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import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
import os
from langchain_google_genai import GoogleGenerativeAIEmbeddings
import google.generativeai as genai
from langchain.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_dotenv()
os.getenv("AIzaSyAZ6yZYDu8cbdpm1_4XmvWLQIy7PkUR5-M")
genai.configure(api_key=os.getenv("AIzaSyAZ6yZYDu8cbdpm1_4XmvWLQIy7PkUR5-M"))
def get_pdf_text(pdf_docs):
text=""
for pdf in pdf_docs:
pdf_reader= PdfReader(pdf)
for page in pdf_reader.pages:
text+= page.extract_text()
return text
def get_text_chunks(text):
text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
chunks = text_splitter.split_text(text)
return chunks
def get_vector_store(text_chunks):
embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001", google_api_key='AIzaSyAZ6yZYDu8cbdpm1_4XmvWLQIy7PkUR5-M')
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
provided context just say, "I could not find the answer, can you please try different words", don't provide the wrong answer\n\n
Context:\n {context}?\n
Question: \n{question}\n
Answer:
"""
model = ChatGoogleGenerativeAI(model="gemini-pro",
temperature=0.1, google_api_key='AIzaSyAZ6yZYDu8cbdpm1_4XmvWLQIy7PkUR5-M')
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 = GoogleGenerativeAIEmbeddings(model = "models/embedding-001", google_api_key='AIzaSyAZ6yZYDu8cbdpm1_4XmvWLQIy7PkUR5-M')
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"])
def main():
st.set_page_config("Chat PDF")
st.header("Ask your questions from the PDFs you uploaded")
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 your PDF Files and Click on the Submit & Process Button", 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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