Update app.py

app.py

@@ -65,10 +65,8 @@ def create_langchain_index(input_text):
     # load it into Chroma
     db = Chroma.from_documents(docs, embeddings)
     persist_directory = "chroma_db"  
-    vectordb = Chroma.from_documents(
-        documents=docs, embedding=embeddings, persist_directory=persist_directory
-    )
-    new_db = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
+    #vectordb = Chroma.from_documents(documents=docs, embedding=embeddings, persist_directory=persist_directory)
+    #new_db = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
     return db
 
 # @st.cache_resource
@@ -82,7 +80,7 @@ def create_langchain_index(input_text):
 
 
 @st.cache_data
-def get_response(input_text,query):
+def get_response(input_text,query,db):
     print(f"--querying---{query}")
     retrieval_chain = RetrievalQA.from_chain_type(llm, chain_type="stuff", retriever=db.as_retriever())
     response = retrieval_chain.run(query)
@@ -102,15 +100,15 @@ tweet_response = ""
 ln_response = ""
 # if st.button("Load"):
 if input_text:
-    index = create_langchain_index(input_text)
+    db = create_langchain_index(input_text)
     summary_query ="Write a 100 words summary of the document"
-    summary_response = get_response(input_text,summary_query)
+    summary_response = get_response(input_text,summary_query,db)
 
     tweet_query ="Write a twitter tweet"
-    tweet_response =  get_response(input_text,tweet_query)
+    tweet_response =  get_response(input_text,tweet_query,db)
 
     ln_query ="Write a linkedin post for the document"
-    ln_response = get_response(input_text,ln_query)
+    ln_response = get_response(input_text,ln_query,db)
 
 
     with st.expander('Page Summary'): 
@@ -127,8 +125,8 @@ st.session_state.input_text = ''
 question=st.text_input("Ask a question from the link you shared...")
 if st.button("Ask"):
         if question:
-            index = create_langchain_index(input_text)
-            response = get_response(input_text,question)
+            db = create_langchain_index(input_text)
+            response = get_response(input_text,question,db)
             st.write(response)
         else:
             st.warning("Please enter a question.")