Update app.py

app.py

@@ -5,6 +5,7 @@ import torch
 from tqdm.auto import tqdm
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 
+
 # from langchain.vectorstores import Chroma
 from langchain.vectorstores import FAISS
 from langchain.embeddings import HuggingFaceInstructEmbeddings
@@ -23,28 +24,47 @@ st.set_page_config(
 st.markdown("# Hello")
 
 
-with open("ait-web-document", "rb") as fp:
-    ait_web_documents = pickle.load(fp)
-    
-    
-text_splitter = RecursiveCharacterTextSplitter(
-    # Set a really small chunk size, just to show.
-    chunk_size = 500,
-    chunk_overlap  = 100,
-    length_function = len,
-)
+@st.cache_data
+def load_scraped_web_info():
+    with open("/Users/carlosito/Library/CloudStorage/OneDrive-Personal/AIT material/99-AIT-thesis/aitGPT/ait-web-document", "rb") as fp:
+        ait_web_documents = pickle.load(fp)
+        
+        
+    text_splitter = RecursiveCharacterTextSplitter(
+        # Set a really small chunk size, just to show.
+        chunk_size = 500,
+        chunk_overlap  = 100,
+        length_function = len,
+    )
+
+    chunked_text = text_splitter.create_documents([doc for doc in tqdm(ait_web_documents)])
+
 
-chunked_text = text_splitter.create_documents([doc for doc in tqdm(ait_web_documents)])
+    st.markdown(f"Number of Documents: {len(ait_web_documents)}")
+    st.markdown(f"Number of chunked texts: {len(chunked_text)}")
 
 
-st.markdown(f"Number of Documents: {len(ait_web_documents)}")
-st.markdown(f"Number of chunked texts: {len(chunked_text)}")
 
 
-embedding_model = HuggingFaceInstructEmbeddings(model_name='hkunlp/instructor-base',
+@st.cache_resource
+def load_embedding_model():
+    embedding_model = HuggingFaceInstructEmbeddings(model_name='hkunlp/instructor-base',
                                                 model_kwargs = {'device': torch.device('cuda' if torch.cuda.is_available() else 'cpu')})
+    return embedding_model
+
+@st.cache_data
+def load_faiss_index():
+    vector_database = FAISS.load_local("faiss_index", embedding_model)
+    return vector_database
+
 
-vector_database = FAISS.load_local("faiss_index", embedding_model)
+#--------------
+
+
+
+load_scraped_web_info()
+embedding_model = load_embedding_model()
+vector_database = load_faiss_index()
 print("load done")
 
 
@@ -57,3 +77,4 @@ def retrieve_document(query_input):
 
 output = st.text_area(label = "Here is the relevant documents",
                       value = retrieve_document(query_input))
+