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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')
ChurnData2 = os.environ.get('Churn_Data2')
#read data
from llama_index import VectorStoreIndex, SimpleDirectoryReader, SummaryIndex, download_loader
DataReader = download_loader(Data_Read)
loader = DataReader()
### 1st file
documents = loader.load_data(file=ChurnData)
### 1st file
### 2nd file
documents2 = loader.load_data(file=ChurnData2)
documents = documents + documents2
### 2nd file
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="70vh",label="Conversation"), retry_btn=None,theme=gr.themes.Monochrome(),
title = 'E-Commerce BT/AN/CA/DH/VE/GA CMS Q&A', undo_btn = None, clear_btn = None, css='footer {visibility: hidden}').launch() |