Finalised TXT and PDF files

app.py

@@ -22,7 +22,6 @@ from langchain.document_loaders import UnstructuredPDFLoader
 
 os.environ["HUGGINGFACEHUB_API_TOKEN"] = st.secrets["hf_api_key"]
 
-
 def init():
 	global embeddings, llm, llm2, chain
 	# Embeddings
@@ -102,32 +101,4 @@ with st.sidebar.expander('File'):
     if (uploaded_file):
 	    st.info(uploaded_file.name)
 if os.path.exists('/content/'):
-	st.info(os.listdir('/content/'))
-
-
-# # PDFs
-# # !wget https://github.com/fabiomatricardi/cdQnA/raw/main/PLC_mediumArticle.pdf
-# # !wget https://github.com/fabiomatricardi/cdQnA/raw/main/BridgingTheGaap_fromMedium.pdf
-# # !mkdir pdfs
-# # !cp *pdf '/content/pdfs'
-
-# # pdf_folder_path = '/content/pdfs'
-# # os.listdir(pdf_folder_path)
-
-# # loaders = [UnstructuredPDFLoader(os.path.join(pdf_folder_path, fn)) for fn in os.listdir(pdf_folder_path)]
-# # loaders
-
-# index = VectorstoreIndexCreator(
-#     embedding=HuggingFaceEmbeddings(),
-#     text_splitter=CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)).from_loaders(loaders)
-
-# #Load llm with selected one
-# llm2=HuggingFaceHub(repo_id="declare-lab/flan-alpaca-large", model_kwargs={"temperature":0, "max_length":512})
-# #Prepare the pipeline
-# from langchain.chains import RetrievalQA
-# chain = RetrievalQA.from_chain_type(llm=llm2,
-#                                     chain_type="stuff",
-#                                     retriever=index.vectorstore.as_retriever(),
-#                                     input_key="question")
-# #get reply to our questions
-# # chain.run('What is the difference between a PLC and a PC?')
+	st.info(os.listdir('/content/'))