app.py

import os
import gradio as gr
# import openai
from openai import OpenAI
from dotenv import load_dotenv
from pathlib import Path

load_dotenv()
key = os.getenv('OPENAI_API_KEY')
users = os.getenv('LOGNAME')
unames = users.split(',')
pwds = os.getenv('PASSWORD')
pwdList = pwds.split(',')

site = os.getenv('SITE')
if site == 'local':
    dp = Path('./data')
    dp.mkdir(exist_ok=True)
    dataDir = './data/'
else:
    dp = Path('/data')
    dp.mkdir(exist_ok=True)
    dataDir = '/data/'

client = OpenAI(api_key = key)

def genUsageStats(do_reset=False):
    result = []
    for user in unames:
        total = 0
        fp = dataDir + user + '_log.txt'
        if os.path.exists(fp):
            with open(fp) as f:
                dataList = f.readlines()
            if do_reset:
                os.remove(fp)
            for line in dataList:
                (u, c) = line.split(':')
                total += int(c)
        else:
            total = 0
        result.append([user, str(total)])
            
    return result       

def clear():
    return [None, [], None]

def updatePassword(txt):
    return [txt.lower(), "*********"]

def setModel(val):
    return val

def chat(prompt, user_window, pwd_window, past, response, gptModel):
    user_window = user_window.lower()
    if user_window == unames[0] and pwd_window == pwdList[0]:
        if prompt == 'stats':
            response = genUsageStats()
            return [past, response, None]
        if prompt == 'reset':
            response = genUsageStats(True)
            return [past, response, None]
    if user_window in unames and pwd_window in pwdList:
        past.append({"role":"user", "content":prompt})
        completion = client.chat.completions.create(model=gptModel,
                                            messages=past)
        reply = completion.choices[0].message.content
        response += "\n\nYOU: " + prompt + "\nGPT: " + reply  # + "\nModel = " + gptModel
        if len(response) > 40000:
            response += "\n\nTHIS DIALOG IS GETTING TOO LONG. PLEASE CLEAR IT."
        past.append({"role":"assistant", "content": reply})
        try:
            dataFile = dataDir + user_window + '_log.txt'
            with open(dataFile, 'a') as f:
                f.write(f'{user_window}: {len(response)}\n')
#            with open(dataFile) as f:
#                response += '\n' + f.read()
        except Exception as e:
            response += f"\nDATA LOG FAILED, path = {dataFile}\nmsg = {e}"
        # response += f'\npPath = {p.absolute()}'
        # response += '\nUsers = ' + ','.join(unames)
        return [past, response , None]
    else:
        return [[], "User name and/or password are incorrect", prompt]

with gr.Blocks() as demo:
    history = gr.State([])
    password = gr.State("")
    model = gr.State("gpt-3.5-turbo")
    gr.Markdown('# GPT Chat')
    gr.Markdown('Enter user name & password then enter prompt and click submit button.  GPT 3.5 is cheaper but GPT 4o may perform better.  Restart conversation if topic changes')
    # heading = gr.Label(value="GPT Chat", scale=2, color="Crimson" )
    with gr.Row():
        user_window = gr.Textbox(label = "User Name")
        pwd_window = gr.Textbox(label = "Password")
        pwd_window.blur(updatePassword, pwd_window, [password, pwd_window])
    with gr.Row():
        clear_button = gr.Button(value="Restart Conversation")
        gpt_chooser=gr.Radio(choices=[("GPT-3.5","gpt-3.5-turbo"),("GPT-4o","gpt-4o-mini")],
                             value="gpt-3.5-turbo", label="GPT Model", interactive=True)
        submit_window = gr.Button(value="Submit Prompt/Question")
    prompt_window = gr.Textbox(label = "Prompt or Question")
    output_window = gr.Textbox(label = "Dialog")
    submit_window.click(chat, inputs=[prompt_window, user_window, password, history, output_window, model],
                         outputs=[history, output_window, prompt_window])
    clear_button.click(clear, inputs=[], outputs=[prompt_window, history, output_window])
    gpt_chooser.input(fn=setModel, inputs=gpt_chooser, outputs=model)
demo.launch()