Live demonstration of building an ML web application with FastAPI

In this tutorial, we will walk through the process of creating a Machine Learning (ML) web application using FastAPI. FastAPI is a modern web framework for building APIs with Python that is fast and easy to use. By the end of this tutorial, you will have a basic web application that can receive input data, run it through an ML model, and return the prediction to the user.

To get started, make sure you have Python installed on your machine. You can install FastAPI and other required libraries by running the following command in your terminal:

pip install fastapi uvicorn numpy pandas scikit-learn

Next, create a new Python file called app.py and import the necessary libraries:

from fastapi import FastAPI
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from pydantic import BaseModel

Now, create an instance of the FastAPI class:

app = FastAPI()

Next, let’s define a class called InputData which will act as the input data model for our ML model:

class InputData(BaseModel):
    feature1: float
    feature2: float
    feature3: float

Now, let’s define a dummy ML model that we will use for demonstration purposes. In a real-world scenario, you would train your model on actual data.

# Define a dummy ML model
model = RandomForestClassifier()
X = np.random.rand(100, 3)
y = np.random.randint(0, 2, 100)

model.fit(X, y)

Next, let’s create a route that will accept input data, make a prediction using the ML model, and return the prediction to the user:

@app.post("/predict")
def predict(data: InputData):
    features = [data.feature1, data.feature2, data.feature3]
    prediction = model.predict([features])[0]
    return {"prediction": prediction}

Finally, we need to run the FastAPI application using Uvicorn. You can do this by running the following command in your terminal:

uvicorn app:app --reload

You should see output indicating that the FastAPI application is running. You can now navigate to http://localhost:8000/docs in your browser to access the Swagger UI. Here, you can test your ML model by providing input data and seeing the prediction returned by the API.

Congratulations! You have successfully created a Machine Learning web application using FastAPI. Feel free to further customize your application by adding more routes, implementing more complex ML models, or integrating with other technologies. Happy coding!