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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:**
```json
{
"decision": "Mold" | "Possible Mold" | "Not Mold",
"mold_probability": 0.0-1.0,
"biological_probability": 0.0-1.0
}
```
## Usage
### Using curl:
```bash
curl -X POST "https://YOUR_USERNAME-SPACE_NAME.hf.space/predict" \
-F "file=@/path/to/your/image.jpg"
```
### Using Python:
```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)
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