try:
    import gradio as gr
    import requests
    import json
    import langchain
    from langchain import Cohere
    import os
    from langchain.embeddings.cohere import CohereEmbeddings
    from langchain.vectorstores import Chroma
    from langchain.text_splitter import CharacterTextSplitter
    from langchain.llms import Cohere,OpenAI,HuggingFaceHub
    from langchain.chains import ChatVectorDBChain
    from langchain.text_splitter import RecursiveCharacterTextSplitter
    from langchain.chains.question_answering import load_qa_chain
except Exception as  ex:
    print(f'Some Error --> {ex}')

with open('kb.txt') as f:
    state_of_the_union = f.read()

embeddings = CohereEmbeddings()

text_splitter = RecursiveCharacterTextSplitter(
    chunk_size = 100,
    chunk_overlap  = 20,
    length_function = len,
)

texts = text_splitter.create_documents([state_of_the_union])

docsearch = Chroma.from_documents(texts, embeddings)

# llm=Cohere(model="command-xlarge-nightly",temperature=0.7,max_tokens=1000)
llm=OpenAI(temperature=0.7,max_tokens=1000)

print(f'Done with loading and Indexing')

def procdata(name):
    # query = "Name the negatively charged particles emitted from the cathode in the cathode ray tube?"
    # docs = docsearch.similarity_search(query)
    print(f'Start Processing ... --> {name}')
    # chain = load_qa_chain(llm, chain_type="refine")
    # response = chain({"input_documents": docs[:3], "question": query}, return_only_outputs=True)
    # result=response['output_text'].replace('\n','')
    # print(f"End Processing ... --> {result}")
    # return result
    return 'Done'

iface = gr.Interface(fn=procdata, inputs="text", outputs="text",enable_queue=True)
iface.launch()