Update app.py

app.py

@@ -1,11 +1,23 @@
 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
-g=open('test.txt')
-g1=g.read()
-loader = TextLoader('test.txt')
+embeddings = HuggingFaceEmbeddings()
+Gita=open('Gita.txt')
+loader = TextLoader(Gita)
 documents = loader.load()
+text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+docs = text_splitter.split_text(documents)
+qdrant = Qdrant.from_documents(
+    docs, embeddings, 
+    location=":memory:",
+    collection_name="my_documents",)
 def answer(query):
-    out=g1
-    return out
+  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()