import gradio as gr
from langchain.vectorstores import Qdrant
from langchain.docstore.document import Document
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.document_loaders import TextLoader
embeddings = HuggingFaceEmbeddings()
Gita=open('Gita.txt')
loader = TextLoader('Gita.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
qdrant = Qdrant.from_documents(
    docs, embeddings, 
    location=":memory:",
    collection_name="my_documents",)
def answer(query):
  out = qdrant.similarity_search_with_score(query)
  out1=out[0]
  out2=out1[0].page_content
  return out2
demo = gr.Interface(fn=answer, inputs='text',outputs='text',examples=[['song celestial']])
demo.launch()