In the final leg of this Tutorial, we start to build a webpage using Streamlit that can perform three things:

1. Give some description of the Application
2. Take an image input
3. Provide predictions/output on #2

Streamlit is an open-source app framework for Machine Learning and Data Science teams. It is a Python-based library specifically designed for machine learning engineers that does this well and given all the resources from our previous work, we can set to build this service – all in python.

Create a Simple Description of our service

Here we will interact with the markdown functions of Streamlit library, first, create an app.py script in your local directory and paste the following.

## Import the Neccessary Libraries ### Don't forget to update your --requirements.txt file import streamlit as st from PIL import Image import numpy as np import torch import io import os  ## Put some description of the service st.title("DROWSINESS DETECTION") st.subheader("An implementation of Machine Learning to determine when user might be feeling a little drowsy.") st.markdown("---") 

Next, in the same script we load our saved model – and yes! it has to be your folder directory. The following loads the trainned model as well as the torch hub that holds the YOLOv5 pre-trained model, then we evaluate with model.eval().

## Load Model #e.g run_model_path = './last_drowsy_v4.pt' run_model_path = './model_name.pt' model = torch.hub.load('ultralytics/yolov5', 'custom', path=run_model_path) model.eval() 

Great!, next, we create a function that takes our model and the input image to output the prediction as a bounding box, below is how the predict_img

def predict_img(img):     ## Flatten out the image     image = np.array(Image.open(img))      ## predict     result = model(image)      ## Output     output = io.BytesIO()     out_image = np.squeeze(result.render())     output_img = Image.fromarray(out_image)     output_img.save(output, format='JPEG')     result_img = output.getvalue()      return st.image(result_img) 

Take an image input & Provide predictions/output

Phew, now, let's handle the dial for handling image uploads using the file_uploader on the sidebar of our app:

img_file_buffer = st.sidebar.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"])     if img_file_buffer is not None:         UPLOADED_IMAGE = img_file_buffer         predict_img(UPLOADED_IMAGE) 

Notice how we declared the various image formats our app can accept as well as how we handled for if there are no Uploaded files.

That-is-it! Easy right?

Navigate to your terminal and run:

streamlit run app.py

On the Top-Right corner, you should see an option to “Deploy App”, please fire away and share how it turns out. The Author did and you can find the repository and deployed application.

Yay! We made it to the finish line. Now take this approach and create your own web-services powered by ML.