layout updated wide to centered

app.py

@@ -1,67 +1,67 @@
-import streamlit as st
-import utils
-
-
-# https://github.com/serkanyasr/RAG-with-LangChain-URL-PDF/blob/main/utils.py
-
-
-st.set_page_config(layout="wide")
-st.markdown("<h1 style='font-size:24px;'>RAG with LangChain & GenAI: Any url</h1>", unsafe_allow_html=True)
-# st.title("RAG with LangChain & GenAI: Any url")
-
-# URL text box for user input
-url_input = st.text_input("Enter a URL to be queried:", "")
-
-# Input text box for user input
-user_input = st.text_input("Enter your Question below:", "")
-
-# Display the user input
-# st.write("You entered:", user_input)
-# st.write("URL entered:", url_input)
-sumbit_btn = st.button(label="Submit",key="url_btn")
-
-if sumbit_btn:
-    with st.spinner("Processing..."):
-        st.success("Response: Answering with RAG...")
-        response = utils.rag_with_url(url_input,user_input)
-        st.markdown(response)
-
-
-
-
-
-
-            
-
-# st.title("Retrieval-Augmented Generation (RAG) with LangChain : PDF ")
-# st.divider()
-
-# col_input , col_rag , col_normal = st.columns([3,5,5])
-# with col_input:
-#     selected_file = st.file_uploader("PDF File", type=["pdf"])
-#     st.divider()
-#     prompt = st.text_input("Prompt",key="pdf_prompt")
-#     st.divider()
-#     sumbit_btn = st.button(label="Submit",key="pdf_btn")
-
-# if sumbit_btn:
-#     with col_rag:
-#         with st.spinner("Processing..."):
-#             st.success("Response: Answering with RAG...")
-#             response,relevant_documents = utils.rag_with_pdf(file_path=f"./data/{selected_file.name}",
-#                                                                   prompt=prompt)
-#             st.markdown(response)
-#             st.divider()
-#             st.info("Documents")
-#             for doc in relevant_documents:
-#                 st.caption(doc.page_content)
-#                 st.markdown(f"Source: {doc.metadata}")
-#                 st.divider()
-
-#             with col_normal:
-#                 with st.spinner("Processing..."):
-#                     st.info("Response: Answering without RAG...")
-#                     response = utils.ask_gemini(prompt)
-#                     st.markdown(response)
-#                     st.divider()
+import streamlit as st
+import utils
+
+
+# https://github.com/serkanyasr/RAG-with-LangChain-URL-PDF/blob/main/utils.py
+
+
+st.set_page_config(layout="centered")
+st.markdown("<h1 style='font-size:24px;'>RAG with LangChain & GenAI: Any url</h1>", unsafe_allow_html=True)
+# st.title("RAG with LangChain & GenAI: Any url")
+
+# URL text box for user input
+url_input = st.text_input("Enter a URL to be queried:", "")
+
+# Input text box for user input
+user_input = st.text_input("Enter your Question below:", "")
+
+# Display the user input
+# st.write("You entered:", user_input)
+# st.write("URL entered:", url_input)
+sumbit_btn = st.button(label="Submit",key="url_btn")
+
+if sumbit_btn:
+    with st.spinner("Processing..."):
+        st.success("Response: Answering with RAG...")
+        response = utils.rag_with_url(url_input,user_input)
+        st.markdown(response)
+
+
+
+
+
+
+            
+
+# st.title("Retrieval-Augmented Generation (RAG) with LangChain : PDF ")
+# st.divider()
+
+# col_input , col_rag , col_normal = st.columns([3,5,5])
+# with col_input:
+#     selected_file = st.file_uploader("PDF File", type=["pdf"])
+#     st.divider()
+#     prompt = st.text_input("Prompt",key="pdf_prompt")
+#     st.divider()
+#     sumbit_btn = st.button(label="Submit",key="pdf_btn")
+
+# if sumbit_btn:
+#     with col_rag:
+#         with st.spinner("Processing..."):
+#             st.success("Response: Answering with RAG...")
+#             response,relevant_documents = utils.rag_with_pdf(file_path=f"./data/{selected_file.name}",
+#                                                                   prompt=prompt)
+#             st.markdown(response)
+#             st.divider()
+#             st.info("Documents")
+#             for doc in relevant_documents:
+#                 st.caption(doc.page_content)
+#                 st.markdown(f"Source: {doc.metadata}")
+#                 st.divider()
+
+#             with col_normal:
+#                 with st.spinner("Processing..."):
+#                     st.info("Response: Answering without RAG...")
+#                     response = utils.ask_gemini(prompt)
+#                     st.markdown(response)
+#                     st.divider()