Load a small local model [chapter] deterministic

generator = pipeline("text-generation", model="distilgpt2") def generate_response(category: str, user_message: str) -> str: if category == "support": prompt = f"Support response for: {user_message}" elif category == "technical": prompt = f"Technical answer for: {user_message}" else: prompt = f"General response for: {user_message}" result = generator(prompt, max_length=200, do_sample=True) return result[0]["generated_text"]

`

We would then call generate_response in the view after classification. The full flow would be: 1. Receive message via POST. 2. Run classifier with **CLASSIFIER_SYSTEM_PROMPT** (simulated here). 3. Based on category, invoke the appropriate model. 4. Return the generated text to the frontend. The frontend would then append the 's response to the **Conversation History** and display it. This completes the end‑to‑end AI feature pipeline.

Deploying Local‑First AI Applications Local‑first deployment means that all data and compute reside on the developer's machine or on‑premises servers. This is crucial for privacy, latency, and cost control. We will use Docker to containerize both the Django backend and the Next.js frontend, and we will run them behind a simple reverse proxy (nginx) for HTTPS and routing. First, create a Dockerfile for Django.

```dockerfile