Update app.py

app.py

@@ -1,30 +1,11 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
-from langchain_community.document_loaders import PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.vectorstores.faiss import FAISS
-from sentence_transformers import SentenceTransformer
 from langchain.chains import RetrievalQA
 from langchain_community.llms import HuggingFaceHub
-from langchain.docstore.document import Document
 
-# Load the PDF document
-loader = PyPDFLoader("apexcustoms.pdf")
-data = loader.load()
-
-# Split the document into chunks
-text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=20)
-texts = text_splitter.split_documents(data)
-
-# Create a list of document objects from the texts
-documents = [Document(page_content=doc.page_content) for doc in texts]
-
-# Create a vector store
-embeddings = SentenceTransformer("sentence-transformers/all-mpnet-base-v2")
-texts = [doc.page_content for doc in documents]  # Get the text content from the documents
-embeddings = embeddings.encode(texts)  # Get the embeddings for the texts
-
-vector_store = FAISS.from_documents(documents, embeddings)
+# Load the vector store from the saved index files
+vector_store = FAISS.load_local("db.index", embeddings=None)
 
 # Initialize the HuggingFaceHub LLM
 llm = HuggingFaceHub(repo_id="HuggingFaceH4/zephyr-7b-beta", model_kwargs={"temperature": None, "top_p": None})
@@ -47,7 +28,7 @@ def respond(message, history, system_message, max_tokens, temperature, top_p):
 
     messages.append({"role": "user", "content": message})
 
-    result = qa({"input_documents": documents, "question": message})
+    result = qa({"input_documents": vector_store.texts, "question": message})
     response = result["result"]
 
     history.append((message, response))