# Import dependencies
import gradio as gr
"""import os
from llama_index import GPTVectorStoreIndex
from langchain.prompts.prompt import PromptTemplate
from langchain.llms import OpenAI
from langchain.chains import ChatVectorDBChain
from query_data import get_chain
from response import get_response"""


       
# Execute the chat functionality.           
 #       output = chain({"message": inp, "chat_history": history})["response"]        
 #       history.append((inp, output))     
 #       return history

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    
  gr.HTML("<center><h2>Omdena AI Chatbot For Mental Health and Wellbeing</h2></center>")
    
  gr.HTML("WELCOME<br>"
            "I am an AI ChatBot and I am here to assist you with whatever is bothering you. "
            "Our conversation is strictly confidential and I will not remember it when you come back another time."     
        )

  chatbot = gr.Chatbot()  
  message = gr.Textbox(label="What would you like to chat about?")
  response = gr.Textbox

  def respond(message, chat_history):
    response = "Tell me more about that"
    chat_history.append((message, response))
    return "", chat_history
  
  with gr.Row():
    send = gr.Button(value="Send").style(full_width=False) 
    clear = gr.Button(value="Clear Chat").style(full_width=False)

  gr.Examples(
    examples=[
            "I feel lonely",
            "I'm having problems at home",
            "I am looking for some resources",
             ]
    inputs=message
  )
    
  send.click(respond, [message, chatbot], [message, chatbot])
  clear.click(lambda: None, None, chatbot, queue=False)          

    
if __name__ == "__main__":
    demo.launch(debug=True)