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import streamlit as st
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import os
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from langchain_groq import ChatGroq
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from langchain_huggingface import HuggingFaceEmbeddings
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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from langchain.chains.combine_documents import create_stuff_documents_chain
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from langchain_core.prompts import ChatPromptTemplate
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from langchain.chains import create_retrieval_chain
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from langchain_community.vectorstores import FAISS
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from langchain_community.document_loaders import PyPDFLoader
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from dotenv import load_dotenv
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from PyPDF2 import PdfReader
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import time
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load_dotenv()
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st.markdown(
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"""
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<style>
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.stApp {
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background-image: url('https://www.transparenttextures.com/patterns/white-leather.png');
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background-size: cover;
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}
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.sidebar .sidebar-content {
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padding: 20px;
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background-image: url('https://www.transparenttextures.com/patterns/asfalt-light.png');
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background-size: cover;
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border-radius: 10px;
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box-shadow: 0px 0px 10px rgba(0, 0, 0, 0.1);
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}
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.sidebar .bottom-button {
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position: fixed;
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bottom: 20px;
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left: 20px;
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width: calc(100% - 40px);
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}
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</style>
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""",
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unsafe_allow_html=True
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)
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os.environ["HF_TOKEN"] = os.getenv("HF_TOKEN")
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groq_api_key = os.getenv("GROQ_API_KEY")
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embeddings = HuggingFaceEmbeddings(model_name = "all-MiniLM-L6-v2")
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llm = ChatGroq(model = "Llama3-8b-8192",api_key = groq_api_key)
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prompt_template = ChatPromptTemplate.from_template("""
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Answer the following question from the provided context only.
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Please provide the most accurate response based on the question
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<context>
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{context}
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</context>
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Question : {input}
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""")
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def get_pdf_text(pdf_docs):
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text=""
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for pdf in pdf_docs:
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pdf_reader= PdfReader(pdf)
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for page in pdf_reader.pages:
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text+= page.extract_text()
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return text
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def create_vector_embeddings(pdfText):
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if "vectors" not in st.session_state:
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st.session_state.docs = get_pdf_text(pdfText)
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st.session_state.splitter = RecursiveCharacterTextSplitter(chunk_size=1200,chunk_overlap=400)
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st.session_state.final_docs = st.session_state.splitter.split_text(st.session_state.docs)
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st.session_state.vectors = FAISS.from_texts(st.session_state.final_docs, embeddings)
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if "options" not in st.session_state:
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st.session_state.options = ["Select a query"]
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if "user_prompt" not in st.session_state:
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st.session_state.user_prompt = ""
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def autopopulate_promptsbydoctype(uploaded_text):
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if uploaded_text and uploaded_text[0].name.endswith("pdf"):
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itemsToAppend = ["get all the programme details including rights and tape content etc in pointwise manner, dont miss any info",
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"give a structured short summary of the programmes and details",
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"give me programme package with programme details listed"]
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for itemToAppend in itemsToAppend:
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if itemToAppend not in st.session_state.options:
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st.session_state.options.append(itemToAppend)
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st.title("Basic Document QnA")
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with st.sidebar:
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st.title("Menu:")
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st.session_state.uploaded_text = st.file_uploader("Upload your Files and Click on the Submit & Process Button", accept_multiple_files=True)
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if st.button("Click To Process File"):
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with st.spinner("Processing..."):
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create_vector_embeddings(st.session_state.uploaded_text)
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st.write("Vector Database is ready")
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autopopulate_promptsbydoctype(st.session_state.uploaded_text)
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new_option = st.text_input("Or type your query here:")
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if new_option and new_option not in st.session_state.options:
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st.session_state.options.append(new_option)
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st.session_state.user_prompt = new_option
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if st.session_state.uploaded_text and "Technical" not in st.session_state.uploaded_text[0].name:
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st.session_state.user_prompt= st.selectbox("Enter/Select your query from the document", st.session_state.options,
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index=st.session_state.options.index(st.session_state.user_prompt) if st.session_state.user_prompt in st.session_state.options else 0)
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if st.session_state.user_prompt and st.session_state.user_prompt != "Select a query":
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document_chain = create_stuff_documents_chain(llm=llm, prompt= prompt_template)
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retriever = st.session_state.vectors.as_retriever()
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retrieval_chain=create_retrieval_chain(retriever,document_chain)
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start = time.process_time()
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response = retrieval_chain.invoke({"input": st.session_state.user_prompt})
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print(f"Response time :{time.process_time()-start}")
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st.write(response['answer'])
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with st.expander("Document similarity Search"):
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for i,doc in enumerate(response['context']):
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st.write(doc.page_content)
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st.write('------------------------')
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