Simple Webb GUI Ollama LLM

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Creating a web interface for a large language model (LLM) like Ministral-3-3B-Instruct-2512 involves several steps, including frontend development (UI/UX), backend integration, and API handling.
Below is a structured guide to building a simple yet functional web interface for your LLM.

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1. System Architecture Overview

A typical web interface for an LLM consists of:

1. Frontend (UI) – Handles user interaction (HTML, CSS, JavaScript).
2. Backend (API) – Processes LLM requests (Python/Flask/FastAPI, Node.js).
3. LLM Integration – Calls the model (via Hugging Face API, local inference).
4. Database (Optional) – Stores conversation history (if needed).

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2. Step-by-Step Implementation

Option A: Using a Local LLM (Ministral-3-3B-Instruct-2512)

Since you have the model locally, we’ll use FastAPI (Python) for the backend and React/Vue.js for the frontend.

Step 1: Install Required Libraries

pip install fastapi uvicorn python-multipart torch transformers

Step 2: Create a FastAPI Backend (app.py)

from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

app = FastAPI()

# Enable CORS (if frontend is on a different domain)
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # Adjust in production
    allow_methods=["*"],
    allow_headers=["*"],
)

# Load the model (replace with your path)
model_name = "mistralai/Mistral-3-3B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

@app.post("/generate")
async def generate_text(prompt: str):
    try:
        inputs = tokenizer(prompt, return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")
        outputs = model.generate(**inputs, max_new_tokens=100)
        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
        return {"response": response}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)

Step 3: Create a Simple Frontend (React Example)

Install React:

npx create-react-app llm-web-interface
cd llm-web-interface
npm install axios

Create src/App.js:

import React, { useState } from "react";
import axios from "axios";

function App() {
  const [prompt, setPrompt] = useState("");
  const [response, setResponse] = useState("");

  const handleSubmit = async (e) => {
    e.preventDefault();
    try {
      const res = await axios.post("http://localhost:8000/generate", { prompt });
      setResponse(res.data.response);
    } catch (err) {
      alert("Error generating response");
    }
  };

  return (
    <div style={{ padding: "20px", maxWidth: "600px", margin: "0 auto" }}>
      <h1>LLM Chat Interface</h1>
      <form onSubmit={handleSubmit}>
        <input
          type="text"
          value={prompt}
          onChange={(e) => setPrompt(e.target.value)}
          placeholder="Ask a question..."
        />
        <button type="submit">Send</button>
      </form>
      <div style={{ marginTop: "10px" }}>
        <h3>Response:</h3>
        <p>{response}</p>
      </div>
    </div>
  );
}

export default App;

Step 4: Run the Application

1. Start the backend:

   python app.py

2. Start the frontend:

   cd llm-web-interface
   npm start

3. Open http://localhost:3000 in your browser.

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3. Enhancements (Optional)

• Conversation History: Store chats in a database (SQLite, PostgreSQL).
• Themes & UI: Use TailwindCSS or Material-UI for better styling.
• Multi-Language Support: Add language detection.
• Real-Time Updates: Use WebSockets (FastAPI + Socket.IO).
• Deployment: Host on Render, Railway, or AWS.

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4. Security Considerations

• Rate Limiting: Prevent abuse (e.g., Flask-Limiter).
• Input Sanitization: Avoid SQL injection/XXE attacks.
• HTTPS: Always use HTTPS in production.
• Authentication: Restrict API access (JWT/OAuth).

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Conclusion

This guide provides a basic but functional web interface for your LLM. You can extend it based on your needs (e.g., adding more
features, improving UI, or deploying it on the cloud).