Update app.py

app.py

@@ -8,6 +8,7 @@ from langchain_core.runnables import RunnablePassthrough
 from langchain_text_splitters import RecursiveCharacterTextSplitter
 import bs4
 import torch
+import getpass
 
 # Prompt the user to enter their Langchain API key
 api_key_langchain = st.text_input("Enter your LANGCHAIN_API_KEY", type="password")
@@ -17,7 +18,7 @@ api_key_Groq = st.text_input("Enter your Groq_API_KEY", type="password")
 
 # Check if both API keys have been provided
 if not api_key_langchain or not api_key_Groq:
-    st.write("Please enter both API keys if you want to access this app.")
+    st.write("Please enter both API keys.")
 else:
     st.write("Both API keys are set.")
 
@@ -46,11 +47,13 @@ else:
     embedding_model = SentenceTransformerEmbedding('all-MiniLM-L6-v2')
 
     # Load, chunk, and index the contents of the blog
-    def load_data(url):
+    def load_data():
         loader = WebBaseLoader(
-            web_paths=(url,),
+            web_paths=("https://lilianweng.github.io/posts/2023-06-23-agent/",),
             bs_kwargs=dict(
-                parse_only=bs4.SoupStrainer()
+                parse_only=bs4.SoupStrainer(
+                    class_=("post-content", "post-title", "post-header")
+                )
             ),
         )
         docs = loader.load()
@@ -59,34 +62,30 @@ else:
         vectorstore = Chroma.from_documents(documents=splits, embedding=embedding_model)
         return vectorstore
 
-    # Streamlit UI
-    st.title("URL Retrieval and Question Answering")
-
-    # Input URL from user
-    url = st.text_input("Enter the URL:")
+    vectorstore = load_data()
 
-    if url:
-        vectorstore = load_data(url)
+    # Streamlit UI
+    st.title("Blog Retrieval and Question Answering")
 
-        question = st.text_input("Enter your question:")
+    question = st.text_input("Enter your question:")
 
-        if question:
-            retriever = vectorstore.as_retriever()
-            prompt = hub.pull("rlm/rag-prompt", api_key=api_key_langchain)
+    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)
+        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  # Replace with your LLM or appropriate function if needed
-                | StrOutputParser()
-            )
+        rag_chain = (
+            {"context": retriever | format_docs, "question": RunnablePassthrough()}
+            | prompt
+            | llm  # Replace with your LLM or appropriate function if needed
+            | StrOutputParser()
+        )
 
-            # Example invocation
-            try:
-                result = rag_chain.invoke(question)
-                st.write("Answer:", result)
-            except Exception as e:
-                st.error(f"An error occurred: {e}")
+        # Example invocation
+        try:
+            result = rag_chain.invoke(question)
+            st.write("Answer:", result)
+        except Exception as e:
+            st.error(f"An error occurred: {e}")