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import streamlit as st
from PyPDF2 import PdfReader
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np
from transformers import pipeline
# Page config
st.set_page_config(
page_title="PDF RAG Chatbot",
page_icon="📚",
layout="wide"
)
# Initialize session state
if 'processed' not in st.session_state:
st.session_state.processed = False
if 'chunks' not in st.session_state:
st.session_state.chunks = []
if 'index' not in st.session_state:
st.session_state.index = None
if 'embeddings_model' not in st.session_state:
st.session_state.embeddings_model = None
if 'qa_model' not in st.session_state:
st.session_state.qa_model = None
# to extract text from pdf file using pdfReader from pypdf2
def extract_text_from_pdf(pdf_file):
pdf_reader = PdfReader(pdf_file)
text = ""
for page in pdf_reader.pages:
text += page.extract_text()
return text
# splitting Extracted text into small small chunks with operlaping text
def split_text_into_chunks(text, chunk_size=1000, overlap=200):
chunks = []
start = 0
while start < len(text):
end = start + chunk_size
chunk = text[start:end]
if chunk.strip(): # Only add non-empty chunks
chunks.append(chunk)
start += chunk_size - overlap
return chunks
# feed chunks to model to encode and return embeddings
def create_embeddings(chunks, model):
embeddings = model.encode(chunks, show_progress_bar=True)
return embeddings
# Index embeddings into FAISS local index
def create_faiss_index(embeddings):
dimension = embeddings.shape[1]
index = faiss.IndexFlatL2(dimension)
index.add(embeddings.astype('float32'))
return index
# Search for similar chunks using FAISS
def search_similar_chunks(query, model, index, chunks, k=3):
query_embedding = model.encode([query])
distances, indices = index.search(query_embedding.astype('float32'), k)
return [chunks[i] for i in indices[0]]
# Generate answer using Open Source Model google/flan-t5-base
def generate_answer(question, context, qa_model):
max_context_length = 2000 # Combine context (limit to avoid token limits)
if len(context) > max_context_length:
context = context[:max_context_length]
input_text = f"Context: {context}\n\nQuestion: {question}\n\nAnswer:"
# Generate answer
result = qa_model(input_text, max_length=200, min_length=20, do_sample=False)
answer = result[0]['generated_text']
if "Answer:" in answer:
answer = answer.split("Answer:")[-1].strip()
return answer
# Main User Interface webpage
st.title("📚 PDF-Based RAG Chatbot")
st.markdown("Upload two PDF documents and ask questions about their content!")
st.markdown("**100% Free** - Uses open-source models from Hugging Face")
with st.sidebar:
st.header("📄 Upload PDFs")
pdf1 = st.file_uploader("Upload PDF 1", type=['pdf'], key="pdf1")
pdf2 = st.file_uploader("Upload PDF 2", type=['pdf'], key="pdf2")
st.markdown("---")
if st.button("🔄 Process PDFs", type="primary"):
if not pdf1 or not pdf2:
st.error("Please upload both PDF files!")
else:
with st.spinner("Processing PDFs... This may take a minute on first run."):
try:
# Extract text from both PDFs
st.info("📖 Reading PDFs...")
text1 = extract_text_from_pdf(pdf1)
text2 = extract_text_from_pdf(pdf2)
combined_text = text1 + "\n\n" + text2
# Split into chunks
st.info("✂️ Splitting text into chunks...")
chunks = split_text_into_chunks(combined_text)
st.session_state.chunks = chunks
# Load embedding model
if st.session_state.embeddings_model is None:
st.info("🔧 Loading embedding model...")
st.session_state.embeddings_model = SentenceTransformer('all-MiniLM-L6-v2')
# Create embeddings
st.info("🔍 Creating embeddings...")
embeddings = create_embeddings(chunks, st.session_state.embeddings_model)
# Create FAISS index
st.info("📊 Building search index...")
st.session_state.index = create_faiss_index(embeddings)
# Load QA model
if st.session_state.qa_model is None:
st.info("🤖 Loading question-answering model...")
st.session_state.qa_model = pipeline(
"text2text-generation",
model="google/flan-t5-base"
)
st.session_state.processed = True
st.success(f"✅ Successfully processed {len(chunks)} chunks from both PDFs!")
except Exception as e:
st.error(f"Error: {str(e)}")
if st.session_state.processed:
st.success("✅ PDFs are ready!")
st.info(f"📦 Total chunks: {len(st.session_state.chunks)}")
st.markdown("---")
st.markdown("""
### 🛠️ Tech Stack:
- **Streamlit**: UI
- **PyPDF2**: PDF reading
- **Sentence Transformers**: Embeddings
- **FAISS**: Vector search
- **google/flan-t5-base**: Answer generation
All models run locally - no API keys needed!
""")
# Main content area
if st.session_state.processed:
st.markdown("### 💬 Ask Questions")
question = st.text_input(
"Enter your question:",
placeholder="What are the main topics in these documents?"
)
col1, col2 = st.columns([1, 4])
with col1:
ask_button = st.button("🔍 Get Answer", type="primary")
if ask_button:
if not question:
st.warning("Please enter a question!")
else:
with st.spinner("Searching documents and generating answer..."):
try:
# Search for relevant chunks
relevant_chunks = search_similar_chunks(
question,
st.session_state.embeddings_model,
st.session_state.index,
st.session_state.chunks,
k=3
)
# Combine chunks as context
context = "\n\n".join(relevant_chunks)
# Generate answer
answer = generate_answer(question, context, st.session_state.qa_model)
# Display answer
st.markdown("### 📝 Answer:")
st.success(answer)
# Show relevant chunks
with st.expander("📄 View source text chunks"):
for i, chunk in enumerate(relevant_chunks, 1):
st.markdown(f"**Chunk {i}:**")
st.text(chunk[:400] + "..." if len(chunk) > 400 else chunk)
if i < len(relevant_chunks):
st.markdown("---")
except Exception as e:
st.error(f"Error: {str(e)}")
else:
st.info("👈 Please upload two PDFs and click 'Process PDFs' to get started!")
st.markdown("""
### 📖 How to Use:
1. **Upload PDFs**: Upload two PDF documents in the sidebar <- add as much as you want
2. **Process**: Click "Process PDFs" button (takes ~30 seconds first time because it needs to do multiple process)
3. **Ask Questions**: Type your question and click "Get Answer"
4. **View Sources**: Expand to see which text chunks were used
### 💡 Example Questions:
- What are the main topics in these documents?
- Summarize the key findings
- What does the document say about [specific topic]?
- List the important points mentioned
### ✨ Features:
- ✅ 2 document processing at a time concurently
- ✅ FAISS local searching for retrival of similar chunks
- ✅ Open source - Uses Hugging Face models
- ✅ Fast search - FAISS vector similarity
""")
# Footer
st.markdown("---")
st.markdown("Built for Algorizz for Interview round using Streamlit, Sentence Transformers, FAISS, and FLAN-T5 model")
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