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#for learning
import os
import openai
import gradio as gr


openai.api_key =  os.environ.get('O_APIKey')
Data_Read =  os.environ.get('Data_Reader')
ChurnData =  os.environ.get('Churn_Data')

#read data
from llama_index import VectorStoreIndex, SimpleDirectoryReader, SummaryIndex, download_loader
DataReader = download_loader(Data_Read)
loader = DataReader()

#loading options
# loader = SimpleDirectoryReader(input_files = ["pdf1","pdf2"])
# documents = loader.load_data()
# or
# documents = loader.load_data(file=['toolkit.pdf','pdf2','pdf3'])

documents = loader.load_data(file=ChurnData)
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()

def reply(message, history):
  answer = str(query_engine.query(message))
  return answer

Conversing = gr.ChatInterface(reply, chatbot=gr.Chatbot(height=500), retry_btn=None,theme=gr.themes.Monochrome(),
                              title = 'E-Commerce And Digital Marketing Toolkit Q&A', undo_btn = None, clear_btn = None, css='footer {visibility: hidden}').launch()