Step 6: Add success/failure trajectory filtering from metadata
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Step 5: Add pattern analysis with full metrics (mean, median, std, min, max, 90th percentile)
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Step 4: Add labeling interface with CSV export and navigation
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Step 3: Add dataset loading from HuggingFace
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Step 2: Add video player and frame navigation
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Try with launch() call
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Fresh start: Absolute minimal Gradio app
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Pin huggingface-hub==0.19.4 to fix HfFolder import
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Reset to minimal app - build from scratch
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Add demo.queue() for HuggingFace Spaces async processing
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Refactor: Consolidate everything into single app.py file
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Use Gradio 4.44.1 with huggingface-hub==0.19.4 (has HfFolder)
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Reorder requirements - install huggingface-hub first to pin version
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Pin huggingface-hub<0.20.0 to fix HfFolder import issue with Gradio 5.0
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Upgrade to Gradio 5.0.0 for compatibility with newer huggingface_hub
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Upgrade Gradio to 4.44.1 in both README and requirements
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Add error handling with print statements to debug initialization issue
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Minimal app.py - import and create demo at module level
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Simplify app.py - ensure demo is always a Blocks object
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Add extensive debugging to app.py
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Add error handling to app.py for better debugging
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Fix numpy/opencv conflict: use opencv-python-headless<4.12.0 compatible with numpy 1.x (Gradio 4.0.0 requirement)
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Upgrade Gradio to 4.44.1 to fix security vulnerabilities and numpy compatibility
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Fix numpy version conflict with opencv-python
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Fix app.py for HuggingFace Spaces - module level demo
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initial push
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Initial commit: Trajectory End Point Labeler
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