Update app.py

app.py

@@ -63,57 +63,40 @@ else:
                 ),
             )
             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
-    
-    
-        # 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.")