| --- |
| 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 **Conditions** (e.g. Fibroblasts_Fibronectin_6KPa) |
| 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). |
|
|
