app.py

import openai
import gradio as gr
import time
import os
openai.api_key = os.getenv("OPENAI_API_KEY")

def get_completion(prompt, model="gpt-3.5-turbo"):
    messages = [{"role": "user", "content": prompt}]
    response = openai.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=0, # this is the degree of randomness of the model's output
    )
    return response.choices[0].message["content"]



def get_completion_from_messages(input, model="gpt-3.5-turbo", temperature=0.8):
    messages = [
        {'role': 'system', 'content': '너는 자기소개서에 기반하여 질문을 하는 면접관이야.\
                                     만약 전문용어가 있다면 꼬리질문해줘'},
        {
            "role": "user",
            "content": input
        }
    ]
    response = openai.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=temperature, # this is the degree of randomness of the model's output
    )
#     print(str(response.choices[0].message))
    return response.choices[0].message["content"]






####
#user input
#get completion 통과 시켜서 답변얻음
#이때 역할 분담 및 프롬프트 엔지니어링 진행
####
class ChatBot:
    def __init__(self):
        # Initialize the ChatBot class with an empty history
        self.history = []

    def predict(self, user_input):

        response_text =get_completion_from_messages(user_input, temperature=0.8)

        return response_text  # Return the generated response


bot = ChatBot()

title = "자소서기반 면접 시뮬레이션 chat bot (this template based on Tonic's MistralMed Chat)"
#description = "이 공간을 사용하여 현재 모델을 테스트할 수 있습니다. [(Tonic/MistralMed)](https://huggingface.co/Tonic/MistralMed) 또는 이 공간을 복제하고 로컬 또는 🤗HuggingFace에서 사용할 수 있습니다. [Discord에서 함께 만들기 위해 Discord에 가입하십시오](https://discord.gg/VqTxc76K3u). You can use this Space to test out the current model [(Tonic/MistralMed)](https://huggingface.co/Tonic/MistralMed) or duplicate this Space and use it locally or on 🤗HuggingFace. [Join me on Discord to build together](https://discord.gg/VqTxc76K3u)."
#examples = [["[Question:] What is the proper treatment for buccal herpes?",
#             "You are a medicine and public health expert, you will receive a question, answer the question, and provide a complete answer"]]

iface = gr.Interface(
    fn=bot.predict,
    title=title,
    inputs=["text"],  # Take user input and system prompt separately
    outputs="text",
    theme="ParityError/Anime"
)

iface.launch()