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import streamlit as st
import os
import logging
from io import BytesIO
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.prompts import PromptTemplate
from langchain.chains.question_answering import load_qa_chain
from langchain_community.llms import HuggingFaceHub
from transformers import pipeline # For fallback if Hub fails
# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Check API token
if "HUGGINGFACEHUB_API_TOKEN" not in os.environ:
st.error("HUGGINGFACEHUB_API_TOKEN not set in secrets. Add it in Space settings.")
st.stop()
try:
# Function to process PDF
def process_pdf(uploaded_file):
try:
logger.info("Starting PDF processing")
pdf_reader = PdfReader(BytesIO(uploaded_file.getvalue()))
text = ""
for page in pdf_reader.pages:
extracted = page.extract_text()
if extracted:
text += extracted + "\n"
if not text:
raise ValueError("No text extracted from PDF.")
# Chunk text (increased overlap for better context)
text_splitter = CharacterTextSplitter(separator="\n", chunk_size=800, chunk_overlap=200, length_function=len)
chunks = text_splitter.split_text(text)
# Embeddings (light model)
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={'device': 'cpu'})
# Vector store
vector_store = FAISS.from_texts(chunks, embedding=embeddings)
logger.info("PDF processed successfully")
return vector_store
except Exception as e:
logger.error(f"PDF processing error: {str(e)}")
st.error(f"Error processing PDF: {str(e)}")
return None
# Function to answer questions
def answer_question(vector_store, query):
try:
logger.info(f"Answering query: {query}")
# Lighter LLM via pipeline for faster CPU inference
qa_pipeline = pipeline("text2text-generation", model="google/flan-t5-base")
# Retrieve top chunks
docs = vector_store.similarity_search(query, k=3)
context = "\n".join([doc.page_content for doc in docs])
# Prompt
prompt = f"Use this context to answer concisely: {context}\nQuestion: {query}\nAnswer:"
response = qa_pipeline(prompt, max_length=256, num_return_sequences=1)[0]['generated_text']
logger.info("Answer generated")
return response.strip()
except Exception as e:
logger.error(f"Answer generation error: {str(e)}")
st.error(f"Error answering: {str(e)}")
return "Unable to generate answer."
# Streamlit UI with chat history
st.title("Smart PDF Q&A")
st.write("Upload a PDF and ask questions! Chat history is preserved.")
# Initialize session state
if "messages" not in st.session_state:
st.session_state.messages = []
if "vector_store" not in st.session_state:
st.session_state.vector_store = None
# PDF upload and process
uploaded_file = st.file_uploader("Upload PDF", type="pdf")
if uploaded_file:
if st.button("Process PDF"):
with st.spinner("Processing..."):
vector_store = process_pdf(uploaded_file)
if vector_store:
st.session_state.vector_store = vector_store
st.success("PDF ready! Ask away.")
st.session_state.messages = [] # Reset chat on new PDF
# Display chat history
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# Question input
if st.session_state.vector_store:
if prompt := st.chat_input("Ask a question:"):
# Add user message
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.markdown(prompt)
# Generate answer
with st.chat_message("assistant"):
with st.spinner("Thinking..."):
answer = answer_question(st.session_state.vector_store, prompt)
st.markdown(answer)
st.session_state.messages.append({"role": "assistant", "content": answer})
except Exception as e:
logger.error(f"App initialization failed: {str(e)}")
st.error(f"Initialization error: {str(e)}. Check logs or try factory reset.") |