Update bookie.py

bookie.py

@@ -1,3 +1,4 @@
+%%writefile book_searcher/bookie.py
 import streamlit as st
 import joblib
 from langchain_community.document_loaders import PyPDFLoader
@@ -13,7 +14,7 @@ import time
 load_dotenv("bookie.env")
 api_key=os.getenv("OPENAI_API_KEY")
 api_base=os.getenv("OPENAI_API_BASE")
-llm=ChatOpenAI(model_name="qwen/qwen3-coder:free",temperature=0.2)
+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 😊😊")
@@ -32,11 +33,11 @@ if upl and uploaded_file:
   mp.text("loading doc")
   loader = PyPDFLoader(tmp_path)
   docs = loader.load()
-  st.write(len(docs))  
+  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))  
+  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})
@@ -56,10 +57,13 @@ if qb:
       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)
 
 
-      
-