'''
import subprocess
subprocess.check_call(["pip", "install", "-q", "openai"])
subprocess.check_call(["pip", "install", "-q", "gradio", "transformers", "python-dotenv"])
import gradio as gr
from transformers import TFAutoModelForCausalLM, AutoTokenizer
import openai
from dotenv import load_dotenv
import os

load_dotenv() # load environment variables from .env file
api_key = os.getenv("OPENAI_API_KEY") # access the value of the OPENAI_API_KEY environment variable

def openai_chat(prompt):
    if "who are you" in prompt.lower() or "your name" in prompt.lower() or "name" in prompt.lower():
        return "My name is ChatSherman. How can I assist you today?"
    else:
        prompt = "I'm an AI chatbot named ChatSherman designed by a student named ShermanAI at the Department of Electronic and Information Engineering at The Hong Kong Polytechnic University to help you with your engineering questions. Also, I can assist with a wide range of topics and questions." + prompt
        completions = openai.Completion.create(engine="text-davinci-003", prompt=prompt, max_tokens=1024, n=1, temperature=0.5,)
        message = completions.choices[0].text
        return message.strip()

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

title = "ChatSherman"
description = "This is an AI chatbot powered by ShermanAI. Enter your question below to get started."
examples = [
    ["What is ChatSherman, and how does it work?", []],
    ["Is my personal information and data safe when I use the ChatSherman chatbot?", []],
    ["What are some common applications of deep learning in engineering?", []]
]
inputs = [gr.inputs.Textbox(label="Talk to ChatSherman: "), "state"]
outputs = ["chatbot", "state"]
interface = gr.Interface(fn=chatbot, inputs=inputs, outputs=outputs, title=title, description=description, examples=examples)
interface.launch(debug=True)
'''
import openai
import gradio as gr

openai.api_key = "OPENAI_API_KEY"  

def predict(message, history):
    history_openai_format = []
    for human, assistant in history:
        history_openai_format.append({"role": "user", "content": human })
        history_openai_format.append({"role": "assistant", "content":assistant})
    history_openai_format.append({"role": "user", "content": message})

    response = openai.ChatCompletion.create(
        model='gpt-3.5-turbo',
        messages= history_openai_format,
        temperature=1.0,
        stream=True
    )

    partial_message = ""
    for chunk in response:
        if len(chunk['choices'][0]['delta']) != 0:
            partial_message = partial_message + chunk['choices'][0]['delta']['content']
            yield partial_message

gr.ChatInterface(predict).queue().launch()