README.md

metadata

title: Mold Detection API
emoji: 🦠
colorFrom: blue
colorTo: green
sdk: docker
app_file: app.py
pinned: false
license: mit

Mold Detection API

FastAPI backend for mold detection using multi-task ResNet50 deep learning model, deployed with Docker.

Mold Detection API

FastAPI backend for mold detection using multi-task ResNet50 deep learning model.

Features

• Multi-task Learning: Classifies mold types and detects biological material
• 3-Level Decision System:
  • High confidence (≥80%): "Mold"
  • Medium confidence (50-80%) + biological detection: "Possible Mold"
  • Low confidence: "Not Mold"
• RESTful API: Easy integration with any frontend

API Endpoints

GET /

Health check and API information

GET /health

Simple health check

POST /predict

Predict mold detection from an image

Request:

• Content-Type: multipart/form-data
• File: Image file (jpg, png, jpeg)

Response:

{
  "decision": "Mold" | "Possible Mold" | "Not Mold",
  "mold_probability": 0.0-1.0,
  "biological_probability": 0.0-1.0
}

Usage

Using curl:

curl -X POST "https://YOUR_USERNAME-SPACE_NAME.hf.space/predict" \
  -F "file=@/path/to/your/image.jpg"

Using Python:

import requests

url = "https://YOUR_USERNAME-SPACE_NAME.hf.space/predict"
with open("test_image.jpg", "rb") as f:
    response = requests.post(url, files={"file": f})
    print(response.json())

Documentation

Interactive API documentation available at /docs endpoint.

Model

• Architecture: ResNet50 with multi-task heads
• Input: RGB images (224x224)
• Output:
  • Classification head: 9 classes (mold class at index 4)
  • Biological detection head: 2 classes (binary)