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// This chatbot app will use a simple React frontend to display messages and allow user input.
// The backend will use OpenAI's GPT model to generate responses.
// We'll use SQLite to store conversation history.
/** @jsxImportSource https://esm.sh/react */
import React, { useState, useEffect } from "https://esm.sh/react";
import { createRoot } from "https://esm.sh/react-dom/client";
function App() {
const [messages, setMessages] = useState([]);
const [input, setInput] = useState("");
useEffect(() => {
fetchMessages();
}, []);
const fetchMessages = async () => {
const response = await fetch("/messages");
const data = await response.json();
setMessages(data);
};
const sendMessage = async (e) => {
e.preventDefault();
if (!input.trim()) return;
const userMessage = { role: "user", content: input };
setMessages([...messages, userMessage]);
setInput("");
const response = await fetch("/chat", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({ message: input }),
});
const data = await response.json();
setMessages([...messages, userMessage, data]);
};
return (
<div className="chat-container">
<div className="messages">
{messages.map((msg, index) => (
<div key={index} className={`message ${msg.role}`}>
{msg.content}
</div>
))}
</div>
<form onSubmit={sendMessage} className="input-form">
<input
type="text"
value={input}
onChange={(e) => setInput(e.target.value)}
placeholder="Type your message..."
/>
<button type="submit">Send</button>
</form>
</div>
);
}
function client() {
createRoot(document.getElementById("root")).render(<App />);
}
if (typeof document !== "undefined") { client(); }
async function server(request: Request): Promise<Response> {
const Cerebras = await import("https://esm.sh/@cerebras/cerebras_cloud_sdk");
const { sqlite } = await import("https://esm.town/v/stevekrouse/sqlite");
const SCHEMA_VERSION = 1;
const KEY = new URL(import.meta.url).pathname.split("/").at(-1);
await sqlite.execute(`
CREATE TABLE IF NOT EXISTS ${KEY}_messages_${SCHEMA_VERSION} (
id INTEGER PRIMARY KEY AUTOINCREMENT,
role TEXT NOT NULL,
content TEXT NOT NULL,
timestamp DATETIME DEFAULT CURRENT_TIMESTAMP
)
`);
const url = new URL(request.url);
if (url.pathname === "/messages") {
const messages = await sqlite.execute(`SELECT role, content FROM ${KEY}_messages_${SCHEMA_VERSION} ORDER BY timestamp ASC`);
return new Response(JSON.stringify(messages.rows), {
headers: { "Content-Type": "application/json" },
});
} else if (url.pathname === "/chat" && request.method === "POST") {
const { message } = await request.json();
await sqlite.execute(`INSERT INTO ${KEY}_messages_${SCHEMA_VERSION} (role, content) VALUES (?, ?)`, ["user", message]);
const client = new Cerebras.default({
apiKey: Deno.env.get('CEREBRAS_API_KEY'),
});
const completion = await client.chat.completions.create({
messages: [
{ role: "system", content: "You are a helpful AI assistant." },
{ role: "user", content: message }
],