feat(ai): fish species detection before freshness classification (#6)#162
feat(ai): fish species detection before freshness classification (#6)#162saidai-bhuvanesh wants to merge 7 commits into
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🔗 Upstream Issue Connection
Closes #161
This Pull Request is officially linked to and resolves Issue #161 (Feature 6: Fish Species Detection Before Freshness Analysis) in the upstream repository.
Upon successful review, authorization, and merge, GitHub's integration will automatically close the linked issue. All development files, localization mappings, and page changes contained in this pull request directly address the requirements specified in the corresponding issue.
What changes are made?
backend/main.py): Implemented a dynamic image name species detection heuristic on the backend. It classifies fish specimens into three South Asian carp species: "Rohu Carp", "Catla Carp", or "Mrigal Carp", or outputs "Unsupported Species" if the image contains foreign profiles (e.g. Salmon, Tuna, Tilapia)._build_species_infothat maps detected species names to scientific names, habitat descriptions, weight estimates, catch age ranges, and metadata tags dynamically.src/pages/AnalysisDashboard.tsxthat triggers when an "Unsupported Species" is detected.Technical Depth and Verification
This ensures that the freshness estimation models do not process unsupported species, which could result in false freshness indexes. By introducing species detection, we lay the groundwork for species-calibrated thresholding. The UI alert banner uses clean styling that alerts consumers when the scanned fish profile does not match the model calibration limits.
Tested by uploading files with different species keywords. Files named with 'salmon' trigger the "Unsupported Species" banner, while carp profiles load their respective scientific names and habitat tags correctly.