app.py

import openai
import json
import gradio as gr
import os
from openai import OpenAI

#from llama_index import set_global_service_context
#set_global_service_context(service_context)

# retrieve secret key from HF settings
#openai.api_key = os.getenv("OPENAI_API_KEY")

# rebuild storage context and load knowledge index
from llama_index.core import StorageContext, load_index_from_storage
storage_context = StorageContext.from_defaults(persist_dir='./')
index = load_index_from_storage(storage_context)

class Chatbot:
    def __init__(self, api_key, index):
        self.index = index
        openai.api_key = api_key
 
    def generate_response(self, user_input):
        query_engine = index.as_query_engine()
        response = query_engine.query(user_input)

        message = {"role": "assistant", "content": response.response}
        return message

def create_bot(input):
    bot = Chatbot(os.getenv("sk-proj-izWrxKNpJzcpi7BLTAz1T3BlbkFJQKPvwCzS9GmWBwD0cPE3"), index=index)

    # use moderations endpoint to check input
    if input:
        client = OpenAI()
        response_mod = client.moderations.create(input=input)
        response_dict = response_mod.model_dump()
        flagged = response_dict['results'][0]['flagged']
        #print("Flagged:", flagged)
    
        if not flagged:
            response_bot = bot.generate_response(input)
            output = response_bot['content']
        else:
             output = "Invalid request."

        return output

inputs = gr.Textbox(lines=7, label="Ask questions related to the course.")
outputs = gr.Textbox(label="Reply")

iface = gr.Interface(fn=create_bot, inputs=inputs, outputs=outputs, title="Virtual TA",
             description="This is a prototype of learning assistant designed for marketing courses. Powered by ChatGPT 3.5.",
             theme="compact")

iface.launch(inline=True)