updated docker to include sample images

README.md

@@ -32,7 +32,7 @@ cd S2FApp && streamlit run app.py
 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 `sample/`
+4. Upload an image or pick from `samples/`
 5. Click **Run prediction**
 
 Output: heatmap, cell force (sum), and basic stats.

S2FApp/Dockerfile

@@ -24,7 +24,8 @@ COPY --chown=user:user app.py predictor.py download_ckp.py ./
 COPY --chown=user:user models/ models/
 COPY --chown=user:user utils/ utils/
 COPY --chown=user:user config/ config/
-RUN mkdir -p ckp sample && chown -R user:user ckp sample
+COPY --chown=user:user samples/ samples/
+RUN mkdir -p ckp && chown user:user ckp
 
 # Download checkpoints from Hugging Face if ckp is empty (for Space deployment)
 # Requires HF_TOKEN secret in Space Settings (https://huggingface.co/settings/tokens)

S2FApp/README.md

@@ -27,7 +27,7 @@ Checkpoints are downloaded automatically from the [Shape2Force model repo](https
 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 `sample/`
+4. Upload an image or pick from `samples/`
 5. Click **Run prediction**
 
 Output: heatmap, cell force (sum), and basic stats.

S2FApp/app.py

@@ -31,7 +31,7 @@ st.caption("Predict force maps from bright field microscopy images")
 
 # Folders
 ckp_folder = os.path.join(S2F_ROOT, "ckp")
-sample_folder = os.path.join(S2F_ROOT, "sample")
+sample_folder = os.path.join(S2F_ROOT, "samples")
 ckp_files = []
 if os.path.isdir(ckp_folder):
     ckp_files = sorted([f for f in os.listdir(ckp_folder) if f.endswith(".pth")])
@@ -120,7 +120,7 @@ else:
             sample_path = os.path.join(sample_folder, selected_sample)
             img = cv2.imread(sample_path, cv2.IMREAD_GRAYSCALE)
         # Show sample thumbnails
-        st.caption("Sample images (add more to the `sample/` folder)")
+        st.caption("Sample images (add more to the `samples/` folder)")
         n_cols = min(4, len(sample_files))
         cols = st.columns(n_cols)
         for i, fname in enumerate(sample_files[:8]):  # show up to 8
@@ -130,7 +130,7 @@ else:
                 if sample_img is not None:
                     st.image(sample_img, caption=fname, width='content')
     else:
-        st.info("No sample images found. Add images to the `sample/` folder, or use Upload.")
+        st.info("No sample images found. Add images to the `samples/` folder, or use Upload.")
 
 run = st.button("Run prediction", type="primary")
 has_image = img is not None
@@ -202,4 +202,4 @@ elif run and not has_image:
 
 # Footer
 st.sidebar.divider()
-st.sidebar.caption("Place .pth checkpoints in the ckp/ folder")
+st.sidebar.caption("Place .pth checkpoints in ckp/, sample images in samples/")