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| 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("OPENAI_API_KEY"), 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 online course. Powered by ChatGPT.", | |
| theme="compact") | |
| iface.launch(inline=True) | |