Update app.py

app.py

@@ -53,67 +53,67 @@ else:
 
     # Load, chunk, and index the contents of the blog
     def load_data(url):
-    try:
-        loader = WebBaseLoader(
-            web_paths=(url,),
-            bs_kwargs=dict(
-                parse_only=bs4.SoupStrainer(
-                    class_=("post-content", "post-title", "post-header")
-                )
-            ),
-        )
-        docs = loader.load()
-        if not docs:
-            st.error("No documents were loaded. Please check the URL and try again.")
+        try:
+            loader = WebBaseLoader(
+                web_paths=(url,),
+                bs_kwargs=dict(
+                    parse_only=bs4.SoupStrainer(
+                        class_=("post-content", "post-title", "post-header")
+                    )
+                ),
+            )
+            docs = loader.load()
+            if not docs:
+                st.error("No documents were loaded. Please check the URL and try again.")
+                return None
+            
+            st.write(f"Loaded {len(docs)} documents.")
+    
+            text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+            splits = text_splitter.split_documents(docs)
+            if not splits:
+                st.error("No document splits were created. Please check the document content.")
+                return None
+            
+            st.write(f"Created {len(splits)} document splits.")
+    
+            vectorstore = Chroma.from_documents(documents=splits, embedding=embedding_model)
+            if vectorstore is None:
+                st.error("Failed to create the vectorstore.")
+                return None
+            
+            return vectorstore
+        except Exception as e:
+            st.error(f"An error occurred while loading the blog: {e}")
             return None
-        
-        st.write(f"Loaded {len(docs)} documents.")
-
-        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-        splits = text_splitter.split_documents(docs)
-        if not splits:
-            st.error("No document splits were created. Please check the document content.")
-            return None
-        
-        st.write(f"Created {len(splits)} document splits.")
-
-        vectorstore = Chroma.from_documents(documents=splits, embedding=embedding_model)
-        if vectorstore is None:
-            st.error("Failed to create the vectorstore.")
-            return None
-        
-        return vectorstore
-    except Exception as e:
-        st.error(f"An error occurred while loading the blog: {e}")
-        return None
-
-
-    # Load the data if a URL is provided
-    if blog_url:
-        vectorstore = load_data(blog_url)
-        if vectorstore:
-            # Streamlit UI for question input
-            question = st.text_input("Enter your question:")
-
-            if question:
-                retriever = vectorstore.as_retriever()
-                prompt = hub.pull("rlm/rag-prompt", api_key=api_key_langchain)
-
-                def format_docs(docs):
-                    return "\n\n".join(doc.page_content for doc in docs)
-
-                rag_chain = (
-                    {"context": retriever | format_docs, "question": RunnablePassthrough()}
-                    | prompt
-                    | llm
-                    | StrOutputParser()
-                )
-
-                # Example invocation
-                try:
-                    result = rag_chain.invoke(question)
-                    st.write("Answer:", result)
-                except Exception as e:
-                    st.error(f"An error occurred while generating the answer: {e}")
-        else:
-            st.write("Failed to load the blog content. Please check the URL and try again.")
+    
+    
+        # Load the data if a URL is provided
+        if blog_url:
+            vectorstore = load_data(blog_url)
+            if vectorstore:
+                # Streamlit UI for question input
+                question = st.text_input("Enter your question:")
+    
+                if question:
+                    retriever = vectorstore.as_retriever()
+                    prompt = hub.pull("rlm/rag-prompt", api_key=api_key_langchain)
+    
+                    def format_docs(docs):
+                        return "\n\n".join(doc.page_content for doc in docs)
+    
+                    rag_chain = (
+                        {"context": retriever | format_docs, "question": RunnablePassthrough()}
+                        | prompt
+                        | llm
+                        | StrOutputParser()
+                    )
+    
+                    # Example invocation
+                    try:
+                        result = rag_chain.invoke(question)
+                        st.write("Answer:", result)
+                    except Exception as e:
+                        st.error(f"An error occurred while generating the answer: {e}")
+            else:
+                st.write("Failed to load the blog content. Please check the URL and try again.")