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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import Required Libraries"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import gradio as gr "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load Environment Variables"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"load_dotenv()\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"if not openai_api_key:\n",
" print(\"OpenAI API Key not set\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Initialize OpenAI Client and Define Model"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"openai = OpenAI()\n",
"MODEL = 'gpt-4o-mini'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define the System Message"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"system_message = (\n",
" \"You are a helpful assistant, trying your best to answer every question as accurately as possible. \"\n",
" \"You are also free to say you do not know if you do not have the information to answer a question. \"\n",
" \"You always respond in markdown.\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define the Chat Function"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"def chat(message, history):\n",
" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
"\n",
" stream = openai.chat.completions.create(model=MODEL, messages=messages, stream=True)\n",
"\n",
" response = \"\"\n",
" for chunk in stream:\n",
" response += chunk.choices[0].delta.content or ''\n",
" yield response"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create the Chat Interface"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"demo = gr.ChatInterface(\n",
" fn=chat,\n",
" title=\"AI chatbot\",\n",
" description=\"Please login to use the chat interface\",\n",
" type='messages',\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"auth_data is a list of tuples, where each tuple contains a username and password."
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"auth_data = [(\"user_1\", \"password_1\"), (\"user_2\", \"password_2\"), (\"user_3\", \"password_3\")]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Add Authentication and Launch\n",
"\n",
"auth_message is the message displayed to users before accessing the interface."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"demo.launch(share=True,\n",
" auth=auth_data,\n",
" auth_message=\"Please enter your credentials to access the chat interface\",\n",
")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "llms",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 4
}