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Add interactive model submission with online ZeroGPU evaluation
Replaces the static hardcoded results table with a two-tab app: a Leaderboard tab (maintainer reference table + community submissions read from REAL-Lab-Imperial/eval-unlearn-results) and a Submit tab where anyone can point at a public HF diffusers repo and get CLIP Score, TIFA, FID, ERR, UA/IRA, and ASR-I2P computed live via eval-unlearn's free_run technique. Each metric runs in its own short ZeroGPU call and generated images are discarded after scoring, never stored or published. Heavy adversarial ASR (Ring-A-Bell/MMA-Diffusion/P4D) stays an offline, self-reported workflow via uploaded eval-unlearn CLI report files. Removes src/about.py, src/envs.py, src/display/, src/leaderboard/read_evals.py, src/populate.py, src/submission/ — unused leftover scaffolding from HF's generic LLM-leaderboard template, never imported by app.py and shaped for LLM evals (precision/weight_type/architecture columns) rather than diffusion unlearning. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>4690b6b
