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import os
import streamlit as st
import pdfplumber
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from transformers import pipeline
# Set up the page configuration
st.set_page_config(page_title="RAG-based PDF Chat", layout="centered", page_icon="📄")
# Load the summarization pipeline model
@st.cache_resource
def load_summarization_pipeline():
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
return summarizer
summarizer = load_summarization_pipeline()
# Function to preprocess PDFs and store embeddings
def preprocess_pdfs(folder_path, save_vectorstore_path):
all_text = ""
pdf_files = [os.path.join(folder_path, filename) for filename in os.listdir(folder_path) if filename.endswith('.pdf')]
for file_path in pdf_files:
with pdfplumber.open(file_path) as pdf:
for page in pdf.pages:
page_text = page.extract_text()
if page_text:
all_text += page_text
if all_text:
text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
text_chunks = text_splitter.split_text(all_text)
embedding_function = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
vector_store = FAISS.from_texts(text_chunks, embedding=embedding_function)
vector_store.save_local(save_vectorstore_path)
st.success("Data preprocessing and vector store creation completed!")
# Load pre-trained FAISS vector store
@st.cache_resource
def load_vector_store(save_vectorstore_path):
embedding_function = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
return FAISS.load_local(save_vectorstore_path, embedding_function, allow_dangerous_deserialization=True)
# Generate summary based on the retrieved text
def generate_summary_with_huggingface(query, retrieved_text):
summarization_input = f"{query} Related information:{retrieved_text}"
max_input_length = 1024
summarization_input = summarization_input[:max_input_length]
summary = summarizer(summarization_input, max_length=500, min_length=50, do_sample=False)
return summary[0]["summary_text"]
# Generate response for user query
def user_input(user_question, vector_store):
docs = vector_store.similarity_search(user_question)
context_text = " ".join([doc.page_content for doc in docs])
return generate_summary_with_huggingface(user_question, context_text)
# Main function to run the Streamlit app
def main():
st.title("📄 Gen AI Lawyers Guide")
data_folder = 'documents1' # Folder where your PDFs are located
vectorstore_path = 'vector_store_data/faiss_vectorstore' # Folder to save the vector store
# Uncomment this line for initial preprocessing only. Once done, comment it out.
# preprocess_pdfs(data_folder, vectorstore_path)
# Load the pre-trained vector store
vector_store = load_vector_store(vectorstore_path)
user_question = st.text_input("Ask a Question:", placeholder="Type your question here...")
if st.button("Get Response"):
if not user_question:
st.warning("Please enter a question before submitting.")
else:
with st.spinner("Generating response..."):
answer = user_input(user_question, vector_store)
st.markdown(f"**🤖 AI:** {answer}")
if __name__ == "__main__":
main()
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