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View All on GitHubThis Python-based AI project utilizes OpenCV for facial recognition and a pre-trained deep learning model to analyze facial expressions. By identifying your current mood, the system leverages YouTube's search capabilities to recommend music that aligns with your emotions.
AI Summary: Debug and optimize the PySimpleGUI application (app_PySimpleGUI.py) to resolve video stream lagging issues. This involves identifying performance bottlenecks in the code responsible for webcam feed processing and UI updates, and implementing solutions to improve the responsiveness of the application.
This Python-based AI project utilizes OpenCV for facial recognition and a pre-trained deep learning model to analyze facial expressions. By identifying your current mood, the system leverages YouTube's search capabilities to recommend music that aligns with your emotions.
AI Summary: Debug and fix a Streamlit web application deployed to the cloud. The application uses WebRTC for webcam video capture, but the deployed version fails to connect to the webcam, displaying a 'Connection is taking longer than expected' error. The task involves investigating the cause of the failure (likely related to network configuration and NAT traversal), implementing a solution (e.g., using a TURN server or adding error handling), and testing the fix on both Streamlit Cloud and Render.com.
This Python-based AI project utilizes OpenCV for facial recognition and a pre-trained deep learning model to analyze facial expressions. By identifying your current mood, the system leverages YouTube's search capabilities to recommend music that aligns with your emotions.
AI Summary: Improve the user interface (UI) of a Streamlit-based music recommendation application. This involves enhancing the visual appeal and user experience by modifying the theme, layout, fonts, and colors. Several approaches are possible, including using Streamlit's built-in theming, custom CSS with st.markdown(), or other layout adjustments.
This Python-based AI project utilizes OpenCV for facial recognition and a pre-trained deep learning model to analyze facial expressions. By identifying your current mood, the system leverages YouTube's search capabilities to recommend music that aligns with your emotions.
AI Summary: Enhance the Streamlit application to display the top 3-5 YouTube video results for a detected emotion, allowing the user to select and play a video instead of automatically playing the first result. This involves modifying the YouTube scraping logic, implementing a selection widget (e.g., selectbox or radio buttons), and updating the video playback functionality.
This Python-based AI project utilizes OpenCV for facial recognition and a pre-trained deep learning model to analyze facial expressions. By identifying your current mood, the system leverages YouTube's search capabilities to recommend music that aligns with your emotions.