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import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
import os
import io
from langchain_huggingface import HuggingFaceEmbeddings
import google.generativeai as genai
from langchain_community.vectorstores import FAISS
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.chains.question_answering import load_qa_chain
from langchain.prompts import PromptTemplate
from dotenv import load_dotenv
# load the environment variables
load_dotenv()
# configure api key
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
# function to extract text from pdfs
def get_pdf_text(pdf_docs):
text=""
for pdf in pdf_docs:
pdf_reader = PdfReader(io.BytesIO(pdf.read()))
for page in pdf_reader.pages:
text+=page.extract_text()
return text
# function to convert text to chunks
def get_text_chunks(text):
text_splitter=RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
chunks=text_splitter.split_text(text)
return chunks
# function to create vector embedding
def get_vector_store(text_chunks):
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
vector_store=FAISS.from_texts(text_chunks, embedding=embeddings)
vector_store.save_local("faiss_index")
def get_conversational_chain():
prompt_template="""
Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in the provided context just say, "answer is not available in the context", don't provide any wrong answer\n\n
Context:\n{context}?\n
Question: \n{question}\n
Answer:
"""
model=ChatGoogleGenerativeAI(model="gemini-pro",temperature=0.3)
prompt=PromptTemplate(template=prompt_template, input_variables=["context", "question"])
chain=load_qa_chain(model, chain_type="stuff",prompt=prompt)
return chain
def user_input(user_question):
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
docs = new_db.similarity_search(user_question)
chain = get_conversational_chain()
response = chain(
{"input_documents":docs, "question": user_question}
, return_only_outputs=True)
print(response)
#st.write("Reply: ", response["output_text"])
st.write(response["output_text"])
def main():
st.set_page_config("Chat With Multiple PDF")
#st.header("Chat with Multiple PDF using Gemini")
# App name
st.markdown("<h5 style='text-align: center;'>ChatPDF</h5>", unsafe_allow_html=True)
user_question = st.text_input("Ask a Question from the PDF Files")
if user_question:
user_input(user_question)
with st.sidebar:
#st.title("Menu:")
pdf_docs = st.file_uploader("Upload PDFs and Click on Submit & Process", accept_multiple_files=True)
if st.button("Submit & Process"):
with st.spinner("Processing..."):
raw_text = get_pdf_text(pdf_docs)
text_chunks = get_text_chunks(raw_text)
get_vector_store(text_chunks)
st.success("Done")
if __name__=="__main__":
main()
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