---
title: Anomaly Detection Demo
emoji: 🧠
colorFrom: indigo
colorTo: pink
sdk: gradio
sdk_version: "4.27.0"
app_file: app.py
pinned: false
---

# 🧠 Anomaly Detection as a Service

A minimalistic anomaly detection microservice powered by an autoencoder trained on MNIST-style grayscale images.  
This project provides both a backend API and a Gradio web interface to analyze images and identify abnormal patterns.

## 🚀 Features

- 🧪 **FastAPI** backend for anomaly score prediction
- 🎛️ **Gradio** frontend with drag-and-drop image testing
- 🔥 Visual output includes anomaly score, reconstruction, and heatmap
- ⚙️ Pretrained autoencoder (trained on MNIST digit-style grayscale images)
- 🧰 Easily extendable to new datasets and model architectures


## ⚙️ Setup

1. Install dependencies:
```
pip install -r requirements.txt
```
2. (Optional) Train the model from scratch:
```
python src/train.py
```
By default, the model will be saved to `models/autoencoder_mnist.pth`.

Then in `model_config.py` set
`model_path`.

## 🎛️ Run the Gradio Frontend
```
python app.py
```

This starts the API at http://0.0.0.0:7860

This launches an interactive browser interface for:

- Uploading images
- Viewing anomaly scores
- Seeing reconstructions and heatmaps side-by-side

## 📝 Notes
- Input images must be grayscale, 28×28 pixels.
- To adapt to a custom dataset, modify src/train.py accordingly and retrain.
- The project is meant for educational/demo purposes and not intended for production deployment.