app.py

import os
import streamlit as st
import pickle
import time
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from langchain.llms.base import LLM
from langchain.chains import RetrievalQAWithSourcesChain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import UnstructuredURLLoader
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings
from huggingface_hub import login

# Login to Hugging Face
login(os.getenv('HF_llama3chat8b'))

class CustomHuggingFaceLLM(LLM):
    def __init__(self, model_name, temperature=0.7):

        # Configure 8-bit quantization using `BitsAndBytesConfig`
        quantization_config = BitsAndBytesConfig(
            load_in_8bit=True,  # Enable 8-bit quantization
            llm_int8_enable_fp32_cpu_offload=True  # Offload FP32 operations to CPU for further memory savings
        )
        
        self.model = AutoModelForCausalLM.from_pretrained(model_name, device_map="cpu", quantization_config=quantization_config)
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.temperature = temperature

    def _call(self, prompt, stop=None):
        input_ids = self.tokenizer.encode(prompt, return_tensors="pt")
        output = self.model.generate(
            input_ids,
            max_length=512,
            temperature=self.temperature,
            do_sample=True,
            top_p=0.95,
            top_k=3
        )
        generated_text = self.tokenizer.decode(output[0], skip_special_tokens=True)
        return generated_text

    @property
    def _identifying_params(self):
        return {"model_name": self.model.config._name_or_path, "temperature": self.temperature}

    @property
    def _llm_type(self):
        return "custom_huggingface"



main_directory = os.path.dirname(os.path.abspath(__file__))

st.title("Web Page search Bot: Research Tool 📈")
st.sidebar.title("Article URLs")

urls = []
for i in range(3):
    url = st.sidebar.text_input(f"URL {i+1}")
    urls.append(url)

process_url_clicked = st.sidebar.button("Process URLs")
file_path_faiss = "faiss_store.pkl"

main_placeholder = st.empty()

# Load a pre-trained embedding model
embedding_model = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
llm = CustomHuggingFaceLLM(model_name="meta-llama/Meta-Llama-3.1-8B", temperature=0.6)
if process_url_clicked:
    # load data
    loader = UnstructuredURLLoader(urls=urls)
    main_placeholder.text("Data Loading...Started...✅✅✅")
    data = loader.load()
    # split data
    # Do not include unnecessary separators like , and . It will reduce chunks too small.
    text_splitter = RecursiveCharacterTextSplitter(
        separators=['\n\n'],
        chunk_size=1000,
        chunk_overlap=100
    )
    main_placeholder.text("Text Splitter...Started...✅✅✅")
    docs = text_splitter.split_documents(data)

    # create embeddings and save it to FAISS index
    vectorstore_faiss = FAISS.from_documents(documents=docs,embedding=embedding_model)
    main_placeholder.text("Embedding Vector Started Building...✅✅✅")
    time.sleep(2)
    
    # Save the FAISS index to a pickle file
    with open(file_path_faiss, "wb") as f:
        pickle.dump(vectorstore_faiss, f)

query = main_placeholder.text_input("Question: ")
if query:
    if os.path.exists(file_path_faiss):
        with open(file_path_faiss, "rb") as f:
            vectorstore = pickle.load(f)
            chain = RetrievalQAWithSourcesChain.from_llm(llm=llm, retriever=vectorstore.as_retriever(), verbose=True) # type: ignore
            result = chain({"question": query}, return_only_outputs=True)
            # result will be a dictionary of this format --> {"answer": "", "sources": [] }
            st.header("Answer")
            st.write(result["answer"])

            # Display sources, if available
            sources = result.get("sources", "")
            if sources:
                st.subheader("Sources:")
                sources_list = sources.split("\n")  # Split the sources by newline
                for source in sources_list:
                    st.write(source)