#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()

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

### 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=500), retry_btn=None,theme=gr.themes.Monochrome(),
                              title = 'ECommerce And Digital Marketing 2024 Toolkit Q&A', undo_btn = None, clear_btn = None, css='footer {visibility: hidden}').launch()