This is an image classification web application deployed using Streamlit

!pip install streamlit opencv-python tensorflow IDE of your choice: VS Code, Google Colab, Kaggle notebook

The "train_data" folder contains the images train set
The "test_data" folder contains the images test set

1. Create the model from Google Teachable Machine by uploading the images and train the model. You can find it through this link: https://teachablemachine.withgoogle.com/

2. Export and Download the model as Tensorflow NOT Tensorflow.lite, Tensorflow.js. Extract the contents from the zip folder.

3. Create a python script "ïmage.py" and put the Streamlit code. Ensure the keras_model.h5, labels.txt and the image.py are in the same folder

4. Run the code: streamlit run image.py

Here's a look: Home Page

Uploaded Image

Camera-captured Image