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# 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)
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