| | --- |
| | title: Shape2Force |
| | short_description: Force map prediction from bright-field cell images |
| | emoji: 🦠 |
| | colorFrom: indigo |
| | colorTo: blue |
| | tags: |
| | - cell-mechanobiology |
| | - microscopy |
| | - image-to-image |
| | - pytorch |
| | license: cc-by-4.0 |
| | sdk: docker |
| | app_port: 8501 |
| | --- |
| | |
| | # Shape2Force (S2F) App |
| |
|
| | Predict force maps from bright-field microscopy images using deep learning. |
| |
|
| | ## Quick Start |
| |
|
| | ```bash |
| | pip install -r requirements.txt |
| | streamlit run app.py |
| | ``` |
| |
|
| | Checkpoints are downloaded automatically from the [Shape2Force model repo](https://huggingface.co/Angione-Lab/Shape2Force) when running in Docker. For local use, place `.pth` files in `ckp/`. |
| |
|
| | ## Usage |
| |
|
| | 1. Choose **Model type**: Single cell or Spheroid |
| | 2. Select a **Checkpoint** from `ckp/` |
| | 3. For single-cell: pick **Substrate** (e.g. fibroblasts_PDMS) |
| | 4. Upload an image or pick from `samples/` |
| | 5. Click **Run prediction** |
| | |
| | Output: heatmap, cell force (sum), and basic stats. |
| | |
| | ## Full Project |
| | |
| | For training, evaluation, and notebooks, see the main [Shape2Force repository](https://github.com/Angione-Lab/Shape2Force). |
| | |
