app.py

    import streamlit as st
    from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate
    from llama_index.llms.huggingface import HuggingFaceInferenceAPI
    from dotenv import load_dotenv
    from llama_index.embeddings.huggingface import HuggingFaceEmbedding
    from llama_index.core import Settings
    import os
    import base64
    import altair as alt

    # Load environment variables
    load_dotenv()

    # Configure the Llama index settings
    Settings.llm = HuggingFaceInferenceAPI(
        model_name="google/gemma-1.1-7b-it",
        tokenizer_name="google/gemma-1.1-7b-it",
        context_window=3000,
        token=os.getenv("HF_TOKEN"),
        max_new_tokens=512,
        generate_kwargs={"temperature": 0.1},
    )
    Settings.embed_model = HuggingFaceEmbedding(
        model_name="BAAI/bge-small-en-v1.5"
    )

    # Define the directory for persistent storage and data
    PERSIST_DIR = "./db"
    DATA_DIR = "data"

    # Ensure data directory exists
    os.makedirs(DATA_DIR, exist_ok=True)
    os.makedirs(PERSIST_DIR, exist_ok=True)

    def displayPDF(file):
        with open(file, "rb") as f:
            base64_pdf = base64.b64encode(f.read()).decode('utf-8')
        pdf_display = f'<iframe src="data:application/pdf;base64,{base64_pdf}" width="100%" height="600" type="application/pdf"></iframe>'
        st.markdown(pdf_display, unsafe_allow_html=True)

    def data_ingestion():
        documents = SimpleDirectoryReader(DATA_DIR).load_data()
        storage_context = StorageContext.from_defaults()
        index = VectorStoreIndex.from_documents(documents)
        index.storage_context.persist(persist_dir=PERSIST_DIR)

    def handle_query(query):
        storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
        index = load_index_from_storage(storage_context)
        chat_text_qa_msgs = [
        (
            "user",
            """created by vivek created for Neonflake Enterprises OPC Pvt Ltd
            Context:
            {context_str}
            Question:
            {query_str}
            """
        )
        ]
        text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
        
        query_engine = index.as_query_engine(text_qa_template=text_qa_template)
        answer = query_engine.query(query)
        
        if hasattr(answer, 'response'):
            return answer.response
        elif isinstance(answer, dict) and 'response' in answer:
            return answer['response']
        else:
            return "Sorry, I couldn't find an answer."


    # Streamlit app initialization
    st.title("Chat with your PDF📄")
    st.markdown("Built by [vivek](https://github.com/saravivek-cyber)")
    st.markdown("chat here")

    if 'messages' not in st.session_state:
        st.session_state.messages = [{'role': 'assistant', "content": 'Hello! Upload a PDF and ask me anything about its content.'}]

    with st.sidebar:
        st.title("Menu:")
        uploaded_file = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button")
        if st.button("Submit & Process"):
            with st.spinner("Processing..."):
                filepath = "data/saved_pdf.pdf"
                with open(filepath, "wb") as f:
                    f.write(uploaded_file.getbuffer())
                # displayPDF(filepath)  # Display the uploaded PDF
                data_ingestion()  # Process PDF every time new file is uploaded
                st.success("Done")

    user_prompt = st.chat_input("Ask me anything about the content of the PDF:")
    if user_prompt:
        st.session_state.messages.append({'role': 'user', "content": user_prompt})
        response = handle_query(user_prompt)
        st.session_state.messages.append({'role': 'assistant', "content": response})

    for message in st.session_state.messages:
        with st.chat_message(message['role']):
            st.write(message['content'])