Metadata-Version: 2.1
Name: teachable-machine-lite
Version: 0.0.1
Summary: A Python package to simplify the deployment process of exported Teachable Machine models into different embedded systems environments like Raspberry Pi and other SBCs using TensorFlowLite.
Home-page: https://github.com/MeqdadDev/teachable-machine-lite
Author: Meqdad Dev (Meqdad Darwish)
Author-email: meqdad.darweesh@gmail.com
License: UNKNOWN
Download-URL: https://github.com/MeqdadDev/teachable-machine-lite
Description: # Teachable Machine Lite
        
        A Python package to simplify the deployment process of exported [Teachable Machine](https://teachablemachine.withgoogle.com/) models into different embedded environments like Raspberry Pi and other SBCs using TensorFlowLite.
        
        Links:
        
        [PyPI](https://pypi.org/project/teachable-machine-lite/)
        
        [Source Code](https://github.com/MeqdadDev/teachable-machine-lite)
        
        ## Requirements
        
        Python >= 3.8
        
        ## How to install package
        
        ```bash
        pip install teachable-machine-lite
        ```
        
        ## Dependencies
        
        ```numpy, tflite-runtime```
        
        ## How to use teachable machine lite package
        
        ```py
        from teachable_machine_lite import TeachableMachineLite
        import cv2
        from tflite_runtime.interpreter import Interpreter
        
        model_path = 'models/model.tflite'
        interpreter = Interpreter(model_path)
        
        my_model = TeachableMachineLite(model_type='tflite', model_path=model_path)
        
        img_path = 'images/my_image.jpg'
        
        dim = my_model.get_image_dimensions(interpreter)
        height, width = dim['height'], dim['width']
        
        interpreter.allocate_tensors()
        
        img = cv2.imread(img_path)
        img = cv2.resize(img, (width, height))
        my_model.transform_image(interpreter, img)
        interpreter.invoke()
        results = my_model.classify_image(interpreter)
        
        print('highest_class_id', results['highest_class_id'])
        print('highest_class_prob', results['highest_class_prob'])
        
        ```
        
        _highest_class_id_ is selected based on labels.txt file.
        
        More features are coming soon...
        
Keywords: python,teachable machine,ai,computer vision,camera,opencv,image classification,tensorflowlite
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: License :: OSI Approved :: MIT License
Description-Content-Type: text/markdown
