add fastapi for ml

Chapter5/.gitignore

@@ -4,4 +4,5 @@
 .env
 delta_lake*
 employees
-mlruns
+mlruns
+*.joblib

Chapter5/machine_learning.ipynb

@@ -2826,6 +2826,232 @@
    "source": [
     "[Learn more about MLFlow Models](https://bit.ly/46y6gpF)."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8acadfca",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "!pip3 install joblib \"fastapi[standard]\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e815f01b",
+   "metadata": {},
+   "source": [
+    "Imagine this scenario: You have just built a machine learning (ML) model with great performance, and you want to share this model with your team members so that they can develop a web application on top of your model.\n",
+    "\n",
+    "One way to share the model with your team members is to save the model to a file (e.g., using pickle, joblib, or framework-specific methods) and share the file directly\n",
+    "\n",
+    "\n",
+    "```python\n",
+    "import joblib\n",
+    "\n",
+    "model = ...\n",
+    "\n",
+    "# Save model\n",
+    "joblib.dump(model, \"model.joblib\")\n",
+    "\n",
+    "# Load model\n",
+    "model = joblib.load(model)\n",
+    "```\n",
+    "\n",
+    "However, this approach requires the same environment and dependencies, and it can pose potential security risks.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b364f9fc",
+   "metadata": {},
+   "source": [
+    "An alternative is creating an API for your ML model. APIs define how software components interact, allowing:\n",
+    "\n",
+    "1. Access from various programming languages and platforms\n",
+    "2. Easier integration for developers unfamiliar with ML or Python\n",
+    "3. Versatile use across different applications (web, mobile, etc.)\n",
+    "\n",
+    "This approach simplifies model sharing and usage, making it more accessible for diverse development needs.\n",
+    "\n",
+    "Let's learn how to create an ML API with FastAPI, a modern and fast web framework for building APIs with Python. \n",
+    "\n",
+    "Before we begin constructing an API for a machine learning model, let's first develop a basic model that our API will use. In this example, we'll create a model that predicts the median house price in California."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d7ea435d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean squared error: 0.56\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['lr.joblib']"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import fetch_california_housing\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "import joblib\n",
+    "\n",
+    "# Load dataset\n",
+    "X, y = fetch_california_housing(as_frame=True, return_X_y=True)\n",
+    "\n",
+    "# Split dataset into training and test sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X, y, test_size=0.2, random_state=42\n",
+    ")\n",
+    "\n",
+    "# Initialize and train the logistic regression model\n",
+    "model = LinearRegression()\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "# Predict and evaluate the model\n",
+    "y_pred = model.predict(X_test)\n",
+    "mse = mean_squared_error(y_test, y_pred)\n",
+    "print(f\"Mean squared error: {mse:.2f}\")\n",
+    "\n",
+    "# Save model\n",
+    "joblib.dump(model, \"lr.joblib\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5aaad8e",
+   "metadata": {},
+   "source": [
+    "Once we have our model, we can create an API for it using FastAPI. We'll define a POST endpoint for making predictions and use the model to make predictions.\n",
+    "\n",
+    "Here's an example of how to create an API for a machine learning model using FastAPI:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "581f789f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting ml_app.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile ml_app.py\n",
+    "from fastapi import FastAPI\n",
+    "import joblib\n",
+    "import pandas as pd \n",
+    "\n",
+    "# Create a FastAPI application instance\n",
+    "app = FastAPI()\n",
+    "\n",
+    "# Load the pre-trained machine learning model\n",
+    "model = joblib.load(\"lr.joblib\")\n",
+    "\n",
+    "# Define a POST endpoint for making predictions\n",
+    "@app.post(\"/predict/\")\n",
+    "def predict(data: list[float]):\n",
+    "    # Define the column names for the input features\n",
+    "    columns = [\n",
+    "        \"MedInc\",\n",
+    "        \"HouseAge\",\n",
+    "        \"AveRooms\",\n",
+    "        \"AveBedrms\",\n",
+    "        \"Population\",\n",
+    "        \"AveOccup\",\n",
+    "        \"Latitude\",\n",
+    "        \"Longitude\",\n",
+    "    ]\n",
+    "    \n",
+    "    # Create a pandas DataFrame from the input data\n",
+    "    features = pd.DataFrame([data], columns=columns)\n",
+    "    \n",
+    "    # Use the model to make a prediction\n",
+    "    prediction = model.predict(features)[0]\n",
+    "    \n",
+    "    # Return the prediction as a JSON object, rounding to 2 decimal places\n",
+    "    return {\"price\": round(prediction, 2)}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34aba2f2",
+   "metadata": {},
+   "source": [
+    "To run your FastAPI app for development, use the `fastapi dev` command:\n",
+    "```bash\n",
+    "$ fastapi dev ml_app.py\n",
+    "``` "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "375b4bce",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "!fastapi dev ml_app.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3fff7352",
+   "metadata": {},
+   "source": [
+    "This will start the development server and open the API documentation in your default browser.\n",
+    "\n",
+    "You can now use the API to make predictions by sending a POST request to the `/predict/` endpoint with the input data. For example:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8fb49c7c",
+   "metadata": {},
+   "source": [
+    "Running this cURL command on your terminal:\n",
+    "```bash\n",
+    "curl -X 'POST' \\\n",
+    "  'http://127.0.0.1:8000/predict/' \\\n",
+    "  -H 'accept: application/json' \\\n",
+    "  -H 'Content-Type: application/json' \\\n",
+    "  -d '[\n",
+    "  1.68, 25, 4, 2, 1400, 3, 36.06, -119.01\n",
+    "]'\n",
+    "```\n",
+    "This will return the predicted price as a JSON object, rounded to 2 decimal places:\n",
+    "```python\n",
+    "{\"price\":1.51}\n",
+    "```"
+   ]
   }
  ],
  "metadata": {

Chapter5/ml_app.py

@@ -0,0 +1,33 @@
+from fastapi import FastAPI
+import joblib
+import pandas as pd 
+
+# Create a FastAPI application instance
+app = FastAPI()
+
+# Load the pre-trained machine learning model
+model = joblib.load("lr.joblib")
+
+# Define a POST endpoint for making predictions
+@app.post("/predict/")
+def predict(data: list[float]):
+    # Define the column names for the input features
+    columns = [
+        "MedInc",
+        "HouseAge",
+        "AveRooms",
+        "AveBedrms",
+        "Population",
+        "AveOccup",
+        "Latitude",
+        "Longitude",
+    ]
+
+    # Create a pandas DataFrame from the input data
+    features = pd.DataFrame([data], columns=columns)
+
+    # Use the model to make a prediction
+    prediction = model.predict(features)[0]
+
+    # Return the prediction as a JSON object, rounding to 2 decimal places
+    return {"price": round(prediction, 2)}