Local LLM AI ChatBot on Arduino UNO Q

Build Your Own Private AI Assistant Without Internet An local LLM AI chatbot which can also run when offline that was built by installing a small LLM on Arduino UNO Q.

Feb 26, 2026

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Components and supplies

Robu's Arduino UNO Q

USB C

Apps and platforms

Arduino App Lab

Project description

Code

Offline LLM chatbot code

python

nano.py file

1from flask import Flask, request, jsonify, render_template_string
2import requests
3
4app = Flask(__name__)
5
6LLM_URL = "http://localhost:8080/v1/chat/completions"
7
8HTML = """
9<!DOCTYPE html>
10<html>
11<head>
12    <title>SmolLM Chat</title>
13    <style>
14        body { font-family: Arial; background:#111; color:#eee; }
15        #chat { width:80%; margin:auto; margin-top:30px; }
16        .msg { padding:10px; margin:10px 0; border-radius:10px; }
17        .user { background:#2a2a2a; text-align:right; }
18        .bot { background:#1e3a5f; }
19        input { width:80%; padding:10px; }
20        button { padding:10px; }
21    </style>
22</head>
23<body>
24<div id="chat">
25    <h2>SmolLM2 Web Chat</h2>
26    <div id="messages"></div>
27    <input id="input" placeholder="Type message..." />
28    <button onclick="send()">Send</button>
29</div>
30
31<script>
32async function send() {
33    let input = document.getElementById("input");
34    let msg = input.value;
35    if (!msg) return;
36
37    let messages = document.getElementById("messages");
38    messages.innerHTML += `<div class='msg user'>${msg}</div>`;
39    input.value = "";
40
41    let response = await fetch("/chat", {
42        method: "POST",
43        headers: {"Content-Type": "application/json"},
44        body: JSON.stringify({message: msg})
45    });
46
47    let data = await response.json();
48    messages.innerHTML += `<div class='msg bot'>${data.reply}</div>`;
49}
50</script>
51</body>
52</html>
53"""
54
55def ask_llm(prompt):
56    payload = {
57        "model": "smollm2",
58        "messages": [
59            {"role": "user", "content": prompt}
60        ],
61        "max_tokens": 200
62    }
63    r = requests.post(LLM_URL, json=payload)
64    return r.json()["choices"][0]["message"]["content"]
65
66@app.route("/")
67def index():
68    return render_template_string(HTML)
69
70@app.route("/chat", methods=["POST"])
71def chat():
72    user_msg = request.json["message"]
73    reply = ask_llm(user_msg)
74    return jsonify({"reply": reply})
75
76if __name__ == "__main__":
77    app.run(host="0.0.0.0", port=7000)

from flask import Flask, request, jsonify, render_template_string
import requests

app = Flask(__name__)

LLM_URL = "http://localhost:8080/v1/chat/completions"

HTML = """
<!DOCTYPE html>
<html>
<head>
    <title>SmolLM Chat</title>
    <style>
        body { font-family: Arial; background:#111; color:#eee; }
        #chat { width:80%; margin:auto; margin-top:30px; }
        .msg { padding:10px; margin:10px 0; border-radius:10px; }
        .user { background:#2a2a2a; text-align:right; }
        .bot { background:#1e3a5f; }
        input { width:80%; padding:10px; }
        button { padding:10px; }
    </style>
</head>
<body>
<div id="chat">
    <h2>SmolLM2 Web Chat</h2>
    <div id="messages"></div>
    <input id="input" placeholder="Type message..." />
    <button onclick="send()">Send</button>
</div>

<script>
async function send() {
    let input = document.getElementById("input");
    let msg = input.value;
    if (!msg) return;

    let messages = document.getElementById("messages");
    messages.innerHTML += `<div class='msg user'>${msg}</div>`;
    input.value = "";

    let response = await fetch("/chat", {
        method: "POST",
        headers: {"Content-Type": "application/json"},
        body: JSON.stringify({message: msg})
    });

    let data = await response.json();
    messages.innerHTML += `<div class='msg bot'>${data.reply}</div>`;
}
</script>
</body>
</html>
"""

def ask_llm(prompt):
    payload = {
        "model": "smollm2",
        "messages": [
            {"role": "user", "content": prompt}
        ],
        "max_tokens": 200
    }
    r = requests.post(LLM_URL, json=payload)
    return r.json()["choices"][0]["message"]["content"]

@app.route("/")
def index():
    return render_template_string(HTML)

@app.route("/chat", methods=["POST"])
def chat():
    user_msg = request.json["message"]
    reply = ask_llm(user_msg)
    return jsonify({"reply": reply})

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=7000)

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