metadata
license: mit
library_name: sklearn
tags:
- sklearn
- classification
- random-forest
- food-science
- milk-quality
pipeline_tag: tabular-classification
Milk Spoilage Classification Model
A Random Forest classifier for predicting milk spoilage type based on microbial count data.
Model Description
This model classifies milk samples into three spoilage categories based on Standard Plate Count (SPC) and Total Gram-Negative (TGN) bacterial counts measured at days 7, 14, and 21 of shelf life.
Classes
- PPC: Post-Pasteurization Contamination
- no spoilage: No spoilage detected
- spore spoilage: Spore-forming bacteria spoilage
Input Features
| Feature | Description |
|---|---|
| SPC_D7 | Standard Plate Count at Day 7 (log CFU/mL) |
| SPC_D14 | Standard Plate Count at Day 14 (log CFU/mL) |
| SPC_D21 | Standard Plate Count at Day 21 (log CFU/mL) |
| TGN_D7 | Total Gram-Negative count at Day 7 (log CFU/mL) |
| TGN_D14 | Total Gram-Negative count at Day 14 (log CFU/mL) |
| TGN_D21 | Total Gram-Negative count at Day 21 (log CFU/mL) |
Performance
- Test Accuracy: 95.76%
Usage
Using the Inference API
import requests
API_URL = "https://api-inference.huggingface.co/models/chenhaoq87/MilkSpoilageClassifier"
headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}
# Input: [SPC_D7, SPC_D14, SPC_D21, TGN_D7, TGN_D14, TGN_D21]
payload = {"inputs": [[4.5, 5.2, 6.1, 3.2, 4.0, 4.8]]}
response = requests.post(API_URL, headers=headers, json=payload)
print(response.json())
Local Usage
import joblib
import numpy as np
# Load the model
model = joblib.load("model/model.joblib")
# Prepare input features
# [SPC_D7, SPC_D14, SPC_D21, TGN_D7, TGN_D14, TGN_D21]
features = np.array([[4.5, 5.2, 6.1, 3.2, 4.0, 4.8]])
# Make prediction
prediction = model.predict(features)
probabilities = model.predict_proba(features)
print(f"Predicted class: {prediction[0]}")
print(f"Class probabilities: {dict(zip(model.classes_, probabilities[0]))}")
Model Details
- Model Type: Random Forest Classifier
- Framework: scikit-learn
- Number of Estimators: 100
- Max Depth: None (unlimited)
- Min Samples Split: 5
- Min Samples Leaf: 1
Citation
If you use this model, please cite the original research on milk spoilage classification.
Repository Structure
MilkSpoilageClassifier/
├── apps/
│ ├── fastapi/ # REST API application
│ ├── gradio/ # Interactive web interface
│ └── huggingface/ # HF Inference handler
├── data/ # Training and test datasets
├── docs/ # Documentation files
├── model/ # Trained model artifacts
├── notebooks/ # Jupyter notebooks for analysis
├── scripts/ # Utility scripts
└── README.md # This file
See individual README files in each directory for more details.
Quick Start
Train the Model
python scripts/prepare_model.py
Run Gradio Interface
cd apps/gradio
pip install -r requirements.txt
python app.py
Run FastAPI Server
cd apps/fastapi
pip install -r requirements.txt
python app.py
Deploy to Hugging Face
python scripts/upload_to_hf.py
License
MIT License