app.py

import gradio as gr
from langchain.vectorstores import Chroma
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()
g=open('Gita.txt')
Gita=g.read()
#loader = TextLoader('Gita.txt')
#documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_text(Gita)
docsearch = Chroma.from_texts(texts, embeddings)
def answer(query):
  docs = docsearch.similarity_search(query)
  out=docs[0].page_content
  return out
demo = gr.Interface(fn=answer, inputs='text',outputs='text',examples=[['song celestial']])
demo.launch()