MediaMend

A developer built MediaMend, an open-source local AI assistant that scans folders for blurry photos, blank video frames, silent recordings, empty files and duplicates, then routes flagged items through a review queue and a reversible Bin rather than deleting them outright. The tool combines deterministic checks — OpenCV Laplacian variance for blur, FFmpeg-based silence detection, SHA-256 duplicate matching — with a local open-weight vision-language model that judges whether an imperfect image is still worth keeping, returning validated structured JSON. Every move is recorded in a manifest with original path, size and checksum, and the code, prompts and thresholds are public so the benchmark can be rerun.

T This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady What I Built MediaMend is a local AI assistant for people who record far more media than they will ever review: blurry photos, black video frames, silent recordings, empty files, duplicates. The lazy way to clean that up is "delete everything below a threshold", and that is also the way to lose the one blurry, unrepeatable shot that mattered. MediaMend takes a safer lazy path: 1. Scan a folder. Everything runs on your computer. Nothing is uploaded. 2. Review a short queue of flagged files, each with a reason and a confidence level. 3. Move to Bin. Approved files leave your folders but are not deleted. 4. Restore, Archive or Delete permanently from the Bin. Permanent deletion needs a separate confirmation. You never inspect the whole folder. You only judge the cases the tool is unsure about. Code https://github.com/noorulhuda07/mediamend https://github.com/noorulhuda07/mediamend How I Built It - Deterministic checks for measurable problems: OpenCV Laplacian variance for blur, resolution checks, sampled video frames for blank-frame share, FFmpeg decoding for silence measured relative to each recording's own level, SHA-256 for exact duplicates. - A local open-weight vision-language model for judgment: model name and tag, e.g. via Ollama looks at flagged photos and answers one narrow question: could this imperfect image still be worth keeping? It returns structured JSON that I validate. If the model is unavailable or answers badly, the app says so instead of faking a result. - A safety layer: every move records the original path, size and SHA-256 in a manifest and verifies the checksum afterwards. Restore never overwrites an existing file, and nothing outside the scanned folder can be touched. - Stack: Python, Streamlit, OpenCV, FFmpeg, Ollama + model . Technical measurements and AI opinions are stored separately, so a model's guess never overwrites measured evidence. Results I tested on N labelled files N photos, N videos, N audio, N other . Ground truth was labelled before scanning. | Metric | Result | | Blur precision / recall | x/y , x/y | | False positives on must-keep files rules only | n | | False positives on must-keep files rules + AI | n | | Bin/restore checksum success | x/y | | Scan time N files | seconds | | AI time per photo | seconds on hardware | Where it failed: real examples, e.g. a dark but valid photo flagged as blurry because low contrast lowers the sharpness score; the model's wrong call on ... . The thresholds are starting points I tuned on this set, not universal values. Why Open Innovation Matters - Privacy: personal photos and voice recordings never leave the machine. - Control: I can swap or compare models model A vs model B and inspect the exact prompt and outputs. - Cost: no per-image API fees for scanning thousands of files. - Transparency and reproducibility: the code, prompt, thresholds and test set are public, so anyone can rerun the benchmark and see the failures. The honest trade-off: hardware needs, model speed on your laptop, accuracy limits . Limitations AI cannot judge creative value perfectly. Deleting from the Bin may not securely erase every copy on SSDs, backups or synced folders. Anything else you observed. What I Built Demo Code How I Built It Why Does Open Innovation Matter? My Agent Session The Lazy Payoff Prize Categories