Update app.py

app.py

@@ -1,10 +1,10 @@
 import streamlit as st
 from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.llms import HuggingFacePipeline
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_loaders import PyPDFLoader
 from langchain.chains import RetrievalQA
-from langchain.document_loaders import PyPDFLoader
+from langchain.llms import HuggingFacePipeline
 from transformers import pipeline
 
 # ----------------------------
@@ -15,7 +15,7 @@ st.title("📘 PDF Question Answering App")
 st.markdown("Upload a PDF and ask questions about its content.")
 
 # ----------------------------
-# GLOBAL VARIABLES
+# GLOBAL VARIABLE
 # ----------------------------
 qa_chain = None
 
@@ -23,6 +23,7 @@ qa_chain = None
 # FUNCTIONS
 # ----------------------------
 def load_pdf(pdf_file):
+    """Load PDF and split into chunks"""
     loader = PyPDFLoader(pdf_file.name)
     documents = loader.load()
     text_splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=100)
@@ -30,11 +31,13 @@ def load_pdf(pdf_file):
     return docs
 
 def build_vectorstore(docs):
+    """Create FAISS vector store from documents"""
     embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
     vectorstore = FAISS.from_documents(docs, embeddings)
     return vectorstore
 
 def build_qa_chain(vectorstore):
+    """Build QA chain using FLAN-T5"""
     llm = HuggingFacePipeline(
         pipeline=pipeline(
             "text2text-generation",
@@ -45,7 +48,7 @@ def build_qa_chain(vectorstore):
     )
     qa_chain = RetrievalQA.from_chain_type(
         llm=llm,
-        retriever=vectorstore.as_retriever(search_kwargs={"k":3}),
+        retriever=vectorstore.as_retriever(search_kwargs={"k": 3}),
         chain_type="stuff"
     )
     return qa_chain
@@ -67,5 +70,5 @@ if qa_chain:
     if query:
         with st.spinner("Searching..."):
             answer = qa_chain.run(query)
-        st.markdown("### Answer:")
+        st.subheader("📌 Answer:")
         st.write(answer)