Update app.py

app.py

@@ -8,27 +8,32 @@ embeddings = SentenceTransformerEmbeddings(
     model_kwargs={"trust_remote_code": True}
 )
 
+print('Embeddings loaded successfully')
+
 from langchain_community.vectorstores import FAISS
 from langchain_text_splitters import RecursiveCharacterTextSplitter
 from langchain.document_loaders import TextLoader, PyPDFLoader
 
 loader = PyPDFLoader("https://www.versusarthritis.org/media/24901/fibromyalgia-information-booklet-july2021.pdf")
 documents = loader.load()
-
 text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
 docs = text_splitter.split_documents(documents)
 vector_store = FAISS.from_documents(docs, embeddings)
 retriever = vector_store.as_retriever()
 
+print('Retriever loaded successfully')
+
 from langchain import hub
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.runnables import RunnablePassthrough
 
 prompt = hub.pull("rlm/rag-prompt")
+print(prompt)
 
 from transformers import AutoTokenizer, AutoModelForCausalLM
 tokenizer = AutoTokenizer.from_pretrained("mohamedalcafory/PubMed_Llama3.1_Based_model")
 model = AutoModelForCausalLM.from_pretrained("mohamedalcafory/PubMed_Llama3.1_Based_model")
+print('Model loaded successfully')
 
 from transformers import pipeline
 from langchain_huggingface import HuggingFacePipeline