Create app.py

app.py

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+import gradio as gr
+from langchain.vectorstores import Chroma
+docsearch = Chroma.from_texts(texts, embeddings,persist_directory=persist_directory)
+def answer(question):
+  out=chain.run(question)
+  return out
+demo = gr.Interface(fn=answer, inputs='text',outputs='text',examples=[['What is the capital of India ?']])
+demo.launch()
+from langchain.docstore.document import Document
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.text_splitter import CharacterTextSplitter
+embeddings = HuggingFaceEmbeddings()
+with open('Gita full.txt') as f:
+    gita = f.read()
+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()