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

# Load environment variables
load_dotenv()

# Configure the Llama index settings
Settings.llm = HuggingFaceInferenceAPI(
    model_name="nltpt/Llama-3.2-3B-Instruct",
    tokenizer_name="nltpt/Llama-3.2-3B-Instruct",
    #     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",
        """You are a Q&A assistant. Your main goal is to provide answers as accurately as possible, based on the context of the document provided. If the question does not match the context or is outside the scope of the document, advise the user to ask questions that are relevant to the document.
        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("(PDF) Information and Inference🗞️")
st.markdown("Retrieval-Augmented Generation") 
st.markdown("Start chat ...🚀")

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

with st.sidebar:
    st.title("Menu:")
    uploaded_files = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", type="pdf", accept_multiple_files=True)
    
    if st.button("Submit & Process"):
        if uploaded_files:
            with st.spinner("Processing..."):
                for uploaded_file in uploaded_files:
                    filepath = os.path.join(DATA_DIR, uploaded_file.name)
                    with open(filepath, "wb") as f:
                        f.write(uploaded_file.getbuffer())
                data_ingestion()  # Process PDFs after they are uploaded
                st.success("Done")
        else:
            st.warning("Please upload at least one PDF file.")

user_prompt = st.chat_input("Ask me anything about the content of the PDF(s):")
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'])