👩💻Streamlit makes it easy to build full stack machine learning applications.
🚀TensorFlow provides a powerful framework for training and deploying machine learning models.
📊Visualization with Streamlit helps in interpreting and understanding the model's predictions.
📱The app can be deployed on edge devices or web servers for wider accessibility.
👨🔬The application showcases the potential of machine learning in a practical use case.
Do I need a GPU to run the app?
—While a GPU can greatly accelerate training and inference, it is not necessary to run the app.
Can I deploy the app on a web server?
—Yes, the app can be deployed on a web server for wider accessibility.
What other libraries are used in the app?
—The app also uses OS, imageio, and other utility libraries for data processing and visualization.
Is the app compatible with PyTorch?
—The app focuses on TensorFlow, but the concepts can be applied to PyTorch as well.
Can the app handle large video files?
—The app can handle large video files by processing them in smaller segments.
00:00Introducing a full stack machine learning application built with TensorFlow and Streamlit.
03:00An overview of the key insights and benefits of using TensorFlow and Streamlit in the app.
07:30Importing the necessary libraries and initializing the app with a sidebar and title.
09:30Adding information about the app and its capabilities using the St.info function.
12:00Loading the pre-trained machine learning model using the load_model function.
15:30Creating a user interface for uploading and processing video files.
20:00Implementing the model inference and displaying the predictions on the user interface.
25:00Using Streamlit's caching feature to improve the app's performance.
