Clean up notebook to reduce file sizes

week8/community_contributions/lisekarimi/10_part2_modal.ipynb

@@ -24,7 +24,8 @@
     "\n",
     "---\n",
     "📢 Find more LLM notebooks on my [GitHub repository](https://github.com/lisekarimi/lexo)\n"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -42,7 +43,8 @@
     "- 🧑‍💻 Local vs Remote code execution:\n",
     "    - Local code runs on your machine\n",
     "    - Remote code runs in the cloud (via Modal"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -53,7 +55,8 @@
     "- Before starting, install Modal in your environment (Run this once): `uv pip install modal`\n",
     "- Create an account at modal.com (they give you $5 free to start).\n",
     "- Then authenticate your environment: `modal setup`"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
@@ -101,7 +104,8 @@
     "- `modal.App`: Defines and registers the Modal app\n",
     "- `.remote()`: Sends request to Modal API to execute the code remotely\n",
     "- `modal deploy -m`: Deploys app permanently like a real cloud service"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -109,7 +113,8 @@
    "metadata": {},
    "source": [
     "## ⚙️ Minimal Example"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
@@ -148,7 +153,8 @@
     "What we have here is an **ephemeral app**: the container shuts down after finishing.\n",
     "\n",
     "For our project, we need a persistently running app that behaves like a production API. To achieve that, we should use `modal deploy -m`, making the app suitable for serving AI services reliably."
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -156,7 +162,8 @@
    "metadata": {},
    "source": [
     "## 📦 Persistent Deployment with `modal deploy`"
-   ]
+   ],
+   "outputs": []
   },
   {
    "attachments": {
@@ -180,7 +187,8 @@
     "This builds and deploys the app (`example-hello-world`), registers `f()`, and makes it callable via `.remote()` anytime — even outside the notebook.\n",
     "\n",
     "![image.png](attachment:b84a3557-9805-462f-a1d5-008b3aa4f4f5.png)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
@@ -239,7 +247,8 @@
     "- Prepare the app to handle `.remote()` calls when they come in\n",
     "\n",
     "![image.png](attachment:b950fed1-8806-424c-830a-d8b99927801e.png)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "attachments": {
@@ -279,7 +288,8 @@
     "To save credits, you can set `min_containers=0` and `scaledown_window=300` — this way, no container stays warm by default, and a new one will spin up only when `.remote()` is called (i.e., on cold start).\n",
     "\n",
     "![image.png](attachment:4a22e438-6b25-4c69-9439-99d146ffd188.png)\n"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
@@ -303,7 +313,8 @@
     "let’s go one step further — wrap this logic inside a local Python class.\n",
     "\n",
     "In the next step, we'll build a local Agent that cleanly interacts with our deployed `Modal app`, using the same `Modal API` under the hood."
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -324,7 +335,8 @@
     "2. `.price.remote(...)` → runs the remote method on Modal  \n",
     "\n",
     "This keeps our code clean and modular."
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
@@ -347,7 +359,8 @@
     "Now that we’ve seen how Modal agents work — connecting to remote services and running `.remote()` — we’ll use the same pattern for the rest of our models.\n",
     "\n",
     "✅ For each model — **XGBoost**, **GPT-4o RAG**, and the **Ensemble** — we’ll build a dedicated Agent.  "
-   ]
+   ],
+   "outputs": []
   }
  ],
  "metadata": {