README.md

metadata

title: Bee Colony Survival Multimodal
emoji: 🐝
colorFrom: gray
colorTo: gray
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: false
license: mit

Bee Colony Survival — multi-modal (audio + virus) batch predictor

Upload one or more hive audio recordings (batch supported). The model predicts Survivor (S) vs Terminal / died (T) for the next 0–3 months and 4–6 months, and shows the prediction next to the ground truth.

The model (audio + virus)

A class-balanced HistGradientBoosting classifier on a 775-dimensional multi-modal feature vector, one model per horizon:

audio .wav ─► AST (Audio Spectrogram Transformer, pretrained)
              MIT/ast-finetuned-audioset-10-10-0.4593 ─► 768-d embedding ─┐
                                                                          ├─► gradient boosting
viral loads (CBPV, DWV, KBV) + country + month ──────► 7-d tabular ───────┘   → P(Terminal)

• The audio is embedded by AST at inference time from the uploaded file.
• The viral loads + metadata are read from the bundled AI_Data_Training.xlsx by matching the file name.
• A strict, high-precision threshold is used so a "Terminal" call is trustworthy.
• Domain constraint enforced: a colony predicted Terminal in 0–3 months is Terminal in 4–6 months.

Ground truth from the Excel workbook

AI_Data_Training.xlsx is bundled in the Space. On upload, each file name is matched to a row to read its viral loads and true labels. If a file is not in the workbook, the app still predicts (from audio + parsed metadata, viral loads default to 0) and reports the ground truth as not available.

Bundled examples

examples/ contains 20 recordings of Terminal colonies that the model correctly detects (the 9 highest-confidence cases plus 11 more). Drag them into the upload box to run a batch and see prediction vs ground truth.

Honest performance note

The 20 bundled files are reproduced correctly because the deployed models are trained on the full reference set. Generalisation to unseen colonies (leakage-free colony-grouped cross-validation) is more modest — a strict "Terminal" call is high-precision but catches only the clearest cases, because most colony deaths in this data occur with zero measured viral load and no strong acoustic signature. See the project notes for the full colony-grouped metrics.

Files

app.py            Gradio batch app (white background, black text/buttons)
ast_encoder.py    pretrained-AST audio encoder (identical at train and inference)
bee_features.py   filename parser + tabular feature builder
xls_lookup.py     ground-truth / viral-load lookup from the Excel workbook
model/            model_03.joblib, model_46.joblib, config.json
AI_Data_Training.xlsx   reference workbook (ground truth + viral loads)
examples/         20 example recordings
requirements.txt

Run locally

pip install -r requirements.txt
python app.py            # http://127.0.0.1:7860  (first run downloads AST weights)

Deploy to Hugging Face Spaces

git clone https://huggingface.co/spaces/<user>/<space>
cp -r hf_space_final/* <space>/
cd <space>
git lfs install && git lfs track "*.wav" "*.joblib" "*.xlsx"
git add . && git commit -m "Bee colony survival multimodal batch app" && git push