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import streamlit as st
from PyPDF2 import PdfReader
from langchain_text_splitters import RecursiveCharacterTextSplitter
import os
from langchain_google_genai import GoogleGenerativeAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.chains.question_answering import load_qa_chain
from langchain.prompts import PromptTemplate
st.set_page_config(page_title="PDF CHATBOT", layout="wide")
st.markdown("""
## Document Genie: Get instant insights from your Documents
This chatbot is built using the Retrieval-Augmented Generation (RAG) framework, leveraging Google's Generative AI model Gemini-PRO. It processes uploaded PDF documents by breaking them down into manageable chunks, creates a searchable vector store, and generates accurate answers to user queries. This advanced approach ensures high-quality, contextually relevant responses for an efficient and effective user experience.
### How It Works
Follow these simple steps to interact with the chatbot:
1. **Upload Your Documents**: The system accepts multiple PDF files at once, analyzing the content to provide comprehensive insights.
2. **Ask a Question**: After processing the documents, ask any question related to the content of your uploaded documents for a precise answer.
""")
def get_pdf(pdf_docs):
text = ""
for pdf in pdf_docs:
pdf_reader = PdfReader(pdf)
for page in pdf_reader.pages:
text += page.extract_text()
return text
GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
def response_generate(text,query):
text_splitter = RecursiveCharacterTextSplitter(
# Set a really small chunk size, just to show.
chunk_size=500,
chunk_overlap=20,
separators=["\n\n","\n"," ",".",","])
chunks=text_splitter.split_text(text)
embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
db = Chroma.from_documents(chunks, embeddings)
# Create retriever interface
retriever = db.as_retriever()
qa = RetrievalQA.from_chain_type(llm = GoogleGenerativeAI(model="gemini-pro", google_api_key=GOOGLE_API_KEY ), chain_type='stuff', retriever=retriever)
return qa.run(query_text)
def main():
st.header("Chat with your pdf💁")
query = st.text_input("Ask a Question from the PDF Files", key="query")
#if query:
# user_call(query)
st.title("Menu:")
pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True, key="pdf_uploader")
if st.button("Submit & Process", key="process_button"):
with st.spinner("Processing..."):
raw_text = get_pdf(pdf_docs)
#text_chunks = text_splitter(raw_text)
response = response_generate(raw_text,query)
st.success("Done")
st.write("Reply: ", response)
if __name__ == "__main__":
main() |