import openai
import gradio as gr
from gradio import HuggingFaceDatasetSaver
openai.api_key ='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'

def openai_chat(prompt):
    completions = openai.Completion.create(
        engine="text-davinci-003",
        prompt=prompt+"The following is the prompt from teacher working in canvas infrastructure",
        max_tokens=1024,
        n=1,
        temperature=0.5,
        frequency_penalty=0,
        presence_penalty=0.6,
        stop=[" Human:", " AI:"]
    )

    message = completions.choices[0].text
    return message.strip()

def chatbot(input, history=[]):
    output = openai_chat(input)
    history.append((input, output))
    return history, history

gr.Interface(fn = chatbot,
             inputs = ["text",'state'],
             outputs = ["chatbot",'state'],
             examples=[["Building a translation demo with Gradio is so easy!", "eng_Latn", "spa_Latn"]],
             cache_examples=False,
             title="Demo app",
            allow_flagging="manual").launch()