Update app.py

app.py

@@ -3,27 +3,37 @@ import gradio as gr
 from langchain.document_loaders import OnlinePDFLoader
 
 from langchain.text_splitter import CharacterTextSplitter
-text_splitter = CharacterTextSplitter(chunk_size=350, chunk_overlap=0)
 
 from langchain.llms import HuggingFaceHub
-flan_ul2 = HuggingFaceHub(repo_id="google/flan-ul2", model_kwargs={"temperature":0.1, "max_new_tokens":300})
 
 from langchain.embeddings import HuggingFaceHubEmbeddings
-embeddings = HuggingFaceHubEmbeddings()
 
 from langchain.vectorstores import Chroma
 
 from langchain.chains import RetrievalQA
+
+global llm 
+
+def define_llm_model(repo_id):
+    llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={"temperature":0.1, "max_new_tokens":300})
+    return "LLM model loaded"
+
+define_llm_model("google/flan-ul2")
+
 def loading_pdf():
     return "Loading..."
+
 def pdf_changes(pdf_doc):
+    
     loader = OnlinePDFLoader(pdf_doc.name)
     documents = loader.load()
+    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
     texts = text_splitter.split_documents(documents)
+    embeddings = HuggingFaceHubEmbeddings()
     db = Chroma.from_documents(texts, embeddings)
     retriever = db.as_retriever()
     global qa 
-    qa = RetrievalQA.from_chain_type(llm=flan_ul2, chain_type="stuff", retriever=retriever, return_source_documents=True)
+    qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True)
     return "Ready"
 
 def add_text(history, text):