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# Importing dependencies
from dotenv import load_dotenv
import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.prompts import PromptTemplate
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
from htmlTemplates import css, bot_template, user_template
# Load environment variables
load_dotenv()
# Creating custom template to guide LLM model
custom_template = """Given the following conversation and a follow-up question, rephrase the follow-up question to be a standalone question, in its original language.
Chat History:
{chat_history}
Follow Up Input: {question}
Standalone question:"""
CUSTOM_QUESTION_PROMPT = PromptTemplate.from_template(custom_template)
# Extracting text from PDF
def get_pdf_text(docs):
text = ""
for pdf in docs:
pdf_reader = PdfReader(pdf)
for page in pdf_reader.pages:
text += page.extract_text()
return text
# Converting text to chunks
def get_chunks(raw_text):
text_splitter = CharacterTextSplitter(
separator="\n",
chunk_size=1000,
chunk_overlap=200,
length_function=len
)
chunks = text_splitter.split_text(raw_text)
return chunks
# Using Hugging Face embeddings model and FAISS to create vectorstore
def get_vectorstore(chunks):
embeddings = HuggingFaceEmbeddings(
model_name="sentence-transformers/all-MiniLM-L6-v2",
model_kwargs={'device': 'cpu'}
)
vectorstore = FAISS.from_texts(texts=chunks, embedding=embeddings)
return vectorstore
# Generating conversation chain
def get_conversationchain(vectorstore):
# Use a Hugging Face model for question-answering
model_name = "distilbert-base-uncased-distilled-squad" # Pretrained QA model
qa_pipeline = pipeline("question-answering", model=model_name, tokenizer=model_name)
def qa_function(question, context):
response = qa_pipeline(question=question, context=context)
return response['answer']
memory = ConversationBufferMemory(
memory_key='chat_history',
return_messages=True,
output_key='answer'
)
def conversation_chain(inputs):
question = inputs['question']
# Extract text content from Document objects
documents = vectorstore.similarity_search(question, k=5)
context = "\n".join([doc.page_content for doc in documents]) # Extract `page_content` from each Document
answer = qa_function(question, context)
memory.save_context({"user_input": question}, {"answer": answer})
return {"chat_history": memory.chat_memory.messages, "answer": answer}
return conversation_chain
# Generating response from user queries and displaying them accordingly
def handle_question(question):
response = st.session_state.conversation({'question': question})
st.session_state.chat_history = response["chat_history"]
for i, msg in enumerate(st.session_state.chat_history):
if i % 2 == 0:
st.write(user_template.replace("{{MSG}}", msg.content), unsafe_allow_html=True)
else:
st.write(bot_template.replace("{{MSG}}", msg.content), unsafe_allow_html=True)
def main():
st.set_page_config(page_title="Chat with multiple PDFs", page_icon=":books:")
st.write(css, unsafe_allow_html=True)
if "conversation" not in st.session_state:
st.session_state.conversation = None
if "chat_history" not in st.session_state:
st.session_state.chat_history = None
st.header("CSS Edge - Intelligent Document Chatbot :books:")
question = st.text_input("Ask a question from your document:")
if question:
handle_question(question)
with st.sidebar:
st.subheader("Your documents")
docs = st.file_uploader("Upload your PDF here and click on 'Process'", accept_multiple_files=True)
if st.button("Process"):
with st.spinner("Processing..."):
# Get the PDF text
raw_text = get_pdf_text(docs)
# Get the text chunks
text_chunks = get_chunks(raw_text)
# Create vectorstore
vectorstore = get_vectorstore(text_chunks)
# Create conversation chain
st.session_state.conversation = get_conversationchain(vectorstore)
if __name__ == '__main__':
main()
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