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import streamlit as st
import joblib
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import SentenceTransformerEmbeddings
from langchain_community.vectorstores import Chroma
from langchain.chains import RetrievalQAWithSourcesChain
from langchain_openai import ChatOpenAI
from dotenv import load_dotenv
import os
import time
load_dotenv("bookie.env")
api_key=os.getenv("OPENAI_API_KEY")
api_base=os.getenv("OPENAI_API_BASE")
llm=ChatOpenAI(model_name="google/gemma-3n-e2b-it:free",temperature=0.2)
em=joblib.load("bai.joblib")
mp=st.empty()
st.title("Welcome to Bookie 😊😊")
st.sidebar.title("give you book in pdf format(digitally generated) and less than 5mb for faster answers😊😊")
uploaded_file = st.sidebar.file_uploader("Upload a PDF file", type=["pdf"])
upl=st.sidebar.button("upload")
import tempfile
if upl and uploaded_file:
if uploaded_file.size > 5 * 1024 * 1024:
st.sidebar.error("❌ File too large. Please upload files under 5MB.")
st.stop()
with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file:
tmp_file.write(uploaded_file.read())
tmp_path = tmp_file.name
mp.text("loading doc")
loader = PyPDFLoader(tmp_path)
docs = loader.load()
st.write(len(docs))
mp.text("loading split")
tct=RecursiveCharacterTextSplitter.from_tiktoken_encoder(encoding_name="cl100k_base",chunk_size=512, chunk_overlap=16)
doc=tct.split_documents(docs)
st.write(len(doc))
mp.text("loading vector db")
vb= Chroma.from_documents(doc,em)
r1=vb.as_retriever(search_type="similarity",search_kwargs={"k":4})
mp.text("loading retriever")
chain=RetrievalQAWithSourcesChain.from_chain_type(llm=llm,chain_type="map_reduce",retriever=r1)
st.session_state.chain=chain
mp.text("loading done")
time.sleep(3)
q=mp.text_input("Ask a question about the document:")
qb=st.button("submit")
if qb:
if "chain" not in st.session_state:
st.warning("⚠️ Please upload a document first.")
st.stop()
else:
with st.spinner("Waiting for it...."):
result=st.session_state.chain({"question":q},return_only_outputs=True)
st.header("Answer")
st.subheader(result["answer"])
sb=st.button("show sources")
if sb:
sources = result.get("sources", "")
st.subheader("Sources")
for line in sources.split("\n"):
st.write(line)
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