|
|
---
|
|
|
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
|
|
|
|
|
|
```python
|
|
|
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
|
|
|
|
|
|
```python
|
|
|
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
|
|
|
```bash
|
|
|
python scripts/prepare_model.py
|
|
|
```
|
|
|
|
|
|
### Run Gradio Interface
|
|
|
```bash
|
|
|
cd apps/gradio
|
|
|
pip install -r requirements.txt
|
|
|
python app.py
|
|
|
```
|
|
|
|
|
|
### Run FastAPI Server
|
|
|
```bash
|
|
|
cd apps/fastapi
|
|
|
pip install -r requirements.txt
|
|
|
python app.py
|
|
|
```
|
|
|
|
|
|
### Deploy to Hugging Face
|
|
|
```bash
|
|
|
python scripts/upload_to_hf.py
|
|
|
```
|
|
|
|
|
|
## License
|
|
|
|
|
|
MIT License
|
|
|
|
