_file)
for page_text in pages.values():
chunks = st.session_state['pdf_processor'].chunk_text(page_text)
st.session_state['all_text_chunks'].extend(chunks)
st.session_state['processed_pdfs'].add(pdf_file.name)
except Exception as e:
st.error(f"Error processing {pdf_file.name}: {str(e)}")
continue
with st.spinner("Generating embeddings..."):
try:
st.session_state['embeddings_manager'].generate_embeddings(
st.session_state['all_text_chunks']
)
st.success("✅ Documents processed successfully!")
except Exception as e:
st.error(f"Error generating embeddings: {str(e)}")
elif selected_page == "Ask Questions":
st.header("❓ Ask Questions")
st.markdown(
"""<p style='font-size: 1.1rem;'>Query your uploaded documents and get precise answers backed by AI-powered analysis.</p>""",
unsafe_allow_html=True
)
if st.session_state['all_text_chunks']:
question = st.text_input("Enter your question:")
if question:
try:
with st.spinner("Finding relevant information..."):
relevant_chunks = st.session_state['embeddings_manager'].find_relevant_chunks(
question, k=3
)
answer = st.session_state['qa_system'].generate_answer(
question, relevant_chunks
)
st.markdown("### 🤖 Answer")
st.write(answer)
with st.expander("🔍 View Source Context"):
for i, chunk in enumerate(relevant_chunks, 1):
st.markdown(f"**Context {i}:**")
st.write(chunk)
st.markdown("---")
except openai.error.RateLimitError:
st.error("Rate limit exceeded. Please try again later.")
except Exception as e:
st.error(f"Error: {str(e)}")
else:
st.warning("Please upload and process documents in the 'Upload PDFs' section first.")
elif selected_page == "About":
st.header("ℹ️ About This App")
st.markdown(
"""
<p style='font-size: 1.1rem;'>
<b>AI-Powered PDF Assistant</b> is a smart solution for extracting and querying information from PDF files. With powerful AI integrations,
this tool allows seamless document analysis and interaction.
</p>
<h3>🔑 Key Features</h3>
<ul>
<li>Upload and process multiple PDF files</li>
<li>Generate embeddings for precise content retrieval</li>
<li>Query documents and receive context-aware answers</li>
</ul>
<h3>🛠️ Technologies Used</h3>
<ul>
<li>Streamlit for interactive UI</li>
<li>OpenAI GPT API for Q&A</li>
<li>Custom PDF processing and embedding tools</li>
</ul>
<p style='text-align: center;'>
Built with ❤️ by [Your Name]
</p>
""",
unsafe_allow_html=True
)
if __name__ == "__main__":
main()
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