apps/fastapi_variants/README.md

FastAPI Multi-Variant API for Milk Spoilage Classification

This API supports 10 model variants optimized for different data availability scenarios.

Features

• 10 Model Variants with test accuracies ranging from 62.8% to 95.8%
• Automatic Feature Validation - API validates you provide required features for selected variant
• RESTful API with comprehensive OpenAPI documentation
• Custom GPT Ready - Designed for seamless ChatGPT integration

Quick Start

Local Development

# Install dependencies
pip install -r requirements.txt

# Run the server
python app.py

The API will be available at http://localhost:7860

Docker Deployment

docker build -t milk-spoilage-api .
docker run -p 7860:7860 milk-spoilage-api

API Endpoints

GET /variants

List all available model variants with metadata

POST /predict

Make a prediction using the specified model variant

Request Body:

{
  "spc_d7": 2.1,
  "spc_d14": 4.7,
  "spc_d21": 6.4,
  "tgn_d7": 1.0,
  "tgn_d14": 3.7,
  "tgn_d21": 5.3,
  "model_variant": "baseline"
}

Response:

{
  "prediction": "PPC",
  "probabilities": {
    "PPC": 0.97,
    "no spoilage": 0.02,
    "spore spoilage": 0.01
  },
  "confidence": 0.97,
  "variant_used": {
    "variant_id": "baseline",
    "name": "Baseline (All Features)",
    "description": "Uses all 6 microbiological measurements",
    "features": ["SPC_D7", "SPC_D14", "SPC_D21", "TGN_D7", "TGN_D14", "TGN_D21"],
    "test_accuracy": 0.9576
  }
}

Model Variants

Variant	Test Acc	Features Required
baseline	95.8%	All 6 features
scenario_1_days14_21	94.2%	SPC_D14, SPC_D21, TGN_D14, TGN_D21
scenario_3_day21	93.7%	SPC_D21, TGN_D21
scenario_4_day14	87.4%	SPC_D14, TGN_D14
scenario_2_days7_14	87.3%	SPC_D7, SPC_D14, TGN_D7, TGN_D14
scenario_6_spc_all	78.3%	SPC_D7, SPC_D14, SPC_D21
scenario_8_spc_7_14	73.3%	SPC_D7, SPC_D14
scenario_9_tgn_7_14	73.1%	TGN_D7, TGN_D14
scenario_7_tgn_all	69.9%	TGN_D7, TGN_D14, TGN_D21
scenario_5_day7	62.8%	SPC_D7, TGN_D7

Custom GPT Integration

See ../../docs/CUSTOM_GPT_SETUP_MULTIVARIANT.md for complete setup instructions.

Example Usage

Python

import requests

url = "https://chenhaoq87-milkspoilageclassifier-api-variants.hf.space/predict"

# Using baseline model with all features
response = requests.post(url, json={
    "spc_d7": 2.1,
    "spc_d14": 4.7,
    "spc_d21": 6.4,
    "tgn_d7": 1.0,
    "tgn_d14": 3.7,
    "tgn_d21": 5.3,
    "model_variant": "baseline"
})

print(response.json())

# Using Day 21 only model (when you don't have earlier measurements)
response = requests.post(url, json={
    "spc_d21": 6.4,
    "tgn_d21": 5.3,
    "model_variant": "scenario_3_day21"
})

print(response.json())

cURL

# List available variants
curl https://chenhaoq87-milkspoilageclassifier-api-variants.hf.space/variants

# Make prediction with baseline model
curl -X POST https://chenhaoq87-milkspoilageclassifier-api-variants.hf.space/predict \
  -H "Content-Type: application/json" \
  -d '{
    "spc_d7": 2.1,
    "spc_d14": 4.7,
    "spc_d21": 6.4,
    "tgn_d7": 1.0,
    "tgn_d14": 3.7,
    "tgn_d21": 5.3,
    "model_variant": "baseline"
  }'

License

MIT