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import os
import getpass
import streamlit as st
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import Chroma
from langchain import HuggingFaceHub
from langchain.chains import RetrievalQA
# __import__('pysqlite3')
# import sys
# sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')
# load huggingface api key
hubtok = os.environ["HUGGINGFACE_HUB_TOKEN"]
# use streamlit file uploader to ask user for file
# file = st.file_uploader("Upload PDF")
path = "https://vedpuran.files.wordpress.com/2013/04/455_gita_roman.pdf"
loader = PyPDFLoader(path)
pages = loader.load()
# st.write(pages)
splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=20)
docs = splitter.split_documents(pages)
embeddings = HuggingFaceEmbeddings()
doc_search = Chroma.from_documents(docs, embeddings)
repo_id = "tiiuae/falcon-7b"
llm = HuggingFaceHub(repo_id=repo_id, huggingfacehub_api_token=hubtok, model_kwargs={'temperature': 0.2,'max_length': 1000})
from langchain.schema import retriever
retireval_chain = RetrievalQA.from_chain_type(llm, chain_type="stuff", retriever=doc_search.as_retriever())
if query := st.chat_input("Enter a question: "):
with st.chat_message("assistant"):
st.write(retireval_chain.run(query)) |