Update app.py

app.py

@@ -1,27 +1,44 @@
 import streamlit as st
-from model import load_vectorstore, ask_question
 import os
+from model import load_vectorstore, ask_question
 
-st.set_page_config(page_title="RAG with Mistral AI", layout="centered")
-
-st.title("RAG Q&A App with Mistral AI")
-st.write("Upload a document and ask questions about it.")
+st.set_page_config(page_title="Simple RAG Q&A", layout="centered")
+st.title("RAG Q&A with Mistral AI")
+st.write("Upload a PDF and ask questions about its content.")
 
+# PDF upload
 uploaded_file = st.file_uploader("Upload PDF", type=["pdf"])
+pdf_path = "/app/data/document.pdf"
 
 if uploaded_file:
-    with open("document.pdf", "wb") as f:
+    os.makedirs("/app/data", exist_ok=True)
+    with open(pdf_path, "wb") as f:
         f.write(uploaded_file.read())
-    st.success("File uploaded!")
+    st.success("PDF uploaded!")
+    
+    with st.spinner("Indexing document..."):
+        try:
+            load_vectorstore(pdf_path)
+            st.success("Document indexed!")
+        except Exception as e:
+            st.error(f"Indexing failed: {str(e)}")
 
-    if st.button("Index Document"):
-        with st.spinner("Processing..."):
-            load_vectorstore("document.pdf")
-            st.success("Vectorstore ready.")
-
-query = st.text_input("Enter your question")
+# Query input
+query = st.text_input("Enter your question", 
+                      "How many articles are there in the Selenium webdriver python course?")
 if st.button("Ask") and query:
-    with st.spinner("Generating answer..."):
-        answer = ask_question(query)
-        st.write("**Answer:**", answer)
-
+    if not os.path.exists(pdf_path):
+        st.error("Please upload a PDF first.")
+    else:
+        with st.spinner("Generating answer..."):
+            try:
+                result = ask_question(query, pdf_path)
+                st.subheader("Answer")
+                st.write(result["answer"])
+                
+                st.subheader("Retrieved Contexts")
+                for i, context in enumerate(result["contexts"], 1):
+                    with st.expander(f"Context {i}"):
+                        st.write(context)
+            except Exception as e:
+                st.error(f"Failed to generate answer: {str(e)}")