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| title: Food Spoilage Detection | |
| emoji: 🍎 | |
| colorFrom: green | |
| colorTo: red | |
| sdk: docker | |
| app_file: app.py | |
| pinned: false | |
| # siPeka — Food Spoilage Detection API | |
| A REST API that classifies fruits and vegetables as **fresh** or **spoiled** using a fine-tuned Swin Transformer Tiny. | |
| **Live demo:** [huggingface.co/spaces/crtal/siPeka-model-api](https://huggingface.co/spaces/crtal/siPeka-model-api) | |
| --- | |
| ## Tech Stack | |
| | Layer | Technology | | |
| |---|---| | |
| | API Framework | FastAPI | | |
| | Model Architecture | Swin Transformer Tiny (torchvision) | | |
| | Training | PyTorch 2.10 · AdamW · Cosine Annealing LR | | |
| | Inference | CUDA / CPU (auto-detected) | | |
| | Containerization | Docker | | |
| | Deployment | Hugging Face Spaces | | |
| | CI/CD | GitHub Actions | | |
| --- | |
| ## API Reference | |
| ### `GET /` | |
| Returns basic API info. | |
| **Response** | |
| ```json | |
| { | |
| "message": "Food Spoilage Detection API is running.", | |
| "usage": "POST an image to /predict", | |
| "supported_classes": ["fresh", "spoiled"] | |
| } | |
| ``` | |
| --- | |
| ### `GET /health` | |
| Health check endpoint. | |
| **Response** | |
| ```json | |
| { "status": "ok" } | |
| ``` | |
| --- | |
| ### `POST /predict` | |
| Classifies an image as fresh or spoiled. | |
| **Request** | |
| | Field | Type | Description | | |
| |---|---|---| | |
| | `file` | `multipart/form-data` | Image file (`.jpg`, `.jpeg`, `.png`) | | |
| **Constraints** | |
| - Max file size: 20 MB | |
| - File must be a valid image readable by PIL | |
| **Responses** | |
| `200 OK` — Successful prediction | |
| ```json | |
| { | |
| "status": "success", | |
| "prediction": "fresh", | |
| "confidence": 0.95, | |
| "model": "swin_transformer_tiny_finetuned" | |
| } | |
| ``` | |
| `200 OK` — Low confidence (image may not be a food item) | |
| ```json | |
| { | |
| "status": "ambiguous", | |
| "confidence": 0.61, | |
| "message": "Model confidence too low. The image might not be a recognized food item or is too low quality.", | |
| "suggestion": "Please provide a clearer image of a fruit or vegetable." | |
| } | |
| ``` | |
| `400 Bad Request` — Invalid input | |
| ```json | |
| { "detail": "File too large. Maximum size is 20 MB." } | |
| { "detail": "Could not read image. Please upload a valid image file." } | |
| ``` | |
| `500 Internal Server Error` — Inference failure | |
| ```json | |
| { "detail": "Error processing image: <reason>" } | |
| ``` | |
| **Example** | |
| ```bash | |
| curl -X POST "https://crtal-sipeka-model-api.hf.space/predict" \ | |
| -F "file=@apple.jpg" | |
| ``` | |
| --- | |
| ## Run Locally | |
| ```bash | |
| git clone https://github.com/yukienjoyer7/sipeka-model-api.git | |
| cd sipeka-model-api | |
| pip install -r requirements.txt | |
| uvicorn app:app --host 0.0.0.0 --port 7860 | |
| ``` | |
| --- | |
| ## Training | |
| ### 1. Prepare data | |
| ```bash | |
| python scripts/prepare_data.py | |
| ``` | |
| Restructures the raw dataset into binary `fresh` / `spoiled` classes and splits into `train` / `val` / `test` (70 / 15 / 15). | |
| ### 2. Train | |
| ```bash | |
| python scripts/train.py \ | |
| --arch swin_t_finetune \ | |
| --output models/spoilage_model_swin_finetuned.pth \ | |
| --epochs 20 \ | |
| --lr 0.0001 \ | |
| --scheduler | |
| ``` | |
| | `--arch` | Description | | |
| |---|---| | |
| | `mobilenet` | MobileNetV2, head only | | |
| | `swin_t` | Swin Transformer Tiny, head only | | |
| | `swin_t_finetune` | Swin Transformer Tiny, last block + head unfrozen | | |
| | `swin_s` | Swin Transformer Small, last block + head unfrozen | | |
| ### 3. Evaluate | |
| ```bash | |
| python scripts/evaluate.py \ | |
| --arch swin_t_finetune \ | |
| --weights models/spoilage_model_swin_finetuned.pth | |
| ``` | |
| Prints a full classification report and confusion matrix on the test set. | |