import subprocess
subprocess.check_call(["pip", "install", "--upgrade", "gradio"])
subprocess.check_call(["pip", "install", "-q", "openai"])
subprocess.check_call(["pip", "install", "-q", "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 predict(message, history):
    prompt = "I'm an AI chatbot named ChatSherman designed by a super-intelligent 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. I am now version 2.0, which is more powerful than version 1.0, able to do more complex tasks, and optimized for chat. "
    history = [(prompt, '')] + 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-16k-0613', #gpt-3.5-turbo-0301 faster  
        messages= history_openai_format,
        temperature=0.5,
        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

title = "ChatSherman-2.0"
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?", []]
]
gr.ChatInterface(predict, title=title, description=description, examples=examples).queue().launch(debug=True)