Skip to content

Latest commit

 

History

History
152 lines (104 loc) · 6.58 KB

README.md

File metadata and controls

152 lines (104 loc) · 6.58 KB
Raw

Image Classification App Boilerplate

Have you been puzzled by tons of videos, blogs and other resources on the internet and don't know where and how to deploy your AI models?

Won't it be nice if you could have a template where you just insert your trained model files, edit some promo text and Voila, it's done?

Well, look no further because this repository makes it as easy for you as it sounds!

How to use this?

NOTE: For now, we are exclusively focused on image classification models built using tensorflow/pytorch. Later we would expand to models dealing with text and speech data as well as training using MXNet or a julia environment

  • I assume you have Python(with Anaconda) installed in your operating system and set to path. If not, please visit this.

  • Now, once that is done, please clone this repository for your local system and use this as the template repo on your GitHub.

  • After cloning the repo, move inside the repo, using the command:

    cd img_ai_app_boilerplate

  • Now, let's fetch our dependencies to run our app. [A python package called StreamLit is at the heart of this app]

    pip install -r requirements.txt

  • Now, let's put our model files in the app. Open the model sub-directory and paste your Keras h5 model files there.

Integrating your Model

  1. Open VSCode or your favorite code editor/IDE.

    code . [Type this in the terminal to open VSCode if you already have it installed]

  2. Now open the file img_classifier.py.

  3. Search for the variable labels in the code and set them as per your training model. [say if you are doing Cats Vs Dogs classification, then = {0: "Cats", 1: "Dogs"]

    NOTE: This is totally dependent on your model training

  4. Update model with the name of your model file.

    [say model = tensorflow.keras.models.load_model('model/catsvsdogs.h5')]

  5. Save the changes.

Making changes in frontend

Continuing with changes to the User Interface or the frontend of our app. Follow the steps mentioned below:

  1. Open app.py.

  2. Search for st.title and update the Title of the app as per your application's needs.

    [say st.title('Our Cats vs Dogs Classifier')]

  3. Let's do the same for our Page Title and tweak our SEO. Search for page_title and update it.

    [say page_title="Cats Vs Dogs",]

  4. If you have some affiliation or maybe the app is made completely by you (perhaps with a group of your friends/colleagues) as a pet project, you can reflect that in the app by searching for st.subheader and updating it

    [say st.subheader("By John Doe and Jane Doe")]

Updating our CONTACT PAGE

You can add your and/or your teammates' names, profile pictures, email and affiliation. You can do so by following the aforementioned steps.

  1. Search for display_team and pass/update the following parameters:

    • Name
    • path_to_image
    • Affiliation
    • email

  2. For adding multiple contributors, you can call the same function multiple times. For eg.:

    display_team("John Doe","./assets/john_doe.png","Stanford University","contact@johndoe.com")
display_team("Jane Doe","./assets/jane_doe.png","Harvard University","contact@janedoe.com")

Storing the Images and User Feedback Online (OPTIONAL)

If you want to store your images and user feedbacks in a cloud database like Firebase, we have some arrangements for you! Follow the steps in Firebase_Setup.MD to set it up.

[NOTE:] If you are not using firebase please feel free to:

  • Remove the firebase_bro.py file,

  • Delete the following lines from the app.py file:

    • import firebase_bro.

    • firebase_bro.send_img(image) line inside the Home block of the if else condition.

    • firebase_bro.send_feedback(first_name, last_name, user_email, feedback) line inside the Feedback block of the if else condition.

  • Also remove firebase and all the lines below the comment For Handling firebase and pyrebase dependency issues in the requirements.txt file.

A FINAL NOTE: The current version of the app only supports jpg, png and jpeg images as input

Testing the app Locally

Now, we are all set to test it!

Open the terminal/command prompt and type

$ streamlit run app.py

Give it a few seconds to start our local server, load Tensorflow and other cool stuff our app requires in order to function properly..

Upload Your Image, Click on Predict, Verify the working

NOTE: If you face any difficulties please raise an issue and let me know

Congrats! You now have your models deployed!

Deploying the App (OPTIONAL)

If you wish to share this as a prototype for others to try, please follow these steps:

  1. Track, Commit and Push the changes to your GitHub repository that you initially made using this template repository 
    $ git add .
$ git commit -m "App v.0.0.1"
$ git push origin master

NOTE: You must be inside the folder containing the app to send the update your copy on GitHub

Now as per your choice of hosting, please refer the following guides:

Further Work 🏗

  • Improve the UI of the app using custom HTML,CSS or REACT
  • Make the App more descriptive
  • Guide for Deployment to Digital Ocean
  • Guide for Deployment to GCP
  • Guide for Deployment to AWS
  • Guide for Deployment to Azure
  • Add Favicon option, Improve SEO
  • Add Multiple Pages
    • Contact Page
    • About Page
    • Feedback Page
  • Support for PyTorch models
  • Support for MXNET models
  • Support for saved_model TF format
  • Add Animations
  • Dark Mode
  • Similar Efforts for a mobile app using TFLite + Flutter ( From building to serving)
  • Experiment with TFJS